xclim.indicators.atmos package

Atmospheric indicators

While the indices module stores the computing functions, this module defines Indicator classes and instances that include a number of functionalities, such as input validation, unit conversion, output meta-data handling, and missing value masking.

The concept followed here is to define Indicator subclasses for each input variable, then create instances for each indicator.

Submodules

xclim.indicators.atmos._conversion module

Atmospheric conversion definitions.

xclim.indicators.atmos._conversion.corn_heat_units(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '4.44 degC', thresh_tasmax: str = '10 degC', ds: Dataset = None) → DataArray

Corn heat units. (realm: atmos)

Temperature-based index used to estimate the development of corn crops. Formula adapted from [BootsmaTremblay&Filion1999]_.

This indicator will check for missing values according to the method “skip”. Based on indice corn_heat_units().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The minimum temperature threshold needed for corn growth. Default : 4.44 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The maximum temperature threshold needed for corn growth. Default : 10 degC. [Required units : [temperature]]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

chu (DataArray) – Corn heat units (Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax}). description: Temperature-based index used to estimate the development of corn crops. Corn growth occurs when the minimum and maximum daily temperature both exceeds specific thresholds : Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax}.

Notes

Formula used in calculating the Corn Heat Units for the Agroclimatic Atlas of Quebec [Audet&al2012]_.

The thresholds of 4.44°C for minimum temperatures and 10°C for maximum temperatures were selected following the assumption that no growth occurs below these values.

Let $TX_{i}$ and $TN_{i}$ be the daily maximum and minimum temperature at day $i$. Then the daily corn heat unit is:

\[CHU_i = \frac{YX_{i} + YN_{i}}{2}\]

with

\[ \begin{align}\begin{aligned}YX_i & = 3.33(TX_i -10) - 0.084(TX_i -10)^2, &\text{if } TX_i > 10°C\\YN_i & = 1.8(TN_i -4.44), &\text{if } TN_i > 4.44°C\end{aligned}\end{align} \]

where $YX_{i}$ and $YN_{i}$ is 0 when $TX_i \leq 10°C$ and $TN_i \leq 4.44°C$, respectively.

References

xclim.indicators.atmos._conversion.heat_index(tasmax: Union[DataArray, str] = 'tasmax', hurs: Union[DataArray, str] = 'hurs', *, ds: Dataset = None) → DataArray

Daily heat index. (realm: atmos)

Perceived temperature after relative humidity is taken into account ([Blazejczyk2012]). The index is only valid for temperatures above 20°C.

Based on indice heat_index().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
hurs (str or DataArray) – Relative humidity. Default : ds.hurs. [Required units : []]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_index (DataArray) – heat index (air_temperature) [C] description: Perceived temperature after relative humidity is taken into account.

Notes

While both the humidex and the heat index are calculated using dew point, the humidex uses a dew point of 7 °C (45 °F) as a base, whereas the heat index uses a dew point base of 14 °C (57 °F). Further, the heat index uses heat balance equations which account for many variables other than vapor pressure, which is used exclusively in the humidex calculation.

References

Blazejczyk2012: Blazejczyk, K., Epstein, Y., Jendritzky, G., Staiger, H., & Tinz, B. (2012). Comparison of UTCI to selected thermal indices. International journal of biometeorology, 56(3), 515-535.

xclim.indicators.atmos._conversion.humidex(tas: Union[DataArray, str] = 'tas', tdps: Optional[Union[DataArray, str]] = None, hurs: Optional[Union[DataArray, str]] = None, *, ds: Dataset = None) → DataArray

Humidex index. (realm: atmos)

The humidex indicates how hot the air feels to an average person, accounting for the effect of humidity. It can be loosely interpreted as the equivalent perceived temperature when the air is dry.

Based on indice humidex().

Parameters

tas (str or DataArray) – Air temperature. Default : ds.tas. [Required units : [temperature]]
tdps (str or DataArray, optional) – Dewpoint temperature. [Required units : [temperature]]
hurs (str or DataArray, optional) – Relative humidity. [Required units : []]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

humidex (DataArray) – humidex index (air_temperature) [C] description: Humidex index describing the temperature felt by the average person in response to relative humidity.

Notes

The humidex is usually computed using hourly observations of dry bulb and dewpoint temperatures. It is computed using the formula based on [masterton79]:

\[T + {\frac {5}{9}}\left[e - 10\right]\]

where $T$ is the dry bulb air temperature (°C). The term $e$ can be computed from the dewpoint temperature $T_{dewpoint}$ in °K:

\[e = 6.112 \times \exp(5417.7530\left({\frac {1}{273.16}}-{\frac {1}{T_{\text{dewpoint}}}}\right)\]

where the constant 5417.753 reflects the molecular weight of water, latent heat of vaporization, and the universal gas constant ([mekis15]). Alternatively, the term $e$ can also be computed from the relative humidity h expressed in percent using [sirangelo20]:

\[e = \frac{h}{100} \times 6.112 * 10^{7.5 T/(T + 237.7)}.\]

The humidex comfort scale ([eccc]) can be interpreted as follows:

20 to 29 : no discomfort;
30 to 39 : some discomfort;
40 to 45 : great discomfort, avoid exertion;
46 and over : dangerous, possible heat stroke;

Please note that while both the humidex and the heat index are calculated using dew point, the humidex uses a dew point of 7 °C (45 °F) as a base, whereas the heat index uses a dew point base of 14 °C (57 °F). Further, the heat index uses heat balance equations which account for many variables other than vapor pressure, which is used exclusively in the humidex calculation.

References

masterton79: Masterton, J. M., & Richardson, F. A. (1979). HUMIDEX, A method of quantifying human discomfort due to excessive heat and humidity, CLI 1-79. Downsview, Ontario: Environment Canada, Atmospheric Environment Service.
mekis15: Éva Mekis, Lucie A. Vincent, Mark W. Shephard & Xuebin Zhang (2015) Observed Trends in Severe Weather Conditions Based on Humidex, Wind Chill, and Heavy Rainfall Events in Canada for 1953–2012, Atmosphere-Ocean, 53:4, 383-397, DOI: 10.1080/07055900.2015.1086970
sirangelo20: Sirangelo, B., Caloiero, T., Coscarelli, R. et al. Combining stochastic models of air temperature and vapour pressure for the analysis of the bioclimatic comfort through the Humidex. Sci Rep 10, 11395 (2020). https://doi.org/10.1038/s41598-020-68297-4
eccc: https://climate.weather.gc.ca/glossary_e.html

xclim.indicators.atmos._conversion.mean_radiant_temperature(rsds: Union[DataArray, str] = 'rsds', rsus: Union[DataArray, str] = 'rsus', rlds: Union[DataArray, str] = 'rlds', rlus: Union[DataArray, str] = 'rlus', *, stat: str = 'average', ds: Dataset = None) → DataArray

Mean radiant temperature. (realm: atmos)

The mean radiant temperature is the incidence of radiation on the body from all directions. WARNING: There are some issues in the calculation of mrt in polar regions.

Based on indice mean_radiant_temperature().

Parameters

rsds (str or DataArray) – Surface Downwelling Shortwave Radiation Default : ds.rsds. [Required units : [radiation]]
rsus (str or DataArray) – Surface Upwelling Shortwave Radiation Default : ds.rsus. [Required units : [radiation]]
rlds (str or DataArray) – Surface Downwelling Longwave Radiation Default : ds.rlds. [Required units : [radiation]]
rlus (str or DataArray) – Surface Upwelling Longwave Radiation Default : ds.rlus. [Required units : [radiation]]
stat ({‘average’, ‘sunlit’, ‘instant’}) – Which statistic to apply. If “average”, the average of the cosine of the solar zenith angle is calculated. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. If “sunlit”, the cosine of the solar zenith angle is calculated during the sunlit period of each interval. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. This is necessary if mrt is not None. Default : average.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

mrt (DataArray) – Mean radiant temperature [K] description: The incidence of radiation on the body from all directions.

Notes

This code was inspired by the thermofeel package.

References

Di Napoli, C., Hogan, R.J. & Pappenberger, F. Mean radiant temperature from global-scale numerical weather prediction models. Int J Biometeorol 64, 1233–1245 (2020). https://doi.org/10.1007/s00484-020-01900-5 Brimicombe , C., Di Napoli, C., Quintino, T., Pappenberger, F., Cornforth, R. and Cloke, H., 2021 thermofeel: a python thermal comfort indices library, https://doi.org/10.21957/mp6v-fd16

xclim.indicators.atmos._conversion.potential_evapotranspiration(tasmin: Optional[Union[DataArray, str]] = None, tasmax: Optional[Union[DataArray, str]] = None, tas: Optional[Union[DataArray, str]] = None, lat: Optional[Union[DataArray, str]] = None, *, method: str = 'BR65', peta: float | None = 0.00516409319477, petb: float | None = 0.0874972822289, ds: Dataset = None) → DataArray

Potential evapotranspiration. (realm: atmos)

The potential for water evaporation from soil and transpiration by plants if the water supply is sufficient, according to a given method.

Based on indice potential_evapotranspiration().

Parameters

tasmin (str or DataArray, optional) – Minimum daily temperature. [Required units : [temperature]]
tasmax (str or DataArray, optional) – Maximum daily temperature. [Required units : [temperature]]
tas (str or DataArray, optional) – Mean daily temperature. [Required units : [temperature]]
lat (str or DataArray, optional) – Latitude. If not given, it is sought on tasmin or tas with cf-xarray. [Required units : []]
method ({‘hargreaves85’, ‘baierrobertson65’, ‘HG85’, ‘MB05’, ‘mcguinnessbordne05’, ‘thornthwaite48’, ‘BR65’, ‘TW48’}) – Which method to use, see notes. Default : BR65.
peta (number) – Used only with method MB05 as $a$ for calculation of PET, see Notes section. Default value resulted from calibration of PET over the UK. Default : 0.00516409319477.
petb (number) – Used only with method MB05 as $b$ for calculation of PET, see Notes section. Default value resulted from calibration of PET over the UK. Default : 0.0874972822289.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

evspsblpot (DataArray) – Potential evapotranspiration (water_potential_evapotranspiration_flux) [kg m-2 s-1] description: The potential for water evaporation from soil and transpiration by plants if the water supply is sufficient, with the method {method}.

Notes

Available methods are:

“baierrobertson65” or “BR65”, based on [BaierRobertson1965]. Requires tasmin and tasmax, daily [D] freq.
“hargreaves85” or “HG85”, based on [Hargreaves1985]. Requires tasmin and tasmax, daily [D] freq. (optional: tas can be given in addition of tasmin and tasmax).
“mcguinnessbordne05” or “MB05”, based on [Tanguy2018]. Requires tas, daily [D] freq, with latitudes ‘lat’.
“thornthwaite48” or “TW48”, based on [Thornthwaite1948]. Requires tasmin and tasmax, monthly [MS] or daily [D] freq. (optional: tas can be given instead of tasmin and tasmax).

The McGuinness-Bordne [McGuinness1972] equation is:

\[PET[mm day^{-1}] = a * \frac{S_0}{\lambda}T_a + b *\frsc{S_0}{\lambda}\]

where $a$ and $b$ are empirical parameters; $S_0$ is the extraterrestrial radiation [MJ m-2 day-1], assuming a solar constant of 1367 W m-2; $\\lambda$ is the latent heat of vaporisation [MJ kg-1] and $T_a$ is the air temperature [°C]. The equation was originally derived for the USA, with $a=0.0147$ and $b=0.07353$. The default parameters used here are calibrated for the UK, using the method described in [Tanguy2018].

Methods “BR65”, “HG85” and “MB05” use an approximation of the extraterrestrial radiation. See extraterrestrial_solar_radiation().

References

BaierRobertson1965: Baier, W., & Robertson, G. W. (1965). Estimation of latent evaporation from simple weather observations. Canadian journal of plant science, 45(3), 276-284.
Hargreaves1985: Hargreaves, G. H., & Samani, Z. A. (1985). Reference crop evapotranspiration from temperature. Applied engineering in agriculture, 1(2), 96-99.
Tanguy2018(1,2): Tanguy, M., Prudhomme, C., Smith, K., & Hannaford, J. (2018). Historical gridded reconstruction of potential evapotranspiration for the UK. Earth System Science Data, 10(2), 951-968.
McGuinness1972: McGuinness, J. L., & Bordne, E. F. (1972). A comparison of lysimeter-derived potential evapotranspiration with computed values (No. 1452). US Department of Agriculture.
Thornthwaite1948: Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical review, 38(1), 55-94.

xclim.indicators.atmos._conversion.rain_approximation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', method: str = 'binary', ds: Dataset = None) → DataArray

Rainfall approximation from total precipitation and temperature. (realm: atmos)

Liquid precipitation estimated from precipitation and temperature according to a given method. This is a convenience method based on snowfall_approximation(), see the latter for details.

Based on indice rain_approximation().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
tas (str or DataArray) – Mean, maximum, or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature, used by method “binary”. Default : 0 degC. [Required units : [temperature]]
method ({‘brown’, ‘auer’, ‘binary’}) – Which method to use when approximating snowfall from total precipitation. See notes. Default : binary.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

prlp (DataArray) – Liquid precipitation (precipitation_flux) [kg m-2 s-1] description: Liquid precipitation estimated from total precipitation and temperature with method {method} and threshold temperature {thresh}.

Notes

This method computes the snowfall approximation and subtracts it from the total precipitation to estimate the liquid rain precipitation.

xclim.indicators.atmos._conversion.relative_humidity(tas: Union[DataArray, str] = 'tas', huss: Union[DataArray, str] = 'huss', ps: Union[DataArray, str] = 'ps', *, ice_thresh: str = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Relative humidity from temperature, pressure and specific humidity. (realm: atmos)

Compute relative humidity from temperature and either dewpoint temperature or specific humidity and pressure through the saturation vapor pressure.

Based on indice relative_humidity(). With injected parameters: tdps=None, invalid_values=mask.

Parameters

tas (str or DataArray) – Temperature array Default : ds.tas. [Required units : [temperature]]
huss (str or DataArray) – Specific humidity. Default : ds.huss. [Required units : []]
ps (str or DataArray) – Air Pressure. Default : ds.ps. [Required units : [pressure]]
ice_thresh (quantity (string with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Does nothing if ‘method’ is “bohren98”. Default : None. [Required units : [temperature]]
method ({‘goffgratch46’, ‘wmo08’, ‘sonntag90’, ‘bohren98’, ‘tetens30’}) – Which method to use, see notes of this function and of saturation_vapor_pressure. Default : sonntag90.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hurs (DataArray) – Relative Humidity (relative_humidity) [%] description: <Dynamically generated string>

Notes

In the following, let $T$, $T_d$, $q$ and $p$ be the temperature, the dew point temperature, the specific humidity and the air pressure.

For the “bohren98” method : This method does not use the saturation vapor pressure directly, but rather uses an approximation of the ratio of $\frac{e_{sat}(T_d)}{e_{sat}(T)}$. With $L$ the enthalpy of vaporization of water and $R_w$ the gas constant for water vapor, the relative humidity is computed as:

\[RH = e^{\frac{-L (T - T_d)}{R_wTT_d}}\]

From [BohrenAlbrecht1998], formula taken from [Lawrence2005]. $L = 2.5\times 10^{-6}$ J kg-1, exact for $T = 273.15$ K, is used.

Other methods: With $w$, $w_{sat}$, $e_{sat}$ the mixing ratio, the saturation mixing ratio and the saturation vapor pressure. If the dewpoint temperature is given, relative humidity is computed as:

\[RH = 100\frac{e_{sat}(T_d)}{e_{sat}(T)}\]

Otherwise, the specific humidity and the air pressure must be given so relative humidity can be computed as:

\[RH = 100\frac{w}{w_{sat}} w = \frac{q}{1-q} w_{sat} = 0.622\frac{e_{sat}}{P - e_{sat}}\]

The methods differ by how $e_{sat}$ is computed. See the doc of xclim.core.utils.saturation_vapor_pressure().

References

Lawrence2005: Lawrence, M.G. (2005). The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications. Bull. Amer. Meteor. Soc., 86, 225–234, https://doi.org/10.1175/BAMS-86-2-225
BohrenAlbrecht1998: Craig F. Bohren, Bruce A. Albrecht. Atmospheric Thermodynamics. Oxford University Press, 1998.

xclim.indicators.atmos._conversion.relative_humidity_from_dewpoint(tas: Union[DataArray, str] = 'tas', tdps: Union[DataArray, str] = 'tdps', *, ice_thresh: str = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Relative humidity from temperature and dewpoint temperature. (realm: atmos)

Compute relative humidity from temperature and either dewpoint temperature or specific humidity and pressure through the saturation vapor pressure.

Based on indice relative_humidity(). With injected parameters: huss=None, ps=None, invalid_values=mask.

Parameters

tas (str or DataArray) – Temperature array Default : ds.tas. [Required units : [temperature]]
tdps (str or DataArray) – Dewpoint temperature, if specified, overrides huss and ps. Default : ds.tdps. [Required units : [temperature]]
ice_thresh (quantity (string with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Does nothing if ‘method’ is “bohren98”. Default : None. [Required units : [temperature]]
method ({‘goffgratch46’, ‘wmo08’, ‘sonntag90’, ‘bohren98’, ‘tetens30’}) – Which method to use, see notes of this function and of saturation_vapor_pressure. Default : sonntag90.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hurs (DataArray) – Relative Humidity (relative_humidity) [%] description: <Dynamically generated string>

Notes

In the following, let $T$, $T_d$, $q$ and $p$ be the temperature, the dew point temperature, the specific humidity and the air pressure.

For the “bohren98” method : This method does not use the saturation vapor pressure directly, but rather uses an approximation of the ratio of $\frac{e_{sat}(T_d)}{e_{sat}(T)}$. With $L$ the enthalpy of vaporization of water and $R_w$ the gas constant for water vapor, the relative humidity is computed as:

\[RH = e^{\frac{-L (T - T_d)}{R_wTT_d}}\]

From [BohrenAlbrecht1998], formula taken from [Lawrence2005]. $L = 2.5\times 10^{-6}$ J kg-1, exact for $T = 273.15$ K, is used.

