xclim.core.indicator.Indicator instances essentially perform the same computations as the functions
found in the
xclim.indices library, but also run a number of health checks on input data
and assign attributes to the output arrays. So for example, if there are missing values in
a time series, indices won’t notice, but indicators will return NaNs for periods with missing
values (depending on the missing values algorithm selected, see Missing Values Identification). Indicators also check that the input data has the expected frequency (e.g. daily) and that
it is indeed the expected variable (e.g. a precipitation flux). The output is assigned attributes
that conform as much as possible with the CF-Convention.
Indicators are split into realms (atmos, land, seaIce), according to the variables they operate on. See Defining new indicators for instruction on how to create your own indicators. This page allows a simple free text search of all indicators. Click on the python names to get to the complete docstring of each indicator.