#!/usr/bin/env python
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"""CLI to estimate coefficients for Ensemble Model Output
Statistics (EMOS), otherwise known as Non-homogeneous Gaussian
Regression (NGR)."""
from improver import cli
[docs]@cli.clizefy
@cli.with_output
def process(
*cubes: cli.inputcube,
distribution,
truth_attribute,
point_by_point=False,
use_default_initial_guess=False,
units=None,
predictor="mean",
tolerance: float = 0.02,
max_iterations: int = 1000,
):
"""Estimate coefficients for Ensemble Model Output Statistics.
Loads in arguments for estimating coefficients for Ensemble Model
Output Statistics (EMOS), otherwise known as Non-homogeneous Gaussian
Regression (NGR). Two sources of input data must be provided: historical
forecasts and historical truth data (to use in calibration).
The estimated coefficients are output as a cube.
Args:
cubes (list of iris.cube.Cube):
A list of cubes containing the historical forecasts and
corresponding truth used for calibration. They must have the same
cube name and will be separated based on the truth attribute.
Optionally this may also contain a single land-sea mask cube on the
same domain as the historic forecasts and truth (where land points
are set to one and sea points are set to zero).
distribution (str):
The distribution that will be used for minimising the
Continuous Ranked Probability Score when estimating the EMOS
coefficients. This will be dependent upon the input phenomenon.
truth_attribute (str):
An attribute and its value in the format of "attribute=value",
which must be present on historical truth cubes.
point_by_point (bool):
If True, coefficients are calculated independently for each point
within the input cube by creating an initial guess and minimising
each grid point independently. If False, a single set of
coefficients is calculated using all points.
Warning: This option is memory intensive and is unsuitable for
gridded input. Using a default initial guess may reduce the memory
overhead option.
use_default_initial_guess (bool):
If True, use the default initial guess. The default initial guess
assumes no adjustments are required to the initial choice of
predictor to generate the calibrated distribution. This means
coefficients of 1 for the multiplicative coefficients and 0 for
the additive coefficients. If False, the initial guess is computed.
units (str):
The units that calibration should be undertaken in. The historical
forecast and truth will be converted as required.
predictor (str):
String to specify the form of the predictor used to calculate the
location parameter when estimating the EMOS coefficients.
Currently the ensemble mean ("mean") and the ensemble realizations
("realizations") are supported as options.
tolerance (float):
The tolerance for the Continuous Ranked Probability Score (CRPS)
calculated by the minimisation. Once multiple iterations result in
a CRPS equal to the same value within the specified tolerance, the
minimisation will terminate.
max_iterations (int):
The maximum number of iterations allowed until the minimisation has
converged to a stable solution. If the maximum number of iterations
is reached but the minimisation has not yet converged to a stable
solution, then the available solution is used anyway, and a warning
is raised. If the predictor is "realizations", then the number of
iterations may require increasing, as there will be more
coefficients to solve.
Returns:
iris.cube.CubeList:
CubeList containing the coefficients estimated using EMOS. Each
coefficient is stored in a separate cube.
"""
from improver.calibration import split_forecasts_and_truth
from improver.calibration.ensemble_calibration import (
EstimateCoefficientsForEnsembleCalibration,
)
forecast, truth, land_sea_mask = split_forecasts_and_truth(cubes, truth_attribute)
plugin = EstimateCoefficientsForEnsembleCalibration(
distribution,
point_by_point=point_by_point,
use_default_initial_guess=use_default_initial_guess,
desired_units=units,
predictor=predictor,
tolerance=tolerance,
max_iterations=max_iterations,
)
return plugin(forecast, truth, landsea_mask=land_sea_mask)