Other methods: With $w$, $w_{sat}$, $e_{sat}$ the mixing ratio, the saturation mixing ratio and the saturation vapor pressure. If the dewpoint temperature is given, relative humidity is computed as:

\[RH = 100\frac{e_{sat}(T_d)}{e_{sat}(T)}\]

Otherwise, the specific humidity and the air pressure must be given so relative humidity can be computed as:

\[RH = 100\frac{w}{w_{sat}} w = \frac{q}{1-q} w_{sat} = 0.622\frac{e_{sat}}{P - e_{sat}}\]

The methods differ by how $e_{sat}$ is computed. See the doc of xclim.core.utils.saturation_vapor_pressure().

References

Lawrence2005: Lawrence, M.G. (2005). The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications. Bull. Amer. Meteor. Soc., 86, 225–234, https://doi.org/10.1175/BAMS-86-2-225
BohrenAlbrecht1998: Craig F. Bohren, Bruce A. Albrecht. Atmospheric Thermodynamics. Oxford University Press, 1998.

xclim.indicators.atmos._conversion.saturation_vapor_pressure(tas: Union[DataArray, str] = 'tas', *, ice_thresh: str = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Saturation vapor pressure from temperature. (realm: atmos)

Based on indice saturation_vapor_pressure().

Parameters

tas (str or DataArray) – Temperature array. Default : ds.tas. [Required units : [temperature]]
ice_thresh (quantity (string with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Default : None. [Required units : [temperature]]
method ({‘goffgratch46’, ‘wmo08’, ‘sonntag90’, ‘its90’, ‘tetens30’}) – Which method to use, see notes. Default : sonntag90.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

e_sat (DataArray) – Saturation vapor pressure [Pa] description: <Dynamically generated string>

Notes

In all cases implemented here $log(e_{sat})$ is an empirically fitted function (usually a polynomial) where coefficients can be different when ice is taken as reference instead of water. Available methods are:

“goffgratch46” or “GG46”, based on [goffgratch46], values and equation taken from [voemel].
“sonntag90” or “SO90”, taken from [sonntag90].
“tetens30” or “TE30”, based on [tetens30], values and equation taken from [voemel].
“wmo08” or “WMO08”, taken from [wmo08].
“its90” or “ITS90”, taken from [its90].

References

goffgratch46: Goff, J. A., and S. Gratch (1946) Low-pressure properties of water from -160 to 212 °F, in Transactions of the American Society of Heating and Ventilating Engineers, pp 95-122, presented at the 52nd annual meeting of the American Society of Heating and Ventilating Engineers, New York, 1946.
sonntag90: Sonntag, D. (1990). Important new values of the physical constants of 1986, vapour pressure formulations based on the ITS-90, and psychrometer formulae. Zeitschrift für Meteorologie, 40(5), 340-344.
tetens30: Tetens, O. 1930. Über einige meteorologische Begriffe. Z. Geophys 6: 207-309.
voemel(1,2): https://cires1.colorado.edu/~voemel/vp.html
wmo08: World Meteorological Organization. (2008). Guide to meteorological instruments and methods of observation. Geneva, Switzerland: World Meteorological Organization. https://www.weather.gov/media/epz/mesonet/CWOP-WMO8.pdf
its90: Hardy, B. (1998). ITS-90 formulations for vapor pressure, frostpoint temperature, dewpoint temperature, and enhancement factors in the range–100 to+ 100 C. In The Proceedings of the Third International Symposium on Humidity & Moisture (pp. 1-8). https://www.thunderscientific.com/tech_info/reflibrary/its90formulas.pdf

xclim.indicators.atmos._conversion.snowfall_approximation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', method: str = 'binary', ds: Dataset = None) → DataArray

Snowfall approximation from total precipitation and temperature. (realm: atmos)

Solid precipitation estimated from precipitation and temperature according to a given method.

Based on indice snowfall_approximation().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
tas (str or DataArray) – Mean, maximum, or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature, used by method “binary”. Default : 0 degC. [Required units : [temperature]]
method ({‘brown’, ‘auer’, ‘binary’}) – Which method to use when approximating snowfall from total precipitation. See notes. Default : binary.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

prsn (DataArray) – Solid precipitation (solid_precipitation_flux) [kg m-2 s-1] description: Solid precipitation estimated from total precipitation and temperature with method {method} and threshold temperature {thresh}.

Notes

The following methods are available to approximate snowfall and are drawn from the Canadian Land Surface Scheme (CLASS, [Verseghy09]).

'binary' : When the temperature is under the freezing threshold, precipitation is assumed to be solid. The method is agnostic to the type of temperature used (mean, maximum or minimum).
'brown' : The phase between the freezing threshold goes from solid to liquid linearly over a range of 2°C over the freezing point.
'auer' : The phase between the freezing threshold goes from solid to liquid as a degree six polynomial over a range of 6°C over the freezing point.

References

Verseghy09: Diana Verseghy (2009), CLASS – The Canadian Land Surface Scheme (Version 3.4), Technical Documentation (Version 1.1), Environment Canada, Climate Research Division, Science and Technology Branch.

https://gitlab.com/cccma/classic/-/blob/master/src/atmosphericVarsCalc.f90

xclim.indicators.atmos._conversion.specific_humidity(tas: Union[DataArray, str] = 'tas', hurs: Union[DataArray, str] = 'hurs', ps: Union[DataArray, str] = 'ps', *, ice_thresh: str = None, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Specific humidity from temperature, relative humidity and pressure. (realm: atmos)

Specific humidity is the ratio between the mass of water vapour and the mass of moist air [WMO08].

Based on indice specific_humidity(). With injected parameters: invalid_values=mask.

Parameters

tas (str or DataArray) – Temperature array Default : ds.tas. [Required units : [temperature]]
hurs (str or DataArray) – Relative Humidity. Default : ds.hurs. [Required units : []]
ps (str or DataArray) – Air Pressure. Default : ds.ps. [Required units : [pressure]]
ice_thresh (quantity (string with units)) – Threshold temperature under which to switch to equations in reference to ice instead of water. If None (default) everything is computed with reference to water. Default : None. [Required units : [temperature]]
method ({‘wmo08’, ‘goffgratch46’, ‘tetens30’, ‘sonntag90’}) – Which method to use, see notes of this function and of saturation_vapor_pressure. Default : sonntag90.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

huss (DataArray) – Specific Humidity (specific_humidity) description: <Dynamically generated string>

Notes

In the following, let $T$, $hurs$ (in %) and $p$ be the temperature, the relative humidity and the air pressure. With $w$, $w_{sat}$, $e_{sat}$ the mixing ratio, the saturation mixing ratio and the saturation vapor pressure, specific humidity $q$ is computed as:

\[w_{sat} = 0.622\frac{e_{sat}}{P - e_{sat}} w = w_{sat} * hurs / 100 q = w / (1 + w)\]

The methods differ by how $e_{sat}$ is computed. See the doc of xclim.core.utils.saturation_vapor_pressure.

If invalid_values is not None, the saturation specific humidity $q_{sat}$ is computed as:

\[q_{sat} = w_{sat} / (1 + w_{sat})\]

References

WMO08: World Meteorological Organization. (2008). Guide to meteorological instruments and methods of observation. Geneva, Switzerland: World Meteorological Organization. https://www.weather.gov/media/epz/mesonet/CWOP-WMO8.pdf

xclim.indicators.atmos._conversion.specific_humidity_from_dewpoint(tdps: Union[DataArray, str] = 'tdps', ps: Union[DataArray, str] = 'ps', *, method: str = 'sonntag90', ds: Dataset = None) → DataArray

Specific humidity from dewpoint temperature and air pressure. (realm: atmos)

Specific humidity is the ratio between the mass of water vapour and the mass of moist air [WMO08].

Based on indice specific_humidity_from_dewpoint().

Parameters

tdps (str or DataArray) – Dewpoint temperature array. Default : ds.tdps. [Required units : [temperature]]
ps (str or DataArray) – Air pressure array. Default : ds.ps. [Required units : [pressure]]
method ({‘wmo08’, ‘goffgratch46’, ‘tetens30’, ‘sonntag90’}) – Method to compute the saturation vapor pressure. Default : sonntag90.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

huss_fromdewpoint (DataArray) – Specific Humidity (specific_humidity) description: Computed from dewpoint temperature and pressure through the saturation vapor pressure, which was calculated according to the {method} method.

Notes

If $e$ is the water vapor pressure, and $p$ the total air pressure, then specific humidity is given by

\[q = m_w e / ( m_a (p - e) + m_w e )\]

where $m_w$ and $m_a$ are the molecular weights of water and dry air respectively. This formula is often written with $ε = m_w / m_a$, which simplifies to $q = ε e / (p - e (1 - ε))$.

References

WMO08: World Meteorological Organization. (2008). Guide to meteorological instruments and methods of observation. Geneva, Switzerland: World Meteorological Organization. https://www.weather.gov/media/epz/mesonet/CWOP-WMO8.pdf

xclim.indicators.atmos._conversion.tg(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, ds: Dataset = None) → DataArray

Average temperature from minimum and maximum temperatures. (realm: atmos)

We assume a symmetrical distribution for the temperature and retrieve the average value as Tg = (Tx + Tn) / 2

Based on indice tas().

Parameters

tasmin (str or DataArray) – Minimum (daily) temperature Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum (daily) temperature Default : ds.tasmax. [Required units : [temperature]]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

tg (DataArray) – Daily mean temperature (air_temperature) [K] cell_methods: time: mean within days description: Estimated mean temperature from maximum and minimum temperatures

xclim.indicators.atmos._conversion.universal_thermal_climate_index(tas: Union[DataArray, str] = 'tas', hurs: Union[DataArray, str] = 'hurs', sfcWind: Union[DataArray, str] = 'sfcWind', mrt: Optional[Union[DataArray, str]] = None, rsds: Optional[Union[DataArray, str]] = None, rsus: Optional[Union[DataArray, str]] = None, rlds: Optional[Union[DataArray, str]] = None, rlus: Optional[Union[DataArray, str]] = None, *, stat: str = 'average', mask_invalid: bool = True, ds: Dataset = None) → DataArray

Universal thermal climate index. (realm: atmos)

The UTCI is the equivalent temperature for the environment derived from a reference environment and is used to evaluate heat stress in outdoor spaces.

Based on indice universal_thermal_climate_index().

Parameters

tas (str or DataArray) – Mean temperature Default : ds.tas. [Required units : [temperature]]
hurs (str or DataArray) – Relative Humidity Default : ds.hurs. [Required units : []]
sfcWind (str or DataArray) – Wind velocity Default : ds.sfcWind. [Required units : [speed]]
mrt (str or DataArray, optional) – Mean radiant temperature [Required units : [temperature]]
rsds (str or DataArray, optional) – Surface Downwelling Shortwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]
rsus (str or DataArray, optional) – Surface Upwelling Shortwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]
rlds (str or DataArray, optional) – Surface Downwelling Longwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]
rlus (str or DataArray, optional) – Surface Upwelling Longwave Radiation This is necessary if mrt is not None. [Required units : [radiation]]
stat ({‘average’, ‘sunlit’, ‘instant’}) – Which statistic to apply. If “average”, the average of the cosine of the solar zenith angle is calculated. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. If “sunlit”, the cosine of the solar zenith angle is calculated during the sunlit period of each interval. If “instant”, the instantaneous cosine of the solar zenith angle is calculated. This is necessary if mrt is not None. Default : average.
mask_invalid (boolean) – If True (default), UTCI values are NaN where any of the inputs are outside their validity ranges : -50°C < tas < 50°C, -30°C < tas - mrt < 30°C and 0.5 m/s < sfcWind < 17.0 m/s. Default : True.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

utci (DataArray) – Universal Thermal Climate Index [K] description: UTCI is the equivalent temperature for the environment derived from a reference environment and is used to evaluate heat stress in outdoor spaces.

Notes

The calculation uses water vapor partial pressure, which is derived from relative humidity and saturation vapor pressure computed according to the ITS-90 equation.

This code was inspired by the pythermalcomfort and thermofeel packages.

References

Bröde, Peter (2009). Program for calculating UTCI Temperature (UTCI), version a 0.002, http://www.utci.org/public/UTCI%20Program%20Code/UTCI_a002.f90 Błażejczyk, K., Jendritzky, G., Bröde, P., Fiala, D., Havenith, G., Epstein, Y., Psikuta, A., & Kampmann, B. (2013). An introduction to the Universal Thermal Climate Index (UTCI). DOI:10.7163/GPOL.2013.1

xclim.indicators.atmos._conversion.water_budget(pr: Union[DataArray, str] = 'pr', evspsblpot: Optional[Union[DataArray, str]] = None, tasmin: Optional[Union[DataArray, str]] = None, tasmax: Optional[Union[DataArray, str]] = None, tas: Optional[Union[DataArray, str]] = None, lat: Optional[Union[DataArray, str]] = None, *, ds: Dataset = None) → DataArray

Precipitation minus potential evapotranspiration. (realm: atmos)

Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget, where the potential evapotranspiration can be calculated with a given method.

Based on indice water_budget(). With injected parameters: method=dummy.

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
evspsblpot (str or DataArray, optional) – Potential evapotranspiration [Required units : [precipitation]]
tasmin (str or DataArray, optional) – Minimum daily temperature. [Required units : [temperature]]
tasmax (str or DataArray, optional) – Maximum daily temperature. [Required units : [temperature]]
tas (str or DataArray, optional) – Mean daily temperature. [Required units : [temperature]]
lat (str or DataArray, optional) – Latitude, needed if evspsblpot is not given. [Required units : []]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

water_budget (DataArray) – Water budget [kg m-2 s-1] description: Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget.

Notes

Available methods are listed in the description of xclim.indicators.atmos.potential_evapotranspiration.

xclim.indicators.atmos._conversion.water_budget_from_tas(pr: Union[DataArray, str] = 'pr', evspsblpot: Optional[Union[DataArray, str]] = None, tasmin: Optional[Union[DataArray, str]] = None, tasmax: Optional[Union[DataArray, str]] = None, tas: Optional[Union[DataArray, str]] = None, lat: Optional[Union[DataArray, str]] = None, *, method: str = 'BR65', ds: Dataset = None) → DataArray

Precipitation minus potential evapotranspiration. (realm: atmos)

Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget, where the potential evapotranspiration can be calculated with a given method.

Based on indice water_budget().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
evspsblpot (str or DataArray, optional) – Potential evapotranspiration [Required units : [precipitation]]
tasmin (str or DataArray, optional) – Minimum daily temperature. [Required units : [temperature]]
tasmax (str or DataArray, optional) – Maximum daily temperature. [Required units : [temperature]]
tas (str or DataArray, optional) – Mean daily temperature. [Required units : [temperature]]
lat (str or DataArray, optional) – Latitude, needed if evspsblpot is not given. [Required units : []]
method (str) – Method to use to calculate the potential evapotranspiration. Default : BR65.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

water_budget_from_tas (DataArray) – Water budget [kg m-2 s-1] description: Precipitation minus potential evapotranspiration as a measure of an approximated surface water budget, where the potential evapotranspiration is calculated with the method {method}.

Notes

Available methods are listed in the description of xclim.indicators.atmos.potential_evapotranspiration.

xclim.indicators.atmos._conversion.wind_chill_index(tas: Union[DataArray, str] = 'tas', sfcWind: Union[DataArray, str] = 'sfcWind', *, method: str = 'CAN', ds: Dataset = None) → DataArray

Wind chill index. (realm: atmos)

The Wind Chill Index is an estimation of how cold the weather feels to the average person. It is computed from the air temperature and the 10-m wind. As defined by the Environment and Climate Change Canada ([MVSZ2015]), two equations exist, the conventional one and one for slow winds (usually < 5 km/h), see Notes.

Based on indice wind_chill_index(). With injected parameters: mask_invalid=True.

Parameters

tas (str or DataArray) – Surface air temperature. Default : ds.tas. [Required units : [temperature]]
sfcWind (str or DataArray) – Surface wind speed (10 m). Default : ds.sfcWind. [Required units : [speed]]
method ({‘US’, ‘CAN’}) – If “CAN” (default), a “slow wind” equation is used where winds are slower than 5 km/h, see Notes. Default : CAN.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

wind_chill (DataArray) – Wind chill index [degC] description: <Dynamically generated string>

Notes

Following the calculations of Environment and Climate Change Canada, this function switches from the standardized index to another one for slow winds. The standard index is the same as used by the National Weather Service of the USA ([NWS]). Given a temperature at surface $T$ (in °C) and 10-m wind speed $V$ (in km/h), the Wind Chill Index $W$ (dimensionless) is computed as:

\[W = 13.12 + 0.6125*T - 11.37*V^0.16 + 0.3965*T*V^0.16\]

Under slow winds ($V < 5$ km/h), and using the canadian method, it becomes:

\[W = T + \frac{-1.59 + 0.1345 * T}{5} * V\]

Both equations are invalid for temperature over 0°C in the canadian method.

The american Wind Chill Temperature index (WCT), as defined by USA’s National Weather Service, is computed when method=’US’. In that case, the maximal valid temperature is 50°F (10 °C) and minimal wind speed is 3 mph (4.8 km/h).

References

MVSZ2015: Éva Mekis, Lucie A. Vincent, Mark W. Shephard & Xuebin Zhang (2015) Observed Trends in Severe Weather Conditions Based on Humidex, Wind Chill, and Heavy Rainfall Events in Canada for 1953–2012, Atmosphere-Ocean, 53:4, 383-397, DOI: 10.1080/07055900.2015.1086970

NWS: Wind Chill Questions, Cold Resources, National Weather Service, retrieved 25-05-21. https://www.weather.gov/safety/cold-faqs

xclim.indicators.atmos._conversion.wind_speed_from_vector(uas: Union[DataArray, str] = 'uas', vas: Union[DataArray, str] = 'vas', *, calm_wind_thresh: str = '0.5 m/s', ds: Dataset = None) → Tuple[DataArray, DataArray]

Wind speed and direction from the eastward and northward wind components. (realm: atmos)

Computes the magnitude and angle of the wind vector from its northward and eastward components, following the meteorological convention that sets calm wind to a direction of 0° and northerly wind to 360°.

