mesmer.calibrate_mesmer.train_lv

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mesmer.calibrate_mesmer.train_lv#

mesmer.calibrate_mesmer.train_lv(preds, targs, esm, cfg, save_params=True, aux={}, params_lv={})#

Derive local variability (i.e., natural variabiliy) parameters.

Parameters:

  • preds (dict) – empty dictionary if none, else nested dictionary of predictors with keys

    • [pred][scen] (1d/ 2d arrays (time)/(run, time) of predictor for specific scenario)

  • targs (dict) – nested dictionary of targets with keys

    • [targ][scen] (3d array (run, time, gp) of target for specific scenario)

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

  • cfg (module) – config file containing metadata

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

  • aux (dict, optional) – provides auxiliary variables needed for lv method at hand

    • [var] (Xd arrays of auxiliary variable)

  • params_lv (dict, optional) – pass the params_lv dict, if it already exists so that builds upon that one

Returns:

params_lv (dict) – dictionary of local variability parameters

  • [“targs”] (emulated variables, str)

  • [“esm”] (Earth System Model, str)

  • [“method”] (applied method, str)

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

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

  • [“part_model_in_lt”] (states if part of the model is saved in params_lt, bool)

  • [“method_lt_each_gp_sep”] (states if local trends method is applied to each grid point separately, bool)

  • [xx] (additional params depend on employed lv method)

Notes

  • Assumptions:

    • all targets use same approach and same predictors

    • each predictor and each target has the same scenarios as keys

    • all available scenarios are used for training

    • identified parameters are valid for all training scenarios

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

    • need to pass the params_lv dict if it already exists so that can continue to build on it

  • Disclaimer:

    • currently no method with preds implemented; but already have in there for consistency

  • TODO:

    • add ability to weight samples differently than equal weight for each scenario in AR process

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