Setting Up Your Dataset#
climpred relies on a consistent naming system for
This allows things to run more easily under-the-hood.
PredictionEnsemble expects at the minimum to contain dimensions
init is the initialization dimension, that relays the time
steps at which the ensemble was initialized.
init is known as
forecast_reference_time in the CF convention.
init must be of type
init is of type
int, it is assumed to be annual data starting Jan 1st.
A UserWarning is issues when this assumption is made.
lead is the lead time of the forecasts from initialization.
lead is known as
forecast_period in the CF convention.
lead must be
The units for the
lead dimension must be specified in as an attribute.
Valid options are
["years", "seasons", "months"] and
["weeks", "pentads", "days", "hours", "minutes", "seconds"].
lead is provided as
pandas.Timedelta up to
is converted to
int and a corresponding
["months", "seasons" or "years"], no conversion is possible.
valid_time=init+lead will be calculated in
Another crucial dimension is
member, which holds the various ensemble members,
which is only required for probabilistic metrics.
member is known as
realization in the CF convention
Any additional dimensions will be broadcasted: these could be dimensions like
If the expected dimensions are not found, but the matching CF convention
standard_name in a coordinate attribute, the dimension is renamed to the
climpred ensemble dimension.
Verification products are expected to contain the
time dimension at the minimum.
For best use of
time dimension should cover the full length of
init and be the same calendar type as the accompanying prediction ensemble.
time dimension must be
time dimension of type
int is assumed to be annual data starting Jan 1st.
A UserWarning is issued when this assumption is made.
These products can also include additional dimensions, such as
See the below table for a summary of dimensions used in
climpred, and data types
climpred supports for them.
lead timestep after initialization
units (str) [
initialization as start date of experiment
Probably the most challenging part is concatenating
xarray.concat()) raw model output with dimension
multiple simulations to a multi-dimensional
valid_time=init+lead. One way of doing it is