Based on indice uas_vas_2_sfcwind().

Parameters

uas (str or DataArray) – Eastward wind velocity Default : ds.uas. [Required units : [speed]]
vas (str or DataArray) – Northward wind velocity Default : ds.vas. [Required units : [speed]]
calm_wind_thresh (quantity (string with units)) – The threshold under which winds are considered “calm” and for which the direction is set to 0. On the Beaufort scale, calm winds are defined as < 0.5 m/s. Default : 0.5 m/s. [Required units : [speed]]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

sfcWind (DataArray) – Near-Surface Wind Speed (wind_speed) [m s-1] description: Wind speed computed as the magnitude of the (uas, vas) vector.
sfcWindfromdir (DataArray) – Near-Surface Wind from Direction (wind_from_direction) [degree] description: Wind direction computed as the angle of the (uas, vas) vector. A direction of 0° is attributed to winds with a speed under {calm_wind_thresh}.

Notes

Winds with a velocity less than calm_wind_thresh are given a wind direction of 0°, while stronger northerly winds are set to 360°.

xclim.indicators.atmos._conversion.wind_vector_from_speed(sfcWind: Union[DataArray, str] = 'sfcWind', sfcWindfromdir: Union[DataArray, str] = 'sfcWindfromdir', *, ds: Dataset = None) → Tuple[DataArray, DataArray]

Eastward and northward wind components from the wind speed and direction. (realm: atmos)

Compute the eastward and northward wind components from the wind speed and direction.

Based on indice sfcwind_2_uas_vas().

Parameters

sfcWind (str or DataArray) – Wind velocity Default : ds.sfcWind. [Required units : [speed]]
sfcWindfromdir (str or DataArray) – Direction from which the wind blows, following the meteorological convention where 360 stands for North. Default : ds.sfcWindfromdir. [Required units : []]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

uas (DataArray) – Near-Surface Eastward Wind (eastward_wind) [m s-1] description: Eastward wind speed computed from its speed and direction of origin.
vas (DataArray) – Near-Surface Northward Wind (northward_wind) [m s-1] description: Northward wind speed computed from its speed and direction of origin.

xclim.indicators.atmos._precip module

Precipitation indicator definitions.

xclim.indicators.atmos._precip.cold_and_dry_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Cold and dry days (realm: atmos)

Returns the total number of days where “Cold” and “Dry” conditions coincide.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_and_dry_days().

Parameters

tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]
pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
tas_per (str or DataArray) – First quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]
pr_per (str or DataArray) – First quartile of daily total precipitation computed by month. .. warning:: Before computing the percentiles, all the precipitation below 1mm must be filtered out ! Otherwise, the percentiles will include non-wet days. Default : ds.pr_per. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

cold_and_dry_days (DataArray) – Cold and dry days [days] cell_methods: time: sum over days description: {freq} number of days where tas < {tas_per_thresh}th percentile and pr < {pr_per_thresh}th percentile

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written [cold_dry_days].

References

cold_dry_days: Beniston, M. (2009). Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100. Geophysical Research Letters, 36(7). https://doi.org/10.1029/2008GL037119

xclim.indicators.atmos._precip.cold_and_wet_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

cold and wet days (realm: atmos)

Returns the total number of days where “cold” and “wet” conditions coincide.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_and_wet_days().

Parameters

tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]
pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
tas_per (str or DataArray) – First quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]
pr_per (str or DataArray) – Third quartile of daily total precipitation computed by month. Default : ds.pr_per. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

cold_and_wet_days (DataArray) – cold and wet days [days] cell_methods: time: sum over days description: {freq} number of days where tas < {tas_per_thresh}th percentile and pr > {pr_per_thresh}th percentile

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written [cold_wet_days].

References

cold_wet_days: Beniston, M. (2009). Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100. Geophysical Research Letters, 36(7). https://doi.org/10.1029/2008GL037119

xclim.indicators.atmos._precip.daily_pr_intensity(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Average daily precipitation intensity. (realm: atmos)

Return the average precipitation over wet days.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_pr_intensity().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

sdii (DataArray) – Average precipitation during wet days (SDII) (lwe_thickness_of_precipitation_amount) [mm/day] description: {freq} Simple Daily Intensity Index (SDII) : {freq} average precipitation for days with daily precipitation over {thresh}. This indicator is also known as the ‘Simple Daily Intensity Index’ (SDII).

Notes

Let $\mathbf{p} = p_0, p_1, \ldots, p_n$ be the daily precipitation and $thresh$ be the precipitation threshold defining wet days. Then the daily precipitation intensity is defined as

\[\frac{\sum_{i=0}^n p_i [p_i \leq thresh]}{\sum_{i=0}^n [p_i \leq thresh]}\]

where $[P]$ is 1 if $P$ is true, and 0 if false.

xclim.indicators.atmos._precip.days_over_precip_doy_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: str = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of wet days with daily precipitation over a given percentile. (realm: atmos)

Number of days over period where the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice days_over_precip_thresh().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

days_over_precip_doy_thresh (DataArray) – Count of days with daily precipitation above the given percentile [days]. (number_of_days_with_lwe_thickness_of_precipitation_amount_above_daily_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with precipitation above the {pr_per_thresh}th daily percentile. Only days with at least {thresh} are counted. A {pr_per_window} day(s) window, centred on each calendar day in the {pr_per_period} period, is used to compute the {pr_per_thresh}th percentile(s).

xclim.indicators.atmos._precip.days_over_precip_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: str = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of wet days with daily precipitation over a given percentile. (realm: atmos)

Number of days over period where the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice days_over_precip_thresh().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

days_over_precip_thresh (DataArray) – Count of days with daily precipitation above the given percentile [days]. (number_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with precipitation above the {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are counted.

xclim.indicators.atmos._precip.days_with_snow(prsn: Union[DataArray, str] = 'prsn', *, low: str = '0 kg m-2 s-1', high: str = '1E6 kg m-2 s-1', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Days with snowfall (realm: atmos)

Return the number of days where snowfall is within low and high thresholds.

This indicator will check for missing values according to the method “from_context”. Based on indice days_with_snow().

Parameters

prsn (str or DataArray) – Solid precipitation flux. Default : ds.prsn. [Required units : [precipitation]]
low (quantity (string with units)) – Minimum threshold solid precipitation flux. Default : 0 kg m-2 s-1. [Required units : [precipitation]]
high (quantity (string with units)) – Maximum threshold solid precipitation flux. Default : 1E6 kg m-2 s-1. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency defining the periods as defined in https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling. Default : AS-JUL.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

days_with_snow (DataArray) – Number of days with solid precipitation flux between low and high thresholds. [days] description: {freq} number of days with solid precipitation flux larger than {low} and smaller or equal to {high}.

References

Matthews, L., Andrey, J., & Picketts, I. (2017). Planning for Winter Road Maintenance in the Context of Climate Change, Weather, Climate, and Society, 9(3), 521-532, https://doi.org/10.1175/WCAS-D-16-0103.1

xclim.indicators.atmos._precip.drought_code(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', lat: Union[DataArray, str] = 'lat', snd: Optional[Union[DataArray, str]] = None, dc0: Optional[Union[DataArray, str]] = None, season_mask: Optional[Union[DataArray, str]] = None, *, season_method: str | None = None, overwintering: bool = False, dry_start: str | None = None, initial_start_up: bool = True, ds: Dataset = None, **params) → DataArray

Drought code (FWI component). (realm: atmos)

The drought code is part of the Canadian Forest Fire Weather Index System. It is a numeric rating of the average moisture content of organic layers.

This indicator will check for missing values according to the method “skip”. Based on indice drought_code().

Parameters

tas (str or DataArray) – Noon temperature. Default : ds.tas. [Required units : [temperature]]
pr (str or DataArray) – Rain fall in open over previous 24 hours, at noon. Default : ds.pr. [Required units : [precipitation]]
lat (str or DataArray) – Latitude coordinate Default : ds.lat. [Required units : []]
snd (str or DataArray, optional) – Noon snow depth. [Required units : [length]]
dc0 (str or DataArray, optional) – Initial values of the drought code. [Required units : []]
season_mask (str or DataArray, optional) – Boolean mask, True where/when the fire season is active. [Required units : []]
season_method ({‘LA08’, None, ‘GFWED’, ‘WF93’}) – How to compute the start-up and shutdown of the fire season. If “None”, no start-ups or shutdowns are computed, similar to the R fwi function. Ignored if season_mask is given. Default : None.
overwintering (boolean) – Whether to activate DC overwintering or not. If True, either season_method or season_mask must be given. Default : False.
dry_start ({None, ‘GFWED’, ‘CFS’}) – Whether to activate the DC and DMC “dry start” mechanism and which method to use. , see fire_weather_ufunc(). Default : None.
initial_start_up (boolean) – If True (default), grid points where the fire season is active on the first timestep go through a start_up phase for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those points. Default : True.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
params – Any other keyword parameters as defined in xclim.indices.fwi.fire_weather_ufunc and in default_params. Default : None.

Returns

dc (DataArray) – Drought Code (drought_code) description: Numeric rating of the average moisture content of organic layers.

Notes

See https://cwfis.cfs.nrcan.gc.ca/background/dsm/fwi, the module’s doc and doc of fire_weather_ufunc() for more information.

References

Updated source code for calculating fire danger indexes in the Canadian Forest Fire Weather Index System, Y. Wang, K.R. Anderson, and R.M. Suddaby, INFORMATION REPORT NOR-X-424, 2015.

xclim.indicators.atmos._precip.dry_days(pr: Union[DataArray, str] = 'pr', *, thresh: str = '0.2 mm/d', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Dry days. (realm: atmos)

The number of days with daily precipitation below threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice dry_days().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0.2 mm/d. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dry_days (DataArray) – Number of dry days (precip < {thresh}) (number_of_days_with_lwe_thickness_of_precipitation_amount_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with daily precipitation under {thresh}.

Notes

Let $PR_{ij}$ be the daily precipitation at day $i$ of period $j$. Then counted is the number of days where:

\[\sum PR_{ij} < Threshold [mm/day]\]

xclim.indicators.atmos._precip.dry_spell_frequency(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1.0 mm', window: int = 3, freq: str = 'YS', op: str = 'sum', ds: Dataset = None) → DataArray

Return the number of dry periods of n days and more. (realm: atmos)

Periods during which the accumulated or maximal daily precipitation amount on a window of n days is under threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice dry_spell_frequency().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation amount under which a period is considered dry. The value against which the threshold is compared depends on op . Default : 1.0 mm. [Required units : [length]]
window (number) – Minimum length of the spells. Default : 3.
freq (offset alias (string)) – Resampling frequency. Default : YS.
op ({‘sum’, ‘max’}) – Operation to perform on the window. Default is “sum”, which checks that the sum of accumulated precipitation over the whole window is less than the threshold. “max” checks that the maximal daily precipitation amount within the window is less than the threshold. This is the same as verifying that each individual day is below the threshold. Default : sum.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

dry_spell_frequency (DataArray) – The {freq} number of dry periods of minimum {window} days. description: The {freq} number of dry periods of {window} days and more, during which the {op} precipitation on a window of {window} days is under {thresh}.

xclim.indicators.atmos._precip.dry_spell_total_length(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1.0 mm', window: int = 3, op: str = 'sum', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Total length of dry spells. (realm: atmos)

Total number of days in dry periods of a minimum length, during which the maximum or accumulated precipitation within a window of the same length is under a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice dry_spell_total_length().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Accumulated precipitation value under which a period is considered dry. Default : 1.0 mm. [Required units : [length]]
window (number) – Number of days when the maximum or accumulated precipitation is under threshold. Default : 3.
op ({‘sum’, ‘max’}) – Reduce operation. Default : sum.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Indexing is done after finding the dry days, but before finding the spells. Default : None.

Returns

dry_spell_total_length (DataArray) – The {freq} total number of days in dry periods of minimum {window} days. [days] description: The {freq} number of days in dry periods of {window} days and more, during which the {op}precipitation within windows of {window} days is under {thresh}.

Notes

The algorithm assumes days before and after the timeseries are “wet”, meaning that the condition for being considered part of a dry spell is stricter on the edges. For example, with window=3 and op=’sum’, the first day of the series is considered part of a dry spell only if the accumulated precipitation within the first 3 days is under the threshold. In comparison, a day in the middle of the series is considered part of a dry spell if any of the three 3-day periods of which it is part are considered dry (so a total of five days are included in the computation, compared to only 3.)

xclim.indicators.atmos._precip.fire_weather_indexes(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', sfcWind: Union[DataArray, str] = 'sfcWind', hurs: Union[DataArray, str] = 'hurs', lat: Union[DataArray, str] = 'lat', snd: Optional[Union[DataArray, str]] = None, ffmc0: Optional[Union[DataArray, str]] = None, dmc0: Optional[Union[DataArray, str]] = None, dc0: Optional[Union[DataArray, str]] = None, season_mask: Optional[Union[DataArray, str]] = None, *, season_method: str | None = None, overwintering: bool = False, dry_start: str | None = None, initial_start_up: bool = True, ds: Dataset = None, **params) → Tuple[DataArray, DataArray, DataArray, DataArray, DataArray, DataArray]

Fire weather indexes. (realm: atmos)

Computes the 6 fire weather indexes as defined by the Canadian Forest Service: the Drought Code, the Duff-Moisture Code, the Fine Fuel Moisture Code, the Initial Spread Index, the Build Up Index and the Fire Weather Index.

This indicator will check for missing values according to the method “skip”. Based on indice fire_weather_indexes().

Parameters

tas (str or DataArray) – Noon temperature. Default : ds.tas. [Required units : [temperature]]
pr (str or DataArray) – Rain fall in open over previous 24 hours, at noon. Default : ds.pr. [Required units : [precipitation]]
sfcWind (str or DataArray) – Noon wind speed. Default : ds.sfcWind. [Required units : [speed]]
hurs (str or DataArray) – Noon relative humidity. Default : ds.hurs. [Required units : []]
lat (str or DataArray) – Latitude coordinate Default : ds.lat. [Required units : []]
snd (str or DataArray, optional) – Noon snow depth, only used if season_method=’LA08’ is passed. [Required units : [length]]
ffmc0 (str or DataArray, optional) – Initial values of the fine fuel moisture code. [Required units : []]
dmc0 (str or DataArray, optional) – Initial values of the Duff moisture code. [Required units : []]
dc0 (str or DataArray, optional) – Initial values of the drought code. [Required units : []]
season_mask (str or DataArray, optional) – Boolean mask, True where/when the fire season is active. [Required units : []]
season_method ({‘LA08’, None, ‘GFWED’, ‘WF93’}) – How to compute the start-up and shutdown of the fire season. If “None”, no start-ups or shutdowns are computed, similar to the R fwi function. Ignored if season_mask is given. Default : None.
overwintering (boolean) – Whether to activate DC overwintering or not. If True, either season_method or season_mask must be given. Default : False.
dry_start ({None, ‘GFWED’, ‘CFS’}) – Whether to activate the DC and DMC “dry start” mechanism or not, see fire_weather_ufunc(). Default : None.
initial_start_up (boolean) – If True (default), gridpoints where the fire season is active on the first timestep go through a start_up phase for that time step. Otherwise, previous codes must be given as a continuing fire season is assumed for those points. Default : True.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
params – Any other keyword parameters as defined in fire_weather_ufunc() and in default_params. Default : None.

Returns

dc (DataArray) – Drought Code (drought_code) description: Numeric rating of the average moisture content of deep, compact organic layers.
dmc (DataArray) – Duff Moisture Code (duff_moisture_code) description: Numeric rating of the average moisture content of loosely compacted organic layers of moderate depth.
ffmc (DataArray) – Fine Fuel Moisture Code (fine_fuel_moisture_code) description: Numeric rating of the average moisture content of litter and other cured fine fuels.
isi (DataArray) – Initial Spread Index (initial_spread_index) description: Numeric rating of the expected rate of fire spread.
bui (DataArray) – Buildup Index (buildup_index) description: Numeric rating of the total amount of fuel available for combustion.
fwi (DataArray) – Fire Weather Index (fire_weather_index) description: Numeric rating of fire intensity.

Notes

See https://cwfis.cfs.nrcan.gc.ca/background/dsm/fwi, the module’s doc and doc of fire_weather_ufunc() for more information.

References

Updated source code for calculating fire danger indexes in the Canadian Forest Fire Weather Index System, Y. Wang, K.R. Anderson, and R.M. Suddaby, INFORMATION REPORT NOR-X-424, 2015.

xclim.indicators.atmos._precip.first_snowfall(prsn: Union[DataArray, str] = 'prsn', *, thresh: str = '0.5 mm/day', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

First day with solid precipitation above a threshold. (realm: atmos)

Returns the first day of a period where the solid precipitation exceeds a threshold. WARNING: The default freq is valid for the northern hemisphere.

This indicator will check for missing values according to the method “from_context”. Based on indice first_snowfall().

Parameters

prsn (str or DataArray) – Solid precipitation flux. Default : ds.prsn. [Required units : [precipitation]]
thresh (quantity (string with units)) – Threshold precipitation flux on which to base evaluation. Default : 0.5 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

first_snowfall (DataArray) – Date of first snowfall (day_of_year) description: {freq} first day where the solid precipitation flux exceeded {thresh}

References

Climate Projections for the National Capital Region (2020), Volume 1: Results and Interpretation for Key Climate Indices, Report 193600.00, Prepared for Ottawa by CBCL.

xclim.indicators.atmos._precip.fraction_over_precip_doy_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: str = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Fraction of precipitation due to wet days with daily precipitation over a given percentile. (realm: atmos)

Percentage of the total precipitation over period occurring in days where the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice fraction_over_precip_thresh().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

fraction_over_precip_doy_thresh (DataArray) – Fraction of precipitation over threshold during wet days. description: {freq} fraction of total precipitation due to days with precipitation above {pr_per_thresh}th daily percentile. Only days with at least {thresh} are included in the total. A {pr_per_window} day(s) window, centred on each calendar day in the {pr_per_period} period, is used to compute the {pr_per_thresh}th percentile(s).

xclim.indicators.atmos._precip.fraction_over_precip_thresh(pr: Union[DataArray, str] = 'pr', pr_per: Union[DataArray, str] = 'pr_per', *, thresh: str = '1 mm/day', freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Fraction of precipitation due to wet days with daily precipitation over a given percentile. (realm: atmos)

Percentage of the total precipitation over period occurring in days where the precipitation is above a threshold defining wet days and above a given percentile for that day.

