mesmer.calibrate_mesmer.train_gv(gv, targ, esm, cfg, save_params=True, **kwargs)#

Derive global variability parameters for a specified method.

  • gv (dict) – Nested global mean variability dictionary with keys

    • [scen] (2d array (run, time) of globally-averaged variability time series)

  • targ (str) – target variable (e.g., “tas”)

  • esm (str) – associated Earth System Model (e.g., “CanESM2” or “CanESM5”)

  • cfg (config module) – config file containing metadata

  • save_params (bool, optional) – determines if parameters are saved or not, default = True

  • **kwargs (Any) – additional arguments, passed through to the training function


params_gv (dict) – dictionary containing the trained parameters for the chosen method / ensemble type

  • [“targ”] (emulated variable, str)

  • [“esm”] (Earth System Model, str)

  • [“method”] (applied method, str)

  • [“preds”] (predictors, list of strs)

  • [“scenarios”] (emission scenarios used for training, list of strs)

  • [xx] additional params depend on method employed, specified in train_gv_T_method() function


  • Assumption
    • if historical data is used for training, it has its own scenario

  • TODO:
    • add ability to weight samples differently than equal weight for each scenario