Multi-model means (MMM)#

Use simple multi-model mean for predictions.


This diagnostic calculates the (unweighted) mean over all given datasets for a given target variable.


Manuel Schlund (DLR, Germany)



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 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.