This indicator will check for missing values according to the method “from_context”. Based on indice fraction_over_precip_thresh().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
pr_per (str or DataArray) – Percentile of wet day precipitation flux. Either computed daily (one value per day of year) or computed over a period (one value per spatial point). Default : ds.pr_per. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

fraction_over_precip_thresh (DataArray) – Fraction of precipitation over threshold during wet days. description: {freq} fraction of total precipitation due to days with precipitation above {pr_per_thresh}th percentile of {pr_per_period} period. Only days with at least {thresh} are included in the total.

xclim.indicators.atmos._precip.high_precip_low_temp(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, pr_thresh: str = '0.4 mm/d', tas_thresh: str = '-0.2 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with precipitation above threshold and temperature below threshold. (realm: atmos)

Number of days where precipitation is greater or equal to some threshold, and temperatures are colder than some threshold. This can be used for example to identify days with the potential for freezing rain or icing conditions.

This indicator will check for missing values according to the method “from_context”. Based on indice high_precip_low_temp().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
tas (str or DataArray) – Daily mean, minimum or maximum temperature. Default : ds.tas. [Required units : [temperature]]
pr_thresh (quantity (string with units)) – Precipitation threshold to exceed. Default : 0.4 mm/d. [Required units : [precipitation]]
tas_thresh (quantity (string with units)) – Temperature threshold not to exceed. Default : -0.2 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

high_precip_low_temp (DataArray) – Count of days with high precipitation and low temperatures. [days] cell_methods: time: sum over days description: {freq} number of days with precipitation above {pr_thresh} and temperature below {tas_thresh}.

xclim.indicators.atmos._precip.last_snowfall(prsn: Union[DataArray, str] = 'prsn', *, thresh: str = '0.5 mm/day', freq: str = 'AS-JUL', ds: Dataset = None, **indexer) → DataArray

Last day with solid precipitation above a threshold. (realm: atmos)

Returns the last day of a period where the solid precipitation exceeds a threshold. WARNING: The default freq is valid for the northern hemisphere.

This indicator will check for missing values according to the method “from_context”. Based on indice last_snowfall().

Parameters

prsn (str or DataArray) – Solid precipitation flux. Default : ds.prsn. [Required units : [precipitation]]
thresh (quantity (string with units)) – Threshold precipitation flux on which to base evaluation. Default : 0.5 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

last_snowfall (DataArray) – Date of last snowfall (day_of_year) description: {freq} last day where the solid precipitation flux exceeded {thresh}

References

Climate Projections for the National Capital Region (2020), Volume 1: Results and Interpretation for Key Climate Indices, Report 193600.00, Prepared for Ottawa by CBCL.

xclim.indicators.atmos._precip.liquid_precip_accumulation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Accumulated liquid precipitation. (realm: atmos)

Resample the original daily mean precipitation flux and accumulate over each period. If a daily temperature is provided, the phase keyword can be used to sum precipitation of a given phase only. When the temperature is under the provided threshold, precipitation is assumed to be snow, and liquid rain otherwise. This indice is agnostic to the type of daily temperature (tas, tasmax or tasmin) given.

This indicator will check for missing values according to the method “from_context”. Based on indice precip_accumulation(). With injected parameters: phase=liquid.

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
tas (str or DataArray) – Mean, maximum or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold of tas over which the precipication is assumed to be liquid rain. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

liquidprcptot (DataArray) – Total liquid precipitation (lwe_thickness_of_liquid_precipitation_amount) [mm] cell_methods: time: sum over days description: {freq} total {phase} precipitation, estimated as precipitation when temperature >= {thresh}

Notes

Let $PR_i$ be the mean daily precipitation of day $i$, then for a period $j$ starting at day $a$ and finishing on day $b$:

\[PR_{ij} = \sum_{i=a}^{b} PR_i\]

If tas and phase are given, the corresponding phase precipitation is estimated before computing the accumulation, using one of snowfall_approximation or rain_approximation with the binary method.

xclim.indicators.atmos._precip.liquid_precip_ratio(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', freq: str = 'QS-DEC', ds: Dataset = None, **indexer) → DataArray

Ratio of rainfall to total precipitation. (realm: atmos)

The ratio of total liquid precipitation over the total precipitation. Liquid precipitation is approximated from total precipitation on days where temperature is above a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice liquid_precip_ratio(). With injected parameters: prsn=None.

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature under which precipitation is assumed to be solid. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : QS-DEC.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

liquid_precip_ratio (DataArray) – Ratio of rainfall to total precipitation. description: {freq} ratio of rainfall to total precipitation. Rainfall is estimated as precipitation on days where temperature is above {thresh}.

Notes

Let $PR_i$ be the mean daily precipitation of day $i$, then for a period $j$ starting at day $a$ and finishing on day $b$:

\[ \begin{align}\begin{aligned}PR_{ij} = \sum_{i=a}^{b} PR_i\\PRwet_{ij}\end{aligned}\end{align} \]

xclim.indicators.atmos._precip.max_1day_precipitation_amount(pr: Union[DataArray, str] = 'pr', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Highest 1-day precipitation amount for a period (frequency). (realm: atmos)

Resample the original daily total precipitation temperature series by taking the max over each period.

This indicator will check for missing values according to the method “from_context”. Based on indice max_1day_precipitation_amount().

Parameters

pr (str or DataArray) – Daily precipitation values. Default : ds.pr. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

rx1day (DataArray) – maximum 1-day total precipitation (lwe_thickness_of_precipitation_amount) [mm/day] cell_methods: time: maximum over days description: {freq} maximum 1-day total precipitation

Notes

Let $PR_i$ be the mean daily precipitation of day i, then for a period j:

\[PRx_{ij} = max(PR_{ij})\]

xclim.indicators.atmos._precip.max_n_day_precipitation_amount(pr: Union[DataArray, str] = 'pr', *, window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Highest precipitation amount cumulated over a n-day moving window. (realm: atmos)

Calculate the n-day rolling sum of the original daily total precipitation series and determine the maximum value over each period.

This indicator will check for missing values according to the method “from_context”. Based on indice max_n_day_precipitation_amount().

Parameters

pr (str or DataArray) – Daily precipitation values. Default : ds.pr. [Required units : [precipitation]]
window (number) – Window size in days. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

rx{window}day (DataArray) – maximum {window}-day total precipitation (lwe_thickness_of_precipitation_amount) [mm] cell_methods: time: maximum over days description: {freq} maximum {window}-day total precipitation.

xclim.indicators.atmos._precip.max_pr_intensity(pr: Union[DataArray, str] = 'pr', *, window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Highest precipitation intensity over a n-hour moving window. (realm: atmos)

Calculate the n-hour rolling average of the original hourly total precipitation series and determine the maximum value over each period.

This indicator will check for missing values according to the method “from_context”. Based on indice max_pr_intensity(). Keywords : IDF curves.

Parameters

pr (str or DataArray) – Hourly precipitation values. Default : ds.pr. [Required units : [precipitation]]
window (number) – Window size in hours. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

max_pr_intensity (DataArray) – Maximum precipitation intensity over {window}h duration (precipitation) [mm/h] cell_methods: time: max description: {freq} maximum precipitation intensity over rolling {window}h window.

xclim.indicators.atmos._precip.maximum_consecutive_dry_days(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1 mm/day', freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum number of consecutive dry days. (realm: atmos)

Return the maximum number of consecutive days within the period where precipitation is below a certain threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_dry_days().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Threshold precipitation on which to base evaluation. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cdd (DataArray) – Maximum consecutive dry days (Precip < {thresh}) (number_of_days_with_lwe_thickness_of_precipitation_amount_below_threshold) [days] cell_methods: time: sum over days description: {freq} maximum number of consecutive days with daily precipitation below {thresh}.

Notes

Let $\mathbf{p}=p_0, p_1, \ldots, p_n$ be a daily precipitation series and $thresh$ the threshold under which a day is considered dry. Then let $\mathbf{s}$ be the sorted vector of indices $i$ where $[p_i < thresh] \neq [p_{i+1} < thresh]$, that is, the days when the precipitation crosses the threshold. Then the maximum number of consecutive dry days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [p_{s_j} > thresh]\]

where $[P]$ is 1 if $P$ is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos._precip.maximum_consecutive_wet_days(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1 mm/day', freq: str = 'YS', ds: Dataset = None) → DataArray

Consecutive wet days. (realm: atmos)

Returns the maximum number of consecutive wet days.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_wet_days().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Threshold precipitation on which to base evaluation. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cwd (DataArray) – Maximum consecutive wet days (Precip >= {thresh}) (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days] cell_methods: time: sum over days description: {freq} maximum number of consecutive days with daily precipitation over {thresh}.

Notes

Let $\mathbf{x}=x_0, x_1, \ldots, x_n$ be a daily precipitation series and $\mathbf{s}$ be the sorted vector of indices $i$ where $[p_i > thresh] \neq [p_{i+1} > thresh]$, that is, the days when the precipitation crosses the wet day threshold. Then the maximum number of consecutive wet days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [x_{s_j} > 0^\circ C]\]

where $[P]$ is 1 if $P$ is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos._precip.precip_accumulation(pr: Union[DataArray, str] = 'pr', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Accumulated total precipitation (solid and liquid) (realm: atmos)

Resample the original daily mean precipitation flux and accumulate over each period. If a daily temperature is provided, the phase keyword can be used to sum precipitation of a given phase only. When the temperature is under the provided threshold, precipitation is assumed to be snow, and liquid rain otherwise. This indice is agnostic to the type of daily temperature (tas, tasmax or tasmin) given.

This indicator will check for missing values according to the method “from_context”. Based on indice precip_accumulation(). With injected parameters: tas=None, phase=None.

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Threshold of tas over which the precipication is assumed to be liquid rain. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

prcptot (DataArray) – Total precipitation (lwe_thickness_of_precipitation_amount) [mm] cell_methods: time: sum over days description: {freq} total precipitation

Notes

Let $PR_i$ be the mean daily precipitation of day $i$, then for a period $j$ starting at day $a$ and finishing on day $b$:

\[PR_{ij} = \sum_{i=a}^{b} PR_i\]

If tas and phase are given, the corresponding phase precipitation is estimated before computing the accumulation, using one of snowfall_approximation or rain_approximation with the binary method.

xclim.indicators.atmos._precip.rain_on_frozen_ground_days(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: str = '1 mm/d', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of rain on frozen ground events. (realm: atmos)

Number of days with rain above a threshold after a series of seven days below freezing temperature. Precipitation is assumed to be rain when the temperature is above 0℃.

This indicator will check for missing values according to the method “from_context”. Based on indice rain_on_frozen_ground_days().

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Precipitation threshold to consider a day as a rain event. Default : 1 mm/d. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

rain_frzgr (DataArray) – Number of rain on frozen ground days (number_of_days_with_lwe_thickness_of_precipitation_amount_above_threshold) [days] description: {freq} number of days with rain above {thresh} after a series of seven days with average daily temperature below 0℃. Precipitation is assumed to be rain when thedaily average temperature is above 0℃.

Notes

Let $PR_i$ be the mean daily precipitation and $TG_i$ be the mean daily temperature of day $i$. Then for a period $j$, rain on frozen grounds days are counted where:

\[PR_{i} > Threshold [mm]\]

and where

\[TG_{i} ≤ 0℃\]

is true for continuous periods where $i ≥ 7$

xclim.indicators.atmos._precip.rprctot(pr: Union[DataArray, str] = 'pr', prc: Union[DataArray, str] = 'prc', *, thresh: str = '1.0 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Proportion of accumulated precipitation arising from convective processes. (realm: atmos)

Return the proportion of total accumulated precipitation due to convection on days with total precipitation exceeding a specified threshold during the given period.

This indicator will check for missing values according to the method “from_context”. Based on indice rprctot().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
prc (str or DataArray) – Daily convective precipitation. Default : ds.prc. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1.0 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

rprctot (DataArray) – The proportion of the total precipitation accounted for by convective precipitation for each period. cell_methods: time: sum description: Proportion of accumulated precipitation arising from convective processes.

xclim.indicators.atmos._precip.solid_precip_accumulation(pr: Union[DataArray, str] = 'pr', tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Accumulated solid precipitation. (realm: atmos)

Resample the original daily mean precipitation flux and accumulate over each period. If a daily temperature is provided, the phase keyword can be used to sum precipitation of a given phase only. When the temperature is under the provided threshold, precipitation is assumed to be snow, and liquid rain otherwise. This indice is agnostic to the type of daily temperature (tas, tasmax or tasmin) given.

This indicator will check for missing values according to the method “from_context”. Based on indice precip_accumulation(). With injected parameters: phase=solid.

Parameters

pr (str or DataArray) – Mean daily precipitation flux. Default : ds.pr. [Required units : [precipitation]]
tas (str or DataArray) – Mean, maximum or minimum daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold of tas over which the precipication is assumed to be liquid rain. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

solidprcptot (DataArray) – Total solid precipitation (lwe_thickness_of_snowfall_amount) [mm] cell_methods: time: sum over days description: {freq} total solid precipitation, estimated as precipitation when temperature < {thresh}

Notes

Let $PR_i$ be the mean daily precipitation of day $i$, then for a period $j$ starting at day $a$ and finishing on day $b$:

\[PR_{ij} = \sum_{i=a}^{b} PR_i\]

If tas and phase are given, the corresponding phase precipitation is estimated before computing the accumulation, using one of snowfall_approximation or rain_approximation with the binary method.

xclim.indicators.atmos._precip.warm_and_dry_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

warm and dry days (realm: atmos)

Returns the total number of days where “warm” and “Dry” conditions coincide.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_and_dry_days().

Parameters

tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]
pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
tas_per (str or DataArray) – Third quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]
pr_per (str or DataArray) – First quartile of daily total precipitation computed by month. .. warning:: Before computing the percentiles, all the precipitation below 1mm must be filtered out ! Otherwise, the percentiles will include non-wet days. Default : ds.pr_per. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

warm_and_dry_days (DataArray) – warm and dry days [days] cell_methods: time: sum over days description: {freq} number of days where tas > {tas_per_thresh}th percentile and pr < {pr_per_thresh}th percentile

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written [warm_dry_days].

References

warm_dry_days: Beniston, M. (2009). Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100. Geophysical Research Letters, 36(7). https://doi.org/10.1029/2008GL037119

xclim.indicators.atmos._precip.warm_and_wet_days(tas: Union[DataArray, str] = 'tas', pr: Union[DataArray, str] = 'pr', tas_per: Union[DataArray, str] = 'tas_per', pr_per: Union[DataArray, str] = 'pr_per', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

warm and wet days (realm: atmos)

Returns the total number of days where “warm” and “wet” conditions coincide.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_and_wet_days().

Parameters

tas (str or DataArray) – Mean daily temperature values Default : ds.tas. [Required units : [temperature]]
pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
tas_per (str or DataArray) – Third quartile of daily mean temperature computed by month. Default : ds.tas_per. [Required units : [temperature]]
pr_per (str or DataArray) – Third quartile of daily total precipitation computed by month. .. warning:: Before computing the percentiles, all the precipitation below 1mm must be filtered out ! Otherwise, the percentiles will include non-wet days. Default : ds.pr_per. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

warm_and_wet_days (DataArray) – warm and wet days [days] cell_methods: time: sum over days description: {freq} number of days where tas > {tas_per_thresh}th percentile and pr > {pr_per_thresh}th percentile

Notes

Bootstrapping is not available for quartiles because it would make no significant difference to bootstrap percentiles so far from the extremes.

Formula to be written [warm_wet_days].

References

warm_wet_days: Beniston, M. (2009). Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100. Geophysical Research Letters, 36(7). https://doi.org/10.1029/2008GL037119

xclim.indicators.atmos._precip.wet_precip_accumulation(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Accumulated total precipitation (solid and liquid) during wet days (realm: atmos)

This indicator will check for missing values according to the method “from_context”. Based on indice prcptot().

Parameters

pr (str or DataArray) – Total precipitation flux [mm d-1], [mm week-1], [mm month-1] or similar. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Threshold over which precipitation starts being cumulated. Default : 1 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

wet_prcptot (DataArray) – Total precipitation (lwe_thickness_of_precipitation_amount) [mm] cell_methods: time: sum over days description: {freq} total precipitation over wet days, defined as days where precipitation exceeds {thresh}.

xclim.indicators.atmos._precip.wetdays(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1.0 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Wet days. (realm: atmos)

Return the total number of days during period with precipitation over threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice wetdays().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1.0 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

wetdays (DataArray) – Number of wet days (precip >= {thresh}) (number_of_days_with_lwe_thickness_of_precipitation_amount_at_or_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with daily precipitation over {thresh}.

xclim.indicators.atmos._precip.wetdays_prop(pr: Union[DataArray, str] = 'pr', *, thresh: str = '1.0 mm/day', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Proportion of wet days. (realm: atmos)

Return the proportion of days during period with precipitation over threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice wetdays_prop().

Parameters

pr (str or DataArray) – Daily precipitation. Default : ds.pr. [Required units : [precipitation]]
thresh (quantity (string with units)) – Precipitation value over which a day is considered wet. Default : 1.0 mm/day. [Required units : [precipitation]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

wetdays_prop (DataArray) – Proportion of wet days (precip >= {thresh}) [1] cell_methods: time: sum over days description: {freq} proportion of days with precipitation over {thresh}.

xclim.indicators.atmos._synoptic module

Synoptic indicator definitions.

xclim.indicators.atmos._synoptic.jetstream_metric_woollings(ua: Union[DataArray, str] = 'ua', *, ds: Dataset = None) → Tuple[DataArray, DataArray]

Strength and latitude of jetstream. (realm: atmos)

Identify latitude and strength of maximum smoothed zonal wind speed in the region from 15 to 75°N and -60 to 0°E, using the formula outlined in ([Woollings2010]).

