improver.cli.estimate_emos_coefficients module
CLI to estimate coefficients for Ensemble Model Output Statistics (EMOS), otherwise known as Non-homogeneous Gaussian Regression (NGR).
- process(*cubes, distribution, truth_attribute, point_by_point=False, use_default_initial_guess=False, units=None, predictor='mean', tolerance=0.02, max_iterations=1000)[source]
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.
- Parameters:
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:
CubeList containing the coefficients estimated using EMOS. Each coefficient is stored in a separate cube.
- Return type: