mesmer.calibrate_mesmer.train_lv_AR1_sci(params_lv, targs, y, wgt_scen_eq, aux, cfg)#

Derive parameters for AR(1) process with spatially-correlated innovations.

  • params_lv (dict) – dictionary with the trained local variability parameters

    • [“targ”] (variable which is emulated, str)

    • [“esm”] (Earth System Model, str)

    • [“method”] (applied method, str)

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

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

    • [xx] (additional keys depend on employed method)

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

    • [targ][scen] with 3d arrays (run, time, gp)

  • y (np.ndarray) – 3d array (sample, gp, targ) of targets

  • wgt_scen_eq (np.ndarray) – 1d array (sample) of sample weights

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

    • [“phi_gc”] (Xd arrays of auxiliary variable)

  • cfg (module) – config file containing metadata


emus_lv (dict) – local variability emulations dictionary with keys

  • [scen] (2d array (emu, time, gp) of local variability in response to global variability emulation time series)


  • Assumptions:
    • do for each target variable independently

    • the variability is Gaussian

    • each scenario receives the same weight during training

  • Potential TODO:
    • add possibility to account for cross-correlation between different variables (i.e., joint instead of independent emulation)