Based on indice jetstream_metric_woollings().

Parameters

ua (str or DataArray) – Eastward wind component (u) at between 750 and 950 hPa. Default : ds.ua. [Required units : [speed]]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

jetlat (DataArray) – Latitude of maximum smoothed zonal wind speed [degrees_North] description: Daily latitude of maximum smoothed zonal wind speed
jetstr (DataArray) – Maximum strength of smoothed zonal wind speed [m s-1] description: Daily maximum strength of smoothed zonal wind speed

References

Woollings2010: Woollings, T., Hannachi, A., & Hoskins, B. (2010). Variability of the North Atlantic eddy‐driven jet stream. Quarterly Journal of the Royal Meteorological Society, 136(649), 856-868.

xclim.indicators.atmos._temperature module

Temperature indicator definitions.

xclim.indicators.atmos._temperature.biologically_effective_degree_days(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', lat: Union[DataArray, str] = 'lat', *, thresh_tasmin: str = '10 degC', low_dtr: str = '10 degC', high_dtr: str = '13 degC', max_daily_degree_days: str = '9 degC', start_date: DayOfYearStr = '04-01', end_date: DayOfYearStr = '11-01', freq: str = 'YS', ds: Dataset = None) → DataArray

Biologically effective growing degree days. (realm: atmos)

Growing-degree days with a base of 10°C and an upper limit of 19°C and adjusted for latitudes between 40°N and 50°N for April to October (Northern Hemisphere; October to April in Southern Hemisphere). A temperature range adjustment also promotes small and large swings in daily temperature range. Used as a heat-summation metric in viticulture agroclimatology.

This indicator will check for missing values according to the method “from_context”. Based on indice biologically_effective_degree_days(). With injected parameters: method=gladstones.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
lat (str or DataArray) – Latitude coordinate. Default : ds.lat. [Required units : []]
thresh_tasmin (quantity (string with units)) – The minimum temperature threshold. Default : 10 degC. [Required units : [temperature]]
low_dtr (quantity (string with units)) – The lower bound for daily temperature range adjustment (default: 10°C). Default : 10 degC. [Required units : [temperature]]
high_dtr (quantity (string with units)) – The higher bound for daily temperature range adjustment (default: 13°C). Default : 13 degC. [Required units : [temperature]]
max_daily_degree_days (quantity (string with units)) – The maximum amount of biologically effective degrees days that can be summed daily. Default : 9 degC. [Required units : [temperature]]
start_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = April, south = October). Default : 04-01.
end_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = October, south = April). This date is non-inclusive. Default : 11-01.
freq (offset alias (string)) – Resampling frequency (default: “YS”; For Southern Hemisphere, should be “AS-JUL”). Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

bedd (DataArray) – Biologically effective degree days computed with {method} formula (Summation of min((max((Tmin + Tmax)/2 - {thresh_tasmin}, 0) * k) + TR_adg, 9°C), for days between {start_date} and {end_date}). [K days] description: Heat-summation index for agroclimatic suitability estimation, developed specifically for viticulture. Considers daily Tmin and Tmax with a base of {thresh_tasmin} between 1 April and 31 October, with a maximum daily value for degree days (typically 9°C). It also integrates a modification coefficient for latitudes between 40°N and 50°N as well as swings in daily temperature range. comment: Original formula published in Gladstones, 1992.

Notes

The tasmax ceiling of 19°C is assumed to be the max temperature beyond which no further gains from daily temperature occur. Indice originally published in [Gladstones1992].

Let $TX_{i}$ and $TN_{i}$ be the daily maximum and minimum temperature at day $i$, $lat$ the latitude of the point of interest, $degdays_{max}$ the maximum amount of degrees that can be summed per day (typically, 9). Then the sum of daily biologically effective growing degree day (BEDD) units between 1 April and 31 October is:

\[BEDD_i = \sum_{i=\text{April 1}}^{\text{October 31}} min\left( \left( max\left( \frac{TX_i + TN_i)}{2} - 10, 0 \right) * k \right) + TR_{adj}, degdays_{max}\right)\]

\[\begin{split}TR_{adj} = f(TX_{i}, TN_{i}) = \begin{cases} 0.25(TX_{i} - TN_{i} - 13), & \text{if } (TX_{i} - TN_{i}) > 13 \\ 0, & \text{if } 10 < (TX_{i} - TN_{i}) < 13\\ 0.25(TX_{i} - TN_{i} - 10), & \text{if } (TX_{i} - TN_{i}) < 10 \\ \end{cases}\end{split}\]

\[k = f(lat) = 1 + \left(\frac{\left| lat \right|}{50} * 0.06, \text{if }40 < |lat| <50, \text{else } 0\right)\]

A second version of the BEDD (method=”icclim”) does not consider $TR_{adj}$ and $k$ and employs a different end date (30 September) ([ECAD]). The simplified formula is as follows:

\[BEDD_i = \sum_{i=\text{April 1}}^{\text{September 30}} min\left( max\left(\frac{TX_i + TN_i)}{2} - 10, 0\right), degdays_{max}\right)\]

References

Gladstones1992: Gladstones, J.S. (1992). Viticulture and environment: a study of the effects of environment on grapegrowing and wine qualities, with emphasis on present and future areas for growing winegrapes in Australia. Adelaide: Winetitles.
ECAD: Project team ECA&D, KNMI (2013). EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) - Algorithm Theoretical Basis Document (ATBD). (KNMI Project number: EPJ029135, v10.7). https://www.ecad.eu/documents/atbd.pdf

xclim.indicators.atmos._temperature.cold_spell_days(tas: Union[DataArray, str] = 'tas', *, thresh: str = '-10 degC', window: int = 5, freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Cold spell days. (realm: atmos)

The number of days that are part of cold spell events, defined as a sequence of consecutive days with mean daily temperature below a threshold in °C.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_spell_days().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature below which a cold spell begins. Default : -10 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperature below threshold to qualify as a cold spell. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cold_spell_days (DataArray) – Number of days part of a cold spell (cold_spell_days) [days] description: {freq} number of days that are part of a cold spell, defined as {window} or more consecutive days with mean daily temperature below {thresh}.

Notes

Let $T_i$ be the mean daily temperature on day $i$, the number of cold spell days during period $\phi$ is given by

\[\sum_{i \in \phi} \prod_{j=i}^{i+5} [T_j < thresh]\]

where $[P]$ is 1 if $P$ is true, and 0 if false.

xclim.indicators.atmos._temperature.cold_spell_duration_index(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, window: int = 6, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None) → DataArray

Cold spell duration index. (realm: atmos)

Number of days with at least window consecutive days where the daily minimum temperature is below the tasmin_per percentiles.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_spell_duration_index().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmin_per (str or DataArray) – nth percentile of daily minimum temperature with dayofyear coordinate. Default : ds.tasmin_per. [Required units : [temperature]]
window (number) – Minimum number of days with temperature below threshold to qualify as a cold spell. Default : 6.
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

csdi_{window} (DataArray) – Number of days part of a percentile-defined cold spell (cold_spell_duration_index) [days] description: {freq} number of days with at least {window} consecutive days where the daily minimum temperature is below the {tasmin_per_thresh}th percentile(s). A {tasmin_per_window} day(s) window, centred on each calendar day in the {tasmin_per_period} period, is used to compute the {tasmin_per_thresh}th percentile(s).

Notes

Let $TN_i$ be the minimum daily temperature for the day of the year $i$ and $TN10_i$ the 10th percentile of the minimum daily temperature over the 1961-1990 period for day of the year $i$, the cold spell duration index over period $\phi$ is defined as:

\[\sum_{i \in \phi} \prod_{j=i}^{i+6} \left[ TN_j < TN10_j \right]\]

where $[P]$ is 1 if $P$ is true, and 0 if false.

References

From the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI).

xclim.indicators.atmos._temperature.cold_spell_frequency(tas: Union[DataArray, str] = 'tas', *, thresh: str = '-10 degC', window: int = 5, freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Cold spell frequency. (realm: atmos)

The number of cold spell events, defined as a sequence of consecutive days with mean daily temperature below a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice cold_spell_frequency().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature below which a cold spell begins. Default : -10 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperature below threshold to qualify as a cold spell. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cold_spell_frequency (DataArray) – Number of cold spell events (cold_spell_frequency) description: {freq} number cold spell events, defined as {window} or more consecutive days with mean daily temperature below {thresh}.

xclim.indicators.atmos._temperature.consecutive_frost_days(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '0.0 degC', freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Maximum number of consecutive frost days (Tn < 0℃). (realm: atmos)

The maximum number of consecutive days within the period where the temperature is under a certain threshold (default: 0°C). WARNING: The default freq value is valid for the northern hemisphere.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_frost_days().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature. Default : 0.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

consecutive_frost_days (DataArray) – Maximum number of consecutive days with Tmin < {thresh} (spell_length_of_days_with_air_temperature_below_threshold) [days] cell_methods: time: maximum over days description: {freq} maximum number of consecutive days with minimum daily temperature below {thresh}.

Notes

Let $\mathbf{t}=t_0, t_1, \ldots, t_n$ be a daily minimum temperature series and $thresh$ the threshold below which a day is considered a frost day. Let $\mathbf{s}$ be the sorted vector of indices $i$ where $[t_i < thresh] \neq [t_{i+1} < thresh]$, that is, the days when the temperature crosses the threshold. Then the maximum number of consecutive frost free days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} > thresh]\]

where $[P]$ is 1 if $P$ is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos._temperature.cool_night_index(tasmin: Union[DataArray, str] = 'tasmin', lat: Union[DataArray, str] = 'lat', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Cool Night Index. (realm: atmos)

A night coolness variable which takes into account the mean minimum night temperatures during the month when ripening usually occurs beyond the ripening period.

This indicator will check for missing values according to the method “from_context”. Based on indice cool_night_index().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
lat (str or DataArray) – Latitude coordinate. Default : ds.lat. [Required units : []]
freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

cool_night_index (DataArray) – cool night index [degC] cell_methods: time: mean over days description: Mean minimum temperature for September (northern hemisphere) or March (southern hemisphere).

Notes

Given that this indice only examines September and March months, it is possible to send in DataArrays containing only these timesteps. Users should be aware that due to the missing values checks in wrapped Indicators, datasets that are missing several months will be flagged as invalid. This check can be ignored by setting the following context:

References

xclim.indicators.atmos._temperature.cooling_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: str = '18.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Cooling degree days. (realm: atmos)

Sum of degree days above the temperature threshold at which spaces are cooled.

This indicator will check for missing values according to the method “from_context”. Based on indice cooling_degree_days().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Temperature threshold above which air is cooled. Default : 18.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

cooling_degree_days (DataArray) – Cooling degree days (Tmean > {thresh}) (integral_of_air_temperature_excess_wrt_time) [K days] cell_methods: time: sum over days description: {freq} cooling degree days above {thresh}.

Notes

Let $x_i$ be the daily mean temperature at day $i$. Then the cooling degree days above temperature threshold $thresh$ over period $\phi$ is given by:

\[\sum_{i \in \phi} (x_{i}-{thresh} [x_i > thresh]\]

where $[P]$ is 1 if $P$ is true, and 0 if false.

xclim.indicators.atmos._temperature.daily_freezethaw_cycles(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '0 degC', thresh_tasmax: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Statistics of consecutive diurnal temperature swing events. (realm: atmos)

A diurnal swing of max and min temperature event is when Tmax > thresh_tasmax and Tmin <= thresh_tasmin. This indice finds all days that constitute these events and computes statistics over the length and frequency of these events.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: window=1, op=sum.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dlyfrzthw (DataArray) – daily freezethaw cycles [days] description: {freq} number of days with a diurnal freeze-thaw cycle : Tmax > {thresh_tasmax} and Tmin <= {thresh_tasmin}.

Notes

Let $TX_{i}$ be the maximum temperature at day $i$ and $TN_{i}$ be the daily minimum temperature at day $i$. Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos._temperature.daily_temperature_range(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of daily temperature range. (realm: atmos)

The mean difference between the daily maximum temperature and the daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range(). With injected parameters: op=mean.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dtr (DataArray) – Mean Diurnal Temperature Range (air_temperature) [K] cell_methods: time range within days time: mean over days description: {freq} mean diurnal temperature range.

Notes

For a default calculation using op=’mean’ :

Let $TX_{ij}$ and $TN_{ij}$ be the daily maximum and minimum temperature at day $i$ of period $j$. Then the mean diurnal temperature range in period $j$ is:

\[DTR_j = \frac{ \sum_{i=1}^I (TX_{ij} - TN_{ij}) }{I}\]

xclim.indicators.atmos._temperature.daily_temperature_range_variability(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean absolute day-to-day variation in daily temperature range. (realm: atmos)

Mean absolute day-to-day variation in daily temperature range.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range_variability().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dtrvar (DataArray) – Mean Diurnal Temperature Range Variability (air_temperature) [K] cell_methods: time range within days time: difference over days time: mean over days description: {freq} mean diurnal temparature range variability (defined as the average day-to-day variation in daily temperature range for the given time period)

Notes

Let $TX_{ij}$ and $TN_{ij}$ be the daily maximum and minimum temperature at day $i$ of period $j$. Then calculated is the absolute day-to-day differences in period $j$ is:

\[vDTR_j = \frac{ \sum_{i=2}^{I} |(TX_{ij}-TN_{ij})-(TX_{i-1,j}-TN_{i-1,j})| }{I}\]

xclim.indicators.atmos._temperature.degree_days_exceedance_date(tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', sum_thresh: str = '25 K days', op: str = '>', after_date: DayOfYearStr = None, freq: str = 'YS', ds: Dataset = None) → DataArray

Degree days exceedance date. (realm: atmos)

Day of year when the sum of degree days exceeds a threshold. Degree days are computed above or below a given temperature threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice degree_days_exceedance_date().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base degree days evaluation. Default : 0 degC. [Required units : [temperature]]
sum_thresh (quantity (string with units)) – Threshold of the degree days sum. Default : 25 K days. [Required units : K days]
op ({‘<=’, ‘lt’, ‘<’, ‘ge’, ‘>=’, ‘gt’, ‘le’, ‘>’}) – If equivalent to ‘>’, degree days are computed as tas - thresh and if equivalent to ‘<’, they are computed as thresh - tas. Default : >.
after_date (date (string, MM-DD)) – Date at which to start the cumulative sum. In “mm-dd” format, defaults to the start of the sampling period. Default : None.
freq (offset alias (string)) – Resampling frequency. If after_date is given, freq should be annual. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

degree_days_exceedance_date (DataArray) – Day of year when cumulative degree days exceed {sum_thresh}. (day_of_year) description: Day of year when the integral of degree days (tmean {op} {thresh}) exceeds {sum_thresh}, the cumulative sum starts on {after_date}.

Notes

Let $TG_{ij}$ be the daily mean temperature at day $i$ of period $j$, $T$ is the reference threshold and $ST$ is the sum threshold. Then, starting at day :math:i_0:, the degree days exceedance date is the first day $k$ such that

\[\begin{split}\begin{cases} ST < \sum_{i=i_0}^{k} \max(TG_{ij} - T, 0) & \text{if $op$ is '>'} \\ ST < \sum_{i=i_0}^{k} \max(T - TG_{ij}, 0) & \text{if $op$ is '<'} \end{cases}\end{split}\]

The resulting $k$ is expressed as a day of year.

Cumulated degree days have numerous applications including plant and insect phenology. See https://en.wikipedia.org/wiki/Growing_degree-day for examples.

xclim.indicators.atmos._temperature.extreme_temperature_range(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Extreme intra-period temperature range. (realm: atmos)

The maximum of max temperature (TXx) minus the minimum of min temperature (TNn) for the given time period.

This indicator will check for missing values according to the method “from_context”. Based on indice extreme_temperature_range().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

etr (DataArray) – Intra-period Extreme Temperature Range (air_temperature) [K] description: {freq} range between the maximum of daily max temperature (tx_max) and the minimum of daily min temperature (tn_min)

Notes

Let $TX_{ij}$ and $TN_{ij}$ be the daily maximum and minimum temperature at day $i$ of period $j$. Then the extreme temperature range in period $j$ is:

\[ETR_j = max(TX_{ij}) - min(TN_{ij})\]

xclim.indicators.atmos._temperature.fire_season(tas: Union[DataArray, str] = 'tas', snd: Optional[Union[DataArray, str]] = None, *, method: str = 'WF93', freq: str | None = None, temp_start_thresh: str = '12 degC', temp_end_thresh: str = '5 degC', temp_condition_days: int = 3, snow_condition_days: int = 3, snow_thresh: str = '0.01 m', ds: Dataset = None) → DataArray

Fire season mask. (realm: atmos)

Binary mask of the active fire season, defined by conditions on consecutive daily temperatures and, optionally, snow depths.

Based on indice fire_season().

Parameters

tas (str or DataArray) – Daily surface temperature, cffdrs recommends using maximum daily temperature. Default : ds.tas. [Required units : [temperature]]
snd (str or DataArray, optional) – Snow depth, used with method == ‘LA08’. [Required units : [length]]
method ({‘LA08’, ‘GFWED’, ‘WF93’}) – Which method to use. “LA08” and “GFWED” need the snow depth. Default : WF93.
freq (offset alias (string)) – If given only the longest fire season for each period defined by this frequency, Every “seasons” are returned if None, including the short shoulder seasons. Default : None.
temp_start_thresh (quantity (string with units)) – Minimal temperature needed to start the season. Default : 12 degC. [Required units : [temperature]]
temp_end_thresh (quantity (string with units)) – Maximal temperature needed to end the season. Default : 5 degC. [Required units : [temperature]]
temp_condition_days (number) – Number of days with temperature above or below the thresholds to trigger a start or an end of the fire season. Default : 3.
snow_condition_days (number) – Parameters for the fire season determination. See fire_season(). Temperature is in degC, snow in m. The snow_thresh parameters is also used when dry_start is set to “GFWED”. Default : 3.
snow_thresh (quantity (string with units)) – Minimal snow depth level to end a fire season, only used with method “LA08”. Default : 0.01 m. [Required units : [length]]
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

fire_season (DataArray) – Fire season mask description: Fire season mask, computed with method {method}.

References

[Wotton&Flannigan1993]_

[Lawson&Armitage2008]_

xclim.indicators.atmos._temperature.first_day_above(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '0 degC', after_date: DayOfYearStr = '01-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures superior to a threshold temperature. (realm: atmos)

Returns first day of period where a temperature is superior to a threshold over a given number of days, limited to a starting calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_above().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
after_date (date (string, MM-DD)) – Date of the year after which to look for the first event. Should have the format ‘%m-%d’. Default : 01-01.
window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_above (DataArray) – First day of year with temperature above {thresh} (day_of_year) description: First day of year with temperature above {thresh} for at least {window} days.

xclim.indicators.atmos._temperature.first_day_below(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '0 degC', after_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

First day of temperatures inferior to a threshold temperature. (realm: atmos)

Returns first day of period where a temperature is inferior to a threshold over a given number of days, limited to a starting calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice first_day_below().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
after_date (date (string, MM-DD)) – Date of the year after which to look for the first frost event. Should have the format ‘%m-%d’. Default : 07-01.
window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

first_day_below (DataArray) – First day of year with temperature below {thresh} (day_of_year) description: First day of year with temperature below {thresh} for at least {window} days.

xclim.indicators.atmos._temperature.freezethaw_spell_frequency(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '0 degC', thresh_tasmax: str = '0 degC', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Frequency of freeze-thaw spells (realm: atmos)

A diurnal swing of max and min temperature event is when Tmax > thresh_tasmax and Tmin <= thresh_tasmin. This indice finds all days that constitute these events and computes statistics over the length and frequency of these events.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: op=count.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]
window (number) – The minimal length of spells to be included in the statistics. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freezethaw_spell_frequency (DataArray) – {freq} number of freeze-thaw spells. [days] description: {freq} number of freeze-thaw spells: Tmax > {thresh_tasmax} and Tmin <= {thresh_tasmin} for at least {window} consecutive day(s).

Notes

Let $TX_{i}$ be the maximum temperature at day $i$ and $TN_{i}$ be the daily minimum temperature at day $i$. Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos._temperature.freezethaw_spell_max_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '0 degC', thresh_tasmax: str = '0 degC', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Maximal length of freeze-thaw spells. (realm: atmos)

A diurnal swing of max and min temperature event is when Tmax > thresh_tasmax and Tmin <= thresh_tasmin. This indice finds all days that constitute these events and computes statistics over the length and frequency of these events.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: op=max.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]
window (number) – The minimal length of spells to be included in the statistics. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freezethaw_spell_max_length (DataArray) – {freq} maximal length of freeze-thaw spells. [days] description: {freq} maximal length of freeze-thaw spells: Tmax > {thresh_tasmax} and Tmin <= {thresh_tasmin} for at least {window} consecutive day(s).

Notes

Let $TX_{i}$ be the maximum temperature at day $i$ and $TN_{i}$ be the daily minimum temperature at day $i$. Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos._temperature.freezethaw_spell_mean_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '0 degC', thresh_tasmax: str = '0 degC', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Averge length of freeze-thaw spells. (realm: atmos)

A diurnal swing of max and min temperature event is when Tmax > thresh_tasmax and Tmin <= thresh_tasmin. This indice finds all days that constitute these events and computes statistics over the length and frequency of these events.

This indicator will check for missing values according to the method “from_context”. Based on indice multiday_temperature_swing(). With injected parameters: op=mean.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The temperature threshold needed to trigger a freeze event. Default : 0 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The temperature threshold needed to trigger a thaw event. Default : 0 degC. [Required units : [temperature]]
window (number) – The minimal length of spells to be included in the statistics. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freezethaw_spell_mean_length (DataArray) – {freq} average length of freeze-thaw spells. [days] description: {freq} average length of freeze-thaw spells: Tmax > {thresh_tasmax} and Tmin <= {thresh_tasmin} for at least {window} consecutive day(s).

Notes

Let $TX_{i}$ be the maximum temperature at day $i$ and $TN_{i}$ be the daily minimum temperature at day $i$. Then freeze thaw spells during a given period are consecutive days where:

\[TX_{i} > 0℃ \land TN_{i} < 0℃\]

This indice returns a given statistic of the found lengths, optionally dropping those shorter than the window argument. For example, window=1 and op=’sum’ returns the same value as daily_freezethaw_cycles().

xclim.indicators.atmos._temperature.freezing_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Heating degree days. (realm: atmos)

Sum of degree days below the temperature threshold at which spaces are heated.

This indicator will check for missing values according to the method “from_context”. Based on indice heating_degree_days().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

freezing_degree_days (DataArray) – Freezing degree days (Tmean < {thresh}) (integral_of_air_temperature_deficit_wrt_time) [K days] cell_methods: time: sum over days description: {freq} freezing degree days below {thresh}.

Notes

This index intentionally differs from its ECA&D equivalent: HD17. In HD17, values below zero are not clipped before the sum. The present definition should provide a better representation of the energy demand for heating buildings to the given threshold.

Let $TG_{ij}$ be the daily mean temperature at day $i$ of period $j$. Then the heating degree days are:

\[HD17_j = \sum_{i=1}^{I} (17℃ - TG_{ij}) | TG_{ij} < 17℃)\]

xclim.indicators.atmos._temperature.freshet_start(tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

First day consistently exceeding threshold temperature. (realm: atmos)

Returns first day of period where a temperature threshold is exceeded over a given number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice freshet_start().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

freshet_start (DataArray) – Day of year of spring freshet start (day_of_year) description: Day of year of spring freshet start, defined as the first day a temperature threshold of {thresh} is exceeded for at least {window} days.

Notes

Let $x_i$ be the daily mean temperature at day of the year $i$ for values of $i$ going from 1 to 365 or 366. The start date of the freshet is given by the smallest index $i$ for which

\[\prod_{j=i}^{i+w} [x_j > thresh]\]

is true, where $w$ is the number of days the temperature threshold should be exceeded, and $[P]$ is 1 if $P$ is true, and 0 if false.

xclim.indicators.atmos._temperature.frost_days(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Frost days index. (realm: atmos)

Number of days where daily minimum temperatures are below a threshold temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_days().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Freezing temperature. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

frost_days (DataArray) – Number of frost days (Tmin < {thresh}) (days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with minimum daily temperature below {thresh}.

Notes

Let $TN_{ij}$ be the daily minimum temperature at day $i$ of period $j$ and :math`TT` the threshold. Then counted is the number of days where:

\[TN_{ij} < TT\]

xclim.indicators.atmos._temperature.frost_free_season_end(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '0 degC', mid_date: DayOfYearStr = '07-01', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

End of the frost free season. (realm: atmos)

Day of the year of the start of a sequence of days with minimum temperatures consistently below a threshold, after a period with minimum temperatures consistently above the same threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_free_season_end().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
mid_date (date (string, MM-DD)) – Date of the year after which to look for the end of the season. Should have the format ‘%m-%d’. Default : 07-01.
window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_free_season_end (DataArray) – Day of year of frost free season end (day_of_year) description: Day of year of end of frost free season, defined as the first day minimum temperatures below a threshold of {thresh}, after a run of days above this threshold, for at least {window} days.

xclim.indicators.atmos._temperature.frost_free_season_length(tasmin: Union[DataArray, str] = 'tasmin', *, window: int = 5, mid_date: DayOfYearStr | None = '07-01', thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None) → DataArray

Frost free season length. (realm: atmos)

The number of days between the first occurrence of at least N (def: 5) consecutive days with minimum daily temperature above a threshold (default: 0℃) and the first occurrence of at least N (def 5) consecutive days with minimum daily temperature below the same threshold A mid date can be given to limit the earliest day the end of season can take. WARNING: The default freq and mid_date values are valid for the northern hemisphere.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_free_season_length().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
window (number) – Minimum number of days with temperature above threshold to mark the beginning and end of frost free season. Default : 5.
mid_date (date (string, MM-DD)) – Date the must be included in the season. It is the earliest the end of the season can be. If None, there is no limit. Default : 07-01.
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_free_season_length (DataArray) – Length of the frost free season (days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days between the first occurrence of at least {window} consecutive days with minimum daily temperature above or at the freezing point and the first occurrence of at least {window} consecutive days with minimum daily temperature below freezing after {mid_date}.

Notes

Let $TN_{ij}$ be the minimum temperature at day $i$ of period $j$. Then counted is the number of days between the first occurrence of at least N consecutive days with:

\[TN_{ij} >= 0 ℃\]

and the first subsequent occurrence of at least N consecutive days with:

\[TN_{ij} < 0 ℃\]

xclim.indicators.atmos._temperature.frost_free_season_start(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '0 degC', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Start of the frost free season. (realm: atmos)

Day of the year of the start of a sequence of days with minimum temperatures consistently above or equal to a threshold, after a period with minimum temperatures consistently above the same threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_free_season_start().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_free_season_start (DataArray) – Day of year of frost free season start (day_of_year) description: Day of year of beginning of frost free season, defined as the first day a minimum temperature threshold of {thresh} is equal or exceeded for at least {window} days.

Notes

Let $x_i$ be the daily mean temperature at day of the year $i$ for values of $i$ going from 1 to 365 or 366. The start date of the start of growing season is given by the smallest index $i$ for which:

\[\prod_{j=i}^{i+w} [x_j >= thresh]\]

is true, where $w$ is the number of days the temperature threshold should be met or exceeded, and $[P]$ is 1 if $P$ is true, and 0 if false.

xclim.indicators.atmos._temperature.frost_season_length(tasmin: Union[DataArray, str] = 'tasmin', *, window: int = 5, mid_date: DayOfYearStr | None = '01-01', freq: str = 'AS-JUL', ds: Dataset = None) → DataArray

Frost season length. (realm: atmos)

The number of days between the first occurrence of at least N (def: 5) consecutive days with minimum daily temperature under a threshold (default: 0℃) and the first occurrence of at least N (def 5) consecutive days with minimum daily temperature above the same threshold A mid date can be given to limit the earliest day the end of season can take. WARNING: The default freq and mid_date values are valid for the northern hemisphere.

This indicator will check for missing values according to the method “from_context”. Based on indice frost_season_length(). With injected parameters: thresh=0 degC.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
window (number) – Minimum number of days with temperature below threshold to mark the beginning and end of frost season. Default : 5.
mid_date (date (string, MM-DD)) – Date the must be included in the season. It is the earliest the end of the season can be. If None, there is no limit. Default : 01-01.
freq (offset alias (string)) – Resampling frequency. Default : AS-JUL.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

frost_season_length (DataArray) – Length of the frost season (days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days between the first occurrence of at least {window} consecutive days with minimum daily temperature below freezing and the first occurrence of at least {window} consecutive days with minimuim daily temperature above freezing after {mid_date}.

Notes

Let $TN_{ij}$ be the minimum temperature at day $i$ of period $j$. Then counted is the number of days between the first occurrence of at least N consecutive days with:

\[TN_{ij} > 0 ℃\]

and the first subsequent occurrence of at least N consecutive days with:

\[TN_{ij} < 0 ℃\]

xclim.indicators.atmos._temperature.growing_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: str = '4.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Growing degree-days over threshold temperature value. (realm: atmos)

The sum of degree-days over the threshold temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_degree_days().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 4.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

growing_degree_days (DataArray) – Growing degree days above {thresh} (integral_of_air_temperature_excess_wrt_time) [K days] cell_methods: time: sum over days description: {freq} growing degree days above {thresh}.

Notes

Let $TG_{ij}$ be the daily mean temperature at day $i$ of period $j$. Then the growing degree days are:

\[GD4_j = \sum_{i=1}^I (TG_{ij}-{4} | TG_{ij} > {4}℃)\]

xclim.indicators.atmos._temperature.growing_season_end(tas: Union[DataArray, str] = 'tas', *, thresh: str = '5.0 degC', mid_date: DayOfYearStr = '07-01', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

End of the growing season. (realm: atmos)

Day of the year of the start of a sequence of days with mean temperatures consistently below a threshold, after a period with mean temperatures consistently above the same threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_season_end().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 5.0 degC. [Required units : [temperature]]
mid_date (date (string, MM-DD)) – Date of the year after which to look for the end of the season. Should have the format ‘%m-%d’. Default : 07-01.
window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

growing_season_end (DataArray) – Day of year of growing season end (day_of_year) description: Day of year of end of growing season, defined as the first day of consistent inferior threshold temperature of {thresh} after a run of {window} days superior to threshold temperature.

xclim.indicators.atmos._temperature.growing_season_length(tas: Union[DataArray, str] = 'tas', *, thresh: str = '5.0 degC', window: int = 6, mid_date: DayOfYearStr = '07-01', freq: str = 'YS', ds: Dataset = None) → DataArray

Growing season length. (realm: atmos)

The number of days between the first occurrence of at least six consecutive days with mean daily temperature over a threshold (default: 5℃) and the first occurrence of at least six consecutive days with mean daily temperature below the same threshold after a certain date. (Usually July 1st in the northern emisphere and January 1st in the southern hemisphere.)

This indicator will check for missing values according to the method “from_context”. Based on indice growing_season_length().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 5.0 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperature above threshold to mark the beginning and end of growing season. Default : 6.
mid_date (date (string, MM-DD)) – Date of the year after which to look for the end of the season. Should have the format ‘%m-%d’. Default : 07-01.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

growing_season_length (DataArray) – ETCCDI Growing Season Length (Tmean > {thresh}) (growing_season_length) [days] description: {freq} number of days between the first occurrence of at least {window} consecutive days with mean daily temperature over {thresh} and the first occurrence of at least {window} consecutive days with mean daily temperature below {thresh} after {mid_date}.

Notes

Let $TG_{ij}$ be the mean temperature at day $i$ of period $j$. Then counted is the number of days between the first occurrence of at least 6 consecutive days with:

\[TG_{ij} > 5 ℃\]

and the first occurrence after 1 July of at least 6 consecutive days with:

\[TG_{ij} < 5 ℃\]

xclim.indicators.atmos._temperature.growing_season_start(tas: Union[DataArray, str] = 'tas', *, thresh: str = '5.0 degC', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Start of the growing season. (realm: atmos)

Day of the year of the start of a sequence of days with mean temperatures consistently above or equal to a threshold, after a period with mean temperatures consistently above the same threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_season_start().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 5.0 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperature above threshold needed for evaluation. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

growing_season_start (DataArray) – Day of year of growing season start (day_of_year) description: Day of year of start of growing season, defined as the first day of consistent superior or equal to threshold temperature of {thresh} after a run of {window} days inferior to threshold temperature.

Notes

Let $x_i$ be the daily mean temperature at day of the year $i$ for values of $i$ going from 1 to 365 or 366. The start date of the start of growing season is given by the smallest index $i$ for which:

\[\prod_{j=i}^{i+w} [x_j >= thresh]\]

is true, where $w$ is the number of days the temperature threshold should be met or exceeded, and $[P]$ is 1 if $P$ is true, and 0 if false.

xclim.indicators.atmos._temperature.heat_wave_frequency(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '22.0 degC', thresh_tasmax: str = '30 degC', window: int = 3, freq: str = 'YS', ds: Dataset = None) → DataArray

Heat wave frequency. (realm: atmos)

Number of heat waves over a given period. A heat wave is defined as an event where the minimum and maximum daily temperature both exceeds specific thresholds over a minimum number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_frequency(). Keywords : health,.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The minimum temperature threshold needed to trigger a heatwave event. Default : 22.0 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_frequency (DataArray) – Number of heat wave events (Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax} for >= {window} days) (heat_wave_events) description: {freq} number of heat wave events over a given period. An event occurs when the minimum and maximum daily temperature both exceeds specific thresholds : (Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax}) over a minimum number of days ({window}).

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities ([casati2013]).

In Robinson (2001; [robinson2001]), the parameters would be thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

casati2013: Casati, B., Yagouti, A., & Chaumont, D. (2013). Regional Climate Projections of Extreme Heat Events in Nine Pilot Canadian Communities for Public Health Planning. Journal of Applied Meteorology and Climatology, 52(12), 2669‑2698. https://doi.org/10.1175/JAMC-D-12-0341.1
robinson2001: Robinson, P. J. (2001). On the Definition of a Heat Wave. Journal of Applied Meteorology and Climatology, 40(4), 762‑775. https://doi.org/10.1175/1520-0450%282001%29040<0762:OTDOAH>2.0.CO;2

xclim.indicators.atmos._temperature.heat_wave_index(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: str = '25.0 degC', window: int = 5, freq: str = 'YS', ds: Dataset = None) → DataArray

Heat wave index. (realm: atmos)

Number of days that are part of a heatwave, defined as five or more consecutive days over 25℃.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_index().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to designate a heatwave. Default : 25.0 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperature above threshold to qualify as a heatwave. Default : 5.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_index (DataArray) – Number of days that are part of a heatwave (heat_wave_index) [days] description: {freq} number of days that are part of a heatwave, defined as five or more consecutive days over {thresh}.

xclim.indicators.atmos._temperature.heat_wave_max_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '22.0 degC', thresh_tasmax: str = '30 degC', window: int = 3, freq: str = 'YS', ds: Dataset = None) → DataArray

Heat wave max length. (realm: atmos)

Maximum length of heat waves over a given period. A heat wave is defined as an event where the minimum and maximum daily temperature both exceeds specific thresholds over a minimum number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_max_length(). Keywords : health,.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The minimum temperature threshold needed to trigger a heatwave event. Default : 22.0 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_max_length (DataArray) – Maximum length of heat wave events (Tmin > {thresh_tasmin}and Tmax > {thresh_tasmax} for >= {window} days) (spell_length_of_days_with_air_temperature_above_threshold) [days] description: {freq} maximum length of heat wave events occurring in a given period. An event occurs when the minimum and maximum daily temperature both exceeds specific thresholds (Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax}) over a minimum number of days ({window}).

