Multi-model means (MMM)¶
Use simple multi-model mean for predictions.
Description¶
This diagnostic calculates the (unweighted) mean over all given datasets for a given target variable.
Author¶
Manuel Schlund (DLR, Germany)
Project¶
CRESCENDO
Configuration options in recipe¶
- convert_units_to: str, optional
Convert units of the input data. Can also be given as dataset option.
- dtype: str (default: ‘float64’)
Internal data type which is used for all calculations, see https://docs.scipy.org/doc/numpy/user/basics.types.html for a list of allowed values.
- ignore: list of dict, optional
Ignore specific datasets by specifying multiple
dict
s of metadata.- mlr_model_name: str, optional (default: ‘MMM’)
Human-readable name of the MLR model instance (e.g used for labels).
- mmm_error_type: str, optional
If given, additionally saves estimated squared MMM model error. If the option is set to
'loo'
, the (constant) error is estimated as RMSEP using leave-one-out cross-validation. No other options are supported at the moment.- pattern: str, optional
Pattern matched against ancestor file names.
- prediction_name: str, optional
Default
prediction_name
of output cubes if no ‘prediction_reference’ dataset is given.- weighted_samples: dict
If specified, use weighted mean square error to estimate prediction error. The given keyword arguments are directly passed to
esmvaltool.diag_scripts.mlr.get_all_weights()
to calculate the sample weights. By default, area weights and time weights are used.