gouthamnaveen / CRPS

A package to compute the Continuous Ranked Probability Score (crps) (Matheson and Winkler, 1976; Hersbach, 2000), the fair-crps (fcrps) (Ferro et al., 2008), and the adjusted-crps (acrps) (Ferro et al., 2008) given an ensemble prediction and an observation. The continuous ranked probability score is a negatively oriented score that is used to compare the empirical distribution of an ensemble prediction to a scalar observation. References: [1] Matheson, J. E. & Winkler, R. L. Scoring Rules for Continuous Probability Distributions. Management Science 22, 1087–1096 (1976). [2] Hersbach, H. Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems. Wea. Forecasting 15, 559–570 (2000). [3] Ferro, C. A. T., Richardson, D. S. & Weigel, A. P. On the effect of ensemble size on the discrete and continuous ranked probability scores. Meteorological Applications 15, 19–24 (2008).

Documentation

A package to compute the continuous ranked probability score (crps) (Matheson and Winkler, 1976; Hersbach, 2000), the fair-crps (fcrps) (Ferro et al., 2008), and the adjusted-crps (acrps) (Ferro et al., 2008) given an ensemble prediction and an observation.

The CRPS is a negatively oriented score that is used to compare the empirical distribution of an ensemble prediction to a scalar observation.

References:

[1] Matheson, J. E. & Winkler, R. L. Scoring Rules for Continuous Probability Distributions. Management Science 22, 1087–1096 (1976).

[2] Hersbach, H. Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems. Wea. Forecasting 15, 559–570 (2000).

[3] Ferro, C. A. T., Richardson, D. S. & Weigel, A. P. On the effect of ensemble size on the discrete and continuous ranked probability scores. Meteorological Applications 15, 19–24 (2008).

Installation:

pip install CRPS

Parameters:

ensemble_members: numpy.ndarray

The predicted ensemble members. They will be sorted in ascending order automatically.

Ex: np.array([2.1,3.5,4.7,1.2,1.3,5.2,5.3,4.2,3.1,1.7])

observation: float

The observed scalar.

Ex: 5.4

adjusted_ensemble_size: int, optional

The size the ensemble needs to be adjusted to before computing the Adjusted Continuous Ranked Probability Score. The default is 200.

Note: The crps becomes equal to acrps when adjusted_ensemble_size equals the length of the ensemble_members.

Method(s):

compute():

Computes the continuous ranked probability score (crps), the fair-crps (fcrps), and the adjusted-crps (acrps).

Returns:

crps,fcrps,acrps

Attributes:

crps: Continuous Ranked Probability Score

It is the integral of the squared difference between the CDF of the forecast ensemble and the observation.

fcrps: Fair-Continuous Ranked Probability Score

It is the crps computed assuming an infinite ensemble size.

where m is the current ensemble size (i.e., len(ensemble_members))

acrps: Adjusted-Continuous Ranked Probability Score

It is the crps computed assuming an ensemble size of M.

where M is the adjusted_ensemble_size

Demonstration:

import numpy as np
import CRPS.CRPS as pscore

Example - 1:

In [1]: pscore(np.arange(1,5),3.5).compute()
Out[1]: (0.625, 0.4166666666666667, 0.42083333333333334)

Example - 2:

In [2]: crps,fcrps,acrps = pscore(np.arange(1,11),8.3,50).compute()
In [3]: crps
Out[3]: 1.6300000000000003
In [4]: fcrps
Out[4]: 1.446666666666667
In [5]: acrps
Out[5]: 1.4833333333333336