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities ([casati2013]).

In Robinson (2001; [robinson2001]), the parameters would be thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

casati2013: Casati, B., Yagouti, A., & Chaumont, D. (2013). Regional Climate Projections of Extreme Heat Events in Nine Pilot Canadian Communities for Public Health Planning. Journal of Applied Meteorology and Climatology, 52(12), 2669‑2698. https://doi.org/10.1175/JAMC-D-12-0341.1
robinson2001: Robinson, P. J. (2001). On the Definition of a Heat Wave. Journal of Applied Meteorology and Climatology, 40(4), 762‑775. https://doi.org/10.1175/1520-0450%282001%29040<0762:OTDOAH>2.0.CO;2

xclim.indicators.atmos._temperature.heat_wave_total_length(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '22.0 degC', thresh_tasmax: str = '30 degC', window: int = 3, freq: str = 'YS', ds: Dataset = None) → DataArray

Heat wave total length. (realm: atmos)

Total length of heat waves over a given period. A heat wave is defined as an event where the minimum and maximum daily temperature both exceeds specific thresholds over a minimum number of days. This the sum of all days in such events.

This indicator will check for missing values according to the method “from_context”. Based on indice heat_wave_total_length(). Keywords : health,.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – The minimum temperature threshold needed to trigger a heatwave event. Default : 22.0 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

heat_wave_total_length (DataArray) – Total length of heat wave events (Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax} for >= {window} days) (spell_length_of_days_with_air_temperature_above_threshold) [days] description: {freq} total length of heat wave events occurring in a given period. An event occurs when the minimum and maximum daily temperature both exceeds specific thresholds (Tmin > {thresh_tasmin} and Tmax > {thresh_tasmax}) over a minimum number of days ({window}).

Notes

See notes and references of heat_wave_max_length

xclim.indicators.atmos._temperature.heating_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: str = '17.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Heating degree days. (realm: atmos)

Sum of degree days below the temperature threshold at which spaces are heated.

This indicator will check for missing values according to the method “from_context”. Based on indice heating_degree_days().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 17.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

heating_degree_days (DataArray) – Heating degree days (Tmean < {thresh}) (integral_of_air_temperature_deficit_wrt_time) [K days] cell_methods: time: sum over days description: {freq} heating degree days below {thresh}.

Notes

This index intentionally differs from its ECA&D equivalent: HD17. In HD17, values below zero are not clipped before the sum. The present definition should provide a better representation of the energy demand for heating buildings to the given threshold.

Let $TG_{ij}$ be the daily mean temperature at day $i$ of period $j$. Then the heating degree days are:

\[HD17_j = \sum_{i=1}^{I} (17℃ - TG_{ij}) | TG_{ij} < 17℃)\]

xclim.indicators.atmos._temperature.hot_spell_frequency(tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmax: str = '30 degC', window: int = 3, freq: str = 'YS', ds: Dataset = None) → DataArray

Hot spell frequency. (realm: atmos)

Number of hot spells over a given period. A hot spell is defined as an event where the maximum daily temperature exceeds a specific threshold over a minimum number of days.

This indicator will check for missing values according to the method “from_context”. Based on indice hot_spell_frequency(). Keywords : health,.

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 3.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hot_spell_frequency (DataArray) – Number of hot spell events (Tmax > {thresh_tasmax} for >= {window} days) (hot_spell_events) description: {freq} number of hot spell events over a given period. An event occurs when the maximum daily temperature exceeds a specific threshold: (Tmax > {thresh_tasmax}) over a minimum number of days ({window}).

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities (Casati et al., 2013).

In Robinson (2001), the parameters would be thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

Casati, B., A. Yagouti, and D. Chaumont, 2013: Regional Climate Projections of Extreme Heat Events in Nine Pilot Canadian Communities for Public Health Planning. J. Appl. Meteor. Climatol., 52, 2669–2698, https://doi.org/10.1175/JAMC-D-12-0341.1

Robinson, P.J., 2001: On the Definition of a Heat Wave. J. Appl. Meteor., 40, 762–775, https://doi.org/10.1175/1520-0450%282001%29040<0762:OTDOAH>2.0.CO;2

xclim.indicators.atmos._temperature.hot_spell_max_length(tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmax: str = '30 degC', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Longest hot spell. (realm: atmos)

Longest spell of high temperatures over a given period.

This indicator will check for missing values according to the method “from_context”. Based on indice hot_spell_max_length(). Keywords : health,.

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – The maximum temperature threshold needed to trigger a heatwave event. Default : 30 degC. [Required units : [temperature]]
window (number) – Minimum number of days with temperatures above thresholds to qualify as a heatwave. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hot_spell_max_length (DataArray) – Maximum length of hot spell events (Tmax > {thresh_tasmax} for >= {window} days) (spell_length_of_days_with_air_temperature_above_threshold) [days] description: {freq} maximum length of hot spell events occurring in a given period. An event occurs when the maximum daily temperature exceeds a specific threshold: (Tmax > {thresh_tasmax}) over a minimum number of days ({window}).

Notes

The thresholds of 22° and 25°C for night temperatures and 30° and 35°C for day temperatures were selected by Health Canada professionals, following a temperature–mortality analysis. These absolute temperature thresholds characterize the occurrence of hot weather events that can result in adverse health outcomes for Canadian communities (Casati et al., 2013).

In Robinson (2001), the parameters would be thresh_tasmin=27.22, thresh_tasmax=39.44, window=2 (81F, 103F).

References

Casati, B., A. Yagouti, and D. Chaumont, 2013: Regional Climate Projections of Extreme Heat Events in Nine Pilot Canadian Communities for Public Health Planning. J. Appl. Meteor. Climatol., 52, 2669–2698, https://doi.org/10.1175/JAMC-D-12-0341.1

Robinson, P.J., 2001: On the Definition of a Heat Wave. J. Appl. Meteor., 40, 762–775, https://doi.org/10.1175/1520-0450%282001%29040<0762:OTDOAH>2.0.CO;2

xclim.indicators.atmos._temperature.huglin_index(tas: Union[DataArray, str] = 'tas', tasmax: Union[DataArray, str] = 'tasmax', lat: Union[DataArray, str] = 'lat', *, thresh: str = '10 degC', start_date: DayOfYearStr = '04-01', end_date: DayOfYearStr = '10-01', freq: str = 'YS', ds: Dataset = None) → DataArray

Huglin Heliothermal Index. (realm: atmos)

Growing-degree days with a base of 10°C and adjusted for latitudes between 40°N and 50°N for April to September (Northern Hemisphere; October to March in Southern Hemisphere). Originally proposed in [Huglin1978]. Used as a heat-summation metric in viticulture agroclimatology.

This indicator will check for missing values according to the method “from_context”. Based on indice huglin_index(). With injected parameters: method=jones.

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
lat (str or DataArray) – Latitude coordinate. Default : ds.lat. [Required units : []]
thresh (quantity (string with units)) – The temperature threshold. Default : 10 degC. [Required units : [temperature]]
start_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = April, south = October). Default : 04-01.
end_date (date (string, MM-DD)) – The hemisphere-based start date to consider (north = October, south = April). This date is non-inclusive. Default : 10-01.
freq (offset alias (string)) – Resampling frequency (default: “YS”; For Southern Hemisphere, should be “AS-JUL”). Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

hi (DataArray) – Huglin heliothermal index (Summation of ((Tmin + Tmax)/2 - {thresh}) * Latitude-based day-lengthcoefficient (k), for days between {start_date} and {end_date}). description: Heat-summation index for agroclimatic suitability estimation, developed specifically for viticulture. Considers daily Tmin and Tmax with a base of {thresh}, typically between 1 April and 30 September. Integrates a day-length coefficient calculation for higher latitudes. comment: Metric originally published in Huglin (1978). Day-length coefficient based on Hall & Jones (2010)

Notes

Let $TX_{i}$ and $TG_{i}$ be the daily maximum and mean temperature at day $i$ and $T_{thresh}$ the base threshold needed for heat summation (typically, 10 degC). A day-length multiplication, $k$, based on latitude, $lat$, is also considered. Then the Huglin heliothermal index for dates between 1 April and 30 September is:

\[HI = \sum_{i=\text{April 1}}^{\text{September 30}} \left( \frac{TX_i + TG_i)}{2} - T_{thresh} \right) * k\]

For the smoothed method, the day-length multiplication factor, $k$, is calculated as follows:

\[\begin{split}k = f(lat) = \begin{cases} 1, & \text{if } |lat| <= 40 \\ 1 + ((abs(lat) - 40) / 10) * 0.06, & \text{if } 40 < |lat| <= 50 \\ NaN, & \text{if } |lat| > 50 \\ \end{cases}\end{split}\]

For compatibility with ICCLIM, end_date should be set to 11-01, method should be set to icclim. The day-length multiplication factor, $k$, is calculated as follows:

\[\begin{split}k = f(lat) = \begin{cases} 1.0, & \text{if } |lat| <= 40 \\ 1.02, & \text{if } 40 < |lat| <= 42 \\ 1.03, & \text{if } 42 < |lat| <= 44 \\ 1.04, & \text{if } 44 < |lat| <= 46 \\ 1.05, & \text{if } 46 < |lat| <= 48 \\ 1.06, & \text{if } 48 < |lat| <= 50 \\ NaN, & \text{if } |lat| > 50 \\ \end{cases}\end{split}\]

A more robust day-length calculation based on latitude, calendar, day-of-year, and obliquity is available with method=”jones”. See: xclim.indices.generic.day_lengths() or [Hall&Jones2010]_ for more information.

References

Huglin1978: Huglin, P. (1978). Nouveau mode d’évaluation des possibilités héliothermiques d’un milieu viticole. Dans Symposium International sur l’Écologie de la Vigne (p. 89‑98). Ministère de l’Agriculture et de l’Industrie Alimentaire.

xclim.indicators.atmos._temperature.ice_days(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of ice/freezing days. (realm: atmos)

Number of days where daily maximum temperatures are below a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice ice_days().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh (quantity (string with units)) – Freezing temperature. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

ice_days (DataArray) – Number of ice days (Tmax < {thresh}) (days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with maximum daily temperature below {thresh}.

Notes

Let $TX_{ij}$ be the daily maximum temperature at day $i$ of period $j$, and :math`TT` the threshold. Then counted is the number of days where:

\[TX_{ij} < TT\]

xclim.indicators.atmos._temperature.last_spring_frost(tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', before_date: DayOfYearStr = '07-01', window: int = 1, freq: str = 'YS', ds: Dataset = None) → DataArray

Last day of temperatures inferior to a threshold temperature. (realm: atmos)

Returns last day of period where a temperature is inferior to a threshold over a given number of days and limited to a final calendar date.

This indicator will check for missing values according to the method “from_context”. Based on indice last_spring_frost().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
before_date (date (string, MM-DD)) – Date of the year before which to look for the final frost event. Should have the format ‘%m-%d’. Default : 07-01.
window (number) – Minimum number of days with temperature below threshold needed for evaluation. Default : 1.
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

last_spring_frost (DataArray) – Day of year of last spring frost (day_of_year) description: Day of year of last spring frost, defined as the last day a minimum temperature threshold of {thresh} is not exceeded before a given date.

xclim.indicators.atmos._temperature.latitude_temperature_index(tas: Union[DataArray, str] = 'tas', lat: Union[DataArray, str] = 'lat', *, freq: str = 'YS', ds: Dataset = None) → DataArray

Latitude-Temperature Index. (realm: atmos)

Mean temperature of the warmest month with a latitude-based scaling factor ([Jackson&Cherry1988]_). Used for categorizing wine-growing regions.

This indicator will check for missing values according to the method “from_context”. Based on indice latitude_temperature_index(). With injected parameters: lat_factor=60.

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
lat (str or DataArray) – Latitude coordinate. Default : ds.lat. [Required units : []]
freq (offset alias (string)) – Resampling frequency. Restricted to frequencies equivalent to one of [‘A’] Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

lti (DataArray) – Latitude-temperature index description: A climate indice based on mean temperature of the warmest month and a latitude-based coefficient to account for longer day-length favouring growing conditions. Developed specifically for viticulture. Mean temperature of warmest month * ({lat_factor} - latitude). comment: Indice originally published in Jackson, D. I., & Cherry, N. J. (1988)

Notes

The latitude factor of 75 is provided for examining the poleward expansion of wine-growing climates under scenarios of climate change (modified from [Kenny&Shao1992]_). For comparing 20th century/observed historical records, the original scale factor of 60 is more appropriate.

Let $Tn_{j}$ be the average temperature for a given month $j$, $lat_{f}$ be the latitude factor, and $lat$ be the latitude of the area of interest. Then the Latitude-Temperature Index ($LTI$) is:

\[LTI = max(TN_{j}: j = 1..12)(lat_f - |lat|)\]

References

xclim.indicators.atmos._temperature.max_daily_temperature_range(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Maximum of daily temperature range. (realm: atmos)

The mean difference between the daily maximum temperature and the daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice daily_temperature_range(). With injected parameters: op=max.

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

dtrmax (DataArray) – Maximum Diurnal Temperature Range (air_temperature) [K] cell_methods: time range within days time: max over days description: {freq} maximum diurnal temperature range.

Notes

For a default calculation using op=’mean’ :

Let $TX_{ij}$ and $TN_{ij}$ be the daily maximum and minimum temperature at day $i$ of period $j$. Then the mean diurnal temperature range in period $j$ is:

\[DTR_j = \frac{ \sum_{i=1}^I (TX_{ij} - TN_{ij}) }{I}\]

xclim.indicators.atmos._temperature.maximum_consecutive_frost_free_days(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum number of consecutive frost free days (Tn >= 0℃). (realm: atmos)

Return the maximum number of consecutive days within the period where the minimum temperature is above or equal to a certain threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_frost_free_days().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

consecutive_frost_free_days (DataArray) – Maximum number of consecutive days with Tmin >= {thresh} (spell_length_of_days_with_air_temperature_above_threshold) [days] cell_methods: time: maximum over days description: {freq} maximum number of consecutive days with minimum daily temperature above or equal to {thresh}.

Notes

Let $\mathbf{t}=t_0, t_1, \ldots, t_n$ be a daily minimum temperature series and $thresh$ the threshold above or equal to which a day is considered a frost free day. Let $\mathbf{s}$ be the sorted vector of indices $i$ where $[t_i <= thresh] \neq [t_{i+1} <= thresh]$, that is, the days when the temperature crosses the threshold. Then the maximum number of consecutive frost free days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} >= thresh]\]

where $[P]$ is 1 if $P$ is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos._temperature.maximum_consecutive_warm_days(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: str = '25 degC', freq: str = 'YS', ds: Dataset = None) → DataArray

Maximum number of consecutive days with tasmax above a threshold (summer days). (realm: atmos)

Return the maximum number of consecutive days within the period where the maximum temperature is above a certain threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice maximum_consecutive_tx_days().

Parameters

tasmax (str or DataArray) – Max daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature. Default : 25 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

maximum_consecutive_warm_days (DataArray) – The maximum number of days with tasmax > thresh per periods (summer days). (spell_length_of_days_with_air_temperature_above_threshold) [days] cell_methods: time: maximum over days description: {freq} longest spell of consecutive days with Tmax above {thresh}.

Notes

Let $\mathbf{t}=t_0, t_1, \ldots, t_n$ be a daily maximum temperature series and $thresh$ the threshold above which a day is considered a summer day. Let $\mathbf{s}$ be the sorted vector of indices $i$ where $[t_i < thresh] \neq [t_{i+1} < thresh]$, that is, the days when the temperature crosses the threshold. Then the maximum number of consecutive dry days is given by

\[\max(\mathbf{d}) \quad \mathrm{where} \quad d_j = (s_j - s_{j-1}) [t_{s_j} > thresh]\]

where $[P]$ is 1 if $P$ is true, and 0 if false. Note that this formula does not handle sequences at the start and end of the series, but the numerical algorithm does.

xclim.indicators.atmos._temperature.tg10p(tas: Union[DataArray, str] = 'tas', tas_per: Union[DataArray, str] = 'tas_per', *, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of days with daily mean temperature below the 10th percentile. (realm: atmos)

Number of days with daily mean temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tg10p().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
tas_per (str or DataArray) – 10th percentile of daily mean temperature. Default : ds.tas_per. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg10p (DataArray) – Number of days when Tmean < {tas_per_thresh}th percentile (days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with mean daily temperature below the {tas_per_thresh}th percentile(s). A {tas_per_window} day(s) window, centred on each calendar day in the {tas_per_period} period, is used to compute the {tas_per_thresh}th percentile(s).

Notes

The 10th percentile should be computed for a 5 day window centered on each calendar day for a reference period.

xclim.indicators.atmos._temperature.tg90p(tas: Union[DataArray, str] = 'tas', tas_per: Union[DataArray, str] = 'tas_per', *, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of days with daily mean temperature over the 90th percentile. (realm: atmos)

Number of days with daily mean temperature over the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tg90p().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
tas_per (str or DataArray) – 90th percentile of daily mean temperature. Default : ds.tas_per. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg90p (DataArray) – Number of days when Tmean > {tas_per_thresh}th percentile (days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with mean daily temperature above the the {tas_per_thresh}th percentile(s). A {tas_per_window} day(s) window, centred on each calendar day in the {tas_per_period} period, is used to compute the {tas_per_thresh}th percentile(s).

Notes

The 90th percentile should be computed for a 5 day window centered on each calendar day for a reference period.

xclim.indicators.atmos._temperature.tg_days_above(tas: Union[DataArray, str] = 'tas', *, thresh: str = '10.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with tas above a threshold. (realm: atmos)

Number of days where daily mean temperature exceeds a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_days_above().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 10.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_days_above (DataArray) – Number of days with Tavg > {thresh} (number_of_days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days where daily mean temperature exceeds {thresh}.

Notes

Let $TG_{ij}$ be the daily mean temperature at day $i$ of period $j$. Then counted is the number of days where:

\[TG_{ij} > Threshold [℃]\]

xclim.indicators.atmos._temperature.tg_days_below(tas: Union[DataArray, str] = 'tas', *, thresh: str = '10.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with tas below a threshold. (realm: atmos)

Number of days where daily mean temperature is below a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_days_below().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 10.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_days_below (DataArray) – Number of days with Tavg < {thresh} (number_of_days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days where daily mean temperature is below {thresh}.

Notes

Let $TG_{ij}$ be the daily mean temperature at day $i$ of period $j$. Then counted is the number of days where:

\[TG_{ij} < Threshold [℃]\]

xclim.indicators.atmos._temperature.tg_max(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Highest mean temperature. (realm: atmos)

The maximum of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_max().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_max (DataArray) – Maximum daily mean temperature (air_temperature) [K] cell_methods: time: maximum over days description: {freq} maximum of daily mean temperature.

Notes

Let $TN_{ij}$ be the mean temperature at day $i$ of period $j$. Then the maximum daily mean temperature for period $j$ is:

\[TNx_j = max(TN_{ij})\]

xclim.indicators.atmos._temperature.tg_mean(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean of daily average temperature. (realm: atmos)

Resample the original daily mean temperature series by taking the mean over each period.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_mean().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_mean (DataArray) – Mean daily mean temperature (air_temperature) [K] cell_methods: time: mean over days description: {freq} mean of daily mean temperature.

Notes

Let $TN_i$ be the mean daily temperature of day $i$, then for a period $p$ starting at day $a$ and finishing on day $b$:

\[TG_p = \frac{\sum_{i=a}^{b} TN_i}{b - a + 1}\]

xclim.indicators.atmos._temperature.tg_min(tas: Union[DataArray, str] = 'tas', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Lowest mean temperature. (realm: atmos)

Minimum of daily mean temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tg_min().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tg_min (DataArray) – Minimum daily mean temperature (air_temperature) [K] cell_methods: time: minimum over days description: {freq} minimum of daily mean temperature.

Notes

Let $TG_{ij}$ be the mean temperature at day $i$ of period $j$. Then the minimum daily mean temperature for period $j$ is:

\[TGn_j = min(TG_{ij})\]

xclim.indicators.atmos._temperature.thawing_degree_days(tas: Union[DataArray, str] = 'tas', *, thresh: str = '0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Growing degree-days over threshold temperature value. (realm: atmos)

The sum of degree-days over the threshold temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice growing_degree_days().

Parameters

tas (str or DataArray) – Mean daily temperature. Default : ds.tas. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

thawing_degree_days (DataArray) – Thawing degree days (degree days above 0°C) (integral_of_air_temperature_excess_wrt_time) [K days] cell_methods: time: sum over days description: {freq} thawing degree days above 0°C.

Notes

Let $TG_{ij}$ be the daily mean temperature at day $i$ of period $j$. Then the growing degree days are:

\[GD4_j = \sum_{i=1}^I (TG_{ij}-{4} | TG_{ij} > {4}℃)\]

xclim.indicators.atmos._temperature.tn10p(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of days with daily minimum temperature below the 10th percentile. (realm: atmos)

Number of days with daily minimum temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tn10p().

Parameters

tasmin (str or DataArray) – Mean daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmin_per (str or DataArray) – 10th percentile of daily minimum temperature. Default : ds.tasmin_per. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn10p (DataArray) – Number of days when Tmin < {tasmin_per_thresh}th percentile (days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with minimum daily temperature below the the {tasmin_per_thresh}th percentile(s). A {tasmin_per_window} day(s) window, centred on each calendar day in the {tasmin_per_period} period, is used to compute the {tasmin_per_thresh}th percentile(s).

Notes

The 10th percentile should be computed for a 5 day window centered on each calendar day for a reference period.

xclim.indicators.atmos._temperature.tn90p(tasmin: Union[DataArray, str] = 'tasmin', tasmin_per: Union[DataArray, str] = 'tasmin_per', *, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of days with daily minimum temperature over the 90th percentile. (realm: atmos)

Number of days with daily minimum temperature over the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tn90p().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmin_per (str or DataArray) – 90th percentile of daily minimum temperature. Default : ds.tasmin_per. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn90p (DataArray) – Number of days when Tmin > {tasmin_per_thresh}th percentile (days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with minimum daily temperature above the the {tasmin_per_thresh}th percentile(s). A {tasmin_per_window} day(s) window, centred on each calendar day in the {tasmin_per_period} period, is used to compute the {tasmin_per_thresh}th percentile(s).

Notes

The 90th percentile should be computed for a 5 day window centered on each calendar day for a reference period.

xclim.indicators.atmos._temperature.tn_days_above(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '20.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with tasmin above a threshold (number of tropical nights). (realm: atmos)

Number of days where daily minimum temperature exceeds a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_days_above().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 20.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_days_above (DataArray) – Number of days with Tmin > {thresh} (number_of_days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days where daily minimum temperature exceeds {thresh}.

Notes

Let $TN_{ij}$ be the daily minimum temperature at day $i$ of period $j$. Then counted is the number of days where:

\[TN_{ij} > Threshold [℃]\]

xclim.indicators.atmos._temperature.tn_days_below(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '-10.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with tasmin below a threshold. (realm: atmos)

Number of days where daily minimum temperature is below a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_days_below().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : -10.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_days_below (DataArray) – Number of days with Tmin < {thresh} (number_of_days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days where daily minimum temperature is below {thresh}.

Notes

Let $TN_{ij}$ be the daily minimum temperature at day $i$ of period $j$. Then counted is the number of days where:

\[TN_{ij} < Threshold [℃]\]

xclim.indicators.atmos._temperature.tn_max(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Highest minimum temperature. (realm: atmos)

The maximum of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_max().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_max (DataArray) – Maximum daily minimum temperature (air_temperature) [K] cell_methods: time: maximum over days description: {freq} maximum of daily minimum temperature.

Notes

Let $TN_{ij}$ be the minimum temperature at day $i$ of period $j$. Then the maximum daily minimum temperature for period $j$ is:

\[TNx_j = max(TN_{ij})\]

xclim.indicators.atmos._temperature.tn_mean(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean minimum temperature. (realm: atmos)

Mean of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_mean().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_mean (DataArray) – Mean daily minimum temperature (air_temperature) [K] cell_methods: time: mean over days description: {freq} mean of daily minimum temperature.

Notes

Let $TN_{ij}$ be the minimum temperature at day $i$ of period $j$. Then mean values in period $j$ are given by:

\[TN_{ij} = \frac{ \sum_{i=1}^{I} TN_{ij} }{I}\]

xclim.indicators.atmos._temperature.tn_min(tasmin: Union[DataArray, str] = 'tasmin', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Lowest minimum temperature. (realm: atmos)

Minimum of daily minimum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_min().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tn_min (DataArray) – Minimum daily minimum temperature (air_temperature) [K] cell_methods: time: minimum over days description: {freq} minimum of daily minimum temperature.

Notes

Let $TN_{ij}$ be the minimum temperature at day $i$ of period $j$. Then the minimum daily minimum temperature for period $j$ is:

\[TNn_j = min(TN_{ij})\]

xclim.indicators.atmos._temperature.tropical_nights(tasmin: Union[DataArray, str] = 'tasmin', *, thresh: str = '20.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with tasmin above a threshold (number of tropical nights). (realm: atmos)

Number of days where daily minimum temperature exceeds a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tn_days_above().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 20.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tropical_nights (DataArray) – Number of Tropical Nights (Tmin > {thresh}) (number_of_days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of Tropical Nights : defined as days with minimum daily temperature above {thresh}.

Notes

Let $TN_{ij}$ be the daily minimum temperature at day $i$ of period $j$. Then counted is the number of days where:

\[TN_{ij} > Threshold [℃]\]

xclim.indicators.atmos._temperature.tx10p(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of days with daily maximum temperature below the 10th percentile. (realm: atmos)

Number of days with daily maximum temperature below the 10th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tx10p().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
tasmax_per (str or DataArray) – 10th percentile of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx10p (DataArray) – Number of days when Tmax < {tasmax_per_thresh}th percentile (days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with maximum daily temperature below the {tasmax_per_thresh}th percentile(s). A {tasmax_per_window} day(s) window, centred on each calendar day in the {tasmax_per_period} period, is used to compute the {tasmax_per_thresh}th percentile(s).

Notes

The 10th percentile should be computed for a 5 day window centered on each calendar day for a reference period.

xclim.indicators.atmos._temperature.tx90p(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None, **indexer) → DataArray

Number of days with daily maximum temperature over the 90th percentile. (realm: atmos)

Number of days with daily maximum temperature over the 90th percentile.

This indicator will check for missing values according to the method “from_context”. Based on indice tx90p().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
tasmax_per (str or DataArray) – 90th percentile of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx90p (DataArray) – Number of days when Tmax > {tasmax_per_thresh}th percentile (days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with maximum daily temperature above the {tasmax_per_thresh}th percentile(s). A {tasmax_per_window} day(s) window, centred on each calendar day in the {tasmax_per_period} period, is used to compute the {tasmax_per_thresh}th percentile(s).

Notes

The 90th percentile should be computed for a 5-day window centered on each calendar day for a reference period.

xclim.indicators.atmos._temperature.tx_days_above(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: str = '25.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with tasmax above a threshold (number of summer days). (realm: atmos)

Number of days where daily maximum temperature exceeds a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_days_above().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 25.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_days_above (DataArray) – Number of days with Tmax > {thresh} (number_of_days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days where daily maximum temperature exceeds {thresh}.

Notes

Let $TX_{ij}$ be the daily maximum temperature at day $i$ of period $j$. Then counted is the number of days where:

\[TX_{ij} > Threshold [℃]\]

xclim.indicators.atmos._temperature.tx_days_below(tasmax: Union[DataArray, str] = 'tasmax', *, thresh: str = '25.0 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with tmax below a threshold. (realm: atmos)

Number of days where daily maximum temperature is below a threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_days_below().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh (quantity (string with units)) – Threshold temperature on which to base evaluation. Default : 25.0 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_days_below (DataArray) – Number of days with Tmax < {thresh} (number_of_days_with_air_temperature_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days where daily max temperature is below {thresh}.

Notes

Let $TN_{ij}$ be the daily minimum temperature at day $i$ of period $j$. Then counted is the number of days where:

\[TN_{ij} < Threshold [℃]\]

xclim.indicators.atmos._temperature.tx_max(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Highest max temperature. (realm: atmos)

The maximum value of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_max().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_max (DataArray) – Maximum daily maximum temperature (air_temperature) [K] cell_methods: time: maximum over days description: {freq} maximum of daily maximum temperature.

Notes

Let $TX_{ij}$ be the maximum temperature at day $i$ of period $j$. Then the maximum daily maximum temperature for period $j$ is:

\[TXx_j = max(TX_{ij})\]

xclim.indicators.atmos._temperature.tx_mean(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Mean max temperature. (realm: atmos)

The mean of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_mean().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_mean (DataArray) – Mean daily maximum temperature (air_temperature) [K] cell_methods: time: mean over days description: {freq} mean of daily maximum temperature.

Notes

Let $TX_{ij}$ be the maximum temperature at day $i$ of period $j$. Then mean values in period $j$ are given by:

\[TX_{ij} = \frac{ \sum_{i=1}^{I} TX_{ij} }{I}\]

xclim.indicators.atmos._temperature.tx_min(tasmax: Union[DataArray, str] = 'tasmax', *, freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Lowest max temperature. (realm: atmos)

The minimum of daily maximum temperature.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_min().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_min (DataArray) – Minimum daily maximum temperature (air_temperature) [K] cell_methods: time: minimum over days description: {freq} minimum of daily maximum temperature.

Notes

Let $TX_{ij}$ be the maximum temperature at day $i$ of period $j$. Then the minimum daily maximum temperature for period $j$ is:

\[TXn_j = min(TX_{ij})\]

xclim.indicators.atmos._temperature.tx_tn_days_above(tasmin: Union[DataArray, str] = 'tasmin', tasmax: Union[DataArray, str] = 'tasmax', *, thresh_tasmin: str = '22 degC', thresh_tasmax: str = '30 degC', freq: str = 'YS', ds: Dataset = None, **indexer) → DataArray

Number of days with both hot maximum and minimum daily temperatures. (realm: atmos)

The number of days per period with tasmin above a threshold and tasmax above another threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice tx_tn_days_above().

Parameters

tasmin (str or DataArray) – Minimum daily temperature. Default : ds.tasmin. [Required units : [temperature]]
tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
thresh_tasmin (quantity (string with units)) – Threshold temperature for tasmin on which to base evaluation. Default : 22 degC. [Required units : [temperature]]
thresh_tasmax (quantity (string with units)) – Threshold temperature for tasmax on which to base evaluation. Default : 30 degC. [Required units : [temperature]]
freq (offset alias (string)) – Resampling frequency. Default : YS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

tx_tn_days_above (DataArray) – Number of days with Tmax > {thresh_tasmax} and Tmin > {thresh_tasmin} (number_of_days_with_air_temperature_above_threshold) [days] description: {freq} number of days where daily maximum temperature exceeds {thresh_tasmax} and minimum temperature exceeds {thresh_tasmin}.

Notes

Let $TX_{ij}$ be the maximum temperature at day $i$ of period $j$, $TN_{ij}$ the daily minimum temperature at day $i$ of period $j$, $TX_{thresh}$ the threshold for maximum daily temperature, and $TN_{thresh}$ the threshold for minimum daily temperature. Then counted is the number of days where:

\[TX_{ij} > TX_{thresh} [℃]\]

and where:

\[TN_{ij} > TN_{thresh} [℃]\]

xclim.indicators.atmos._temperature.warm_spell_duration_index(tasmax: Union[DataArray, str] = 'tasmax', tasmax_per: Union[DataArray, str] = 'tasmax_per', *, window: int = 6, freq: str = 'YS', bootstrap: bool = False, ds: Dataset = None) → DataArray

Warm spell duration index. (realm: atmos)

Number of days inside spells of a minimum number of consecutive days where the daily maximum temperature is above the 90th percentile. The 90th percentile should be computed for a 5-day moving window, centered on each calendar day in the 1961-1990 period.

This indicator will check for missing values according to the method “from_context”. Based on indice warm_spell_duration_index().

Parameters

tasmax (str or DataArray) – Maximum daily temperature. Default : ds.tasmax. [Required units : [temperature]]
tasmax_per (str or DataArray) – percentile(s) of daily maximum temperature. Default : ds.tasmax_per. [Required units : [temperature]]
window (number) – Minimum number of days with temperature above threshold to qualify as a warm spell. Default : 6.
freq (offset alias (string)) – Resampling frequency. Default : YS.
bootstrap (boolean) – Flag to run bootstrapping of percentiles. Used by percentile_bootstrap decorator. Bootstrapping is only useful when the percentiles are computed on a part of the studied sample. This period, common to percentiles and the sample must be bootstrapped to avoid inhomogeneities with the rest of the time series. Keep bootstrap to False when there is no common period, it would give wrong results plus, bootstrapping is computationally expensive. Default : False.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.

Returns

warm_spell_duration_index (DataArray) – Number of days part of a percentile-defined warm spell (number_of_days_with_air_temperature_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with at least {window} consecutive days where the daily maximum temperature is above the {tasmax_per_thresh}th percentile(s). A {tasmax_per_window} day(s) window, centred on each calendar day in the {tasmax_per_period} period, is used to compute the {tasmax_per_thresh}th percentile(s).

References

From the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Used in Alexander, L. V., et al. (2006), Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res., 111, D05109, doi: 10.1029/2005JD006290.

xclim.indicators.atmos._wind module

xclim.indicators.atmos._wind.calm_days(sfcWind: Union[DataArray, str] = 'sfcWind', *, thresh: str = '2 m s-1', freq: str = 'MS', ds: Dataset = None, **indexer) → DataArray

Calm days. (realm: atmos)

The number of days with average near-surface wind speed below threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice calm_days().

Parameters

sfcWind (str or DataArray) – Daily windspeed. Default : ds.sfcWind. [Required units : [speed]]
thresh (quantity (string with units)) – Threshold average near-surface wind speed on which to base evaluation. Default : 2 m s-1. [Required units : [speed]]
freq (offset alias (string)) – Resampling frequency. Default : MS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

calm_days (DataArray) – Number of days with surface wind speed below threshold (number_of_days_with_sfcWind_below_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with surface wind speed < {thresh}

Notes

Let $WS_{ij}$ be the windspeed at day $i$ of period $j$. Then counted is the number of days where:

\[WS_{ij} < Threshold [m s-1]\]

xclim.indicators.atmos._wind.windy_days(sfcWind: Union[DataArray, str] = 'sfcWind', *, thresh: str = '10.8 m s-1', freq: str = 'MS', ds: Dataset = None, **indexer) → DataArray

Windy days. (realm: atmos)

The number of days with average near-surface wind speed above threshold.

This indicator will check for missing values according to the method “from_context”. Based on indice windy_days().

Parameters

sfcWind (str or DataArray) – Daily average near-surface wind speed. Default : ds.sfcWind. [Required units : [speed]]
thresh (quantity (string with units)) – Threshold average near-surface wind speed on which to base evaluation. Default : 10.8 m s-1. [Required units : [speed]]
freq (offset alias (string)) – Resampling frequency. Default : MS.
ds (Dataset, optional) – A dataset with the variables given by name. Default : None.
indexer – Indexing parameters to compute the indicator on a temporal subset of the data. It accepts the same arguments as xclim.indices.generic.select_time(). Default : None.

Returns

windy_days (DataArray) – Number of days with surface wind speed above threshold (number_of_days_with_sfcWind_above_threshold) [days] cell_methods: time: sum over days description: {freq} number of days with surface wind speed >= {thresh}

Notes

Let $WS_{ij}$ be the windspeed at day $i$ of period $j$. Then counted is the number of days where:

\[WS_{ij} >= Threshold [m s-1]\]