xskillscore: Metrics for verifying forecasts

xskillscore is an open source project and Python package that provides verification metrics of deterministic (and probabilistic from properscoring) forecasts with xarray.

Installing

pip install xskillscore

or

pip install git+https://github.com/raybellwaves/xskillscore

Examples

import xarray as xr
import pandas as pd
import numpy as np
import xskillscore as xs


obs = xr.DataArray(np.random.rand(3, 4, 5),
                   coords=[pd.date_range('1/1/2000', '1/3/2000', freq='D'),
                           np.arange(4), np.arange(5)],
                   dims=['time', 'lat', 'lon'])
fct = xr.DataArray(np.random.rand(3, 4, 5),
                   coords=[pd.date_range('1/1/2000', '1/3/2000', freq='D'),
                           np.arange(4), np.arange(5)],
                   dims=['time', 'lat', 'lon'])

# deterministic
r = xs.pearson_r(obs, fct, 'time')
#>>> r
#<xarray.DataArray (lat: 4, lon: 5)>
#array([[ 0.395493, -0.979171,  0.998584, -0.758511,  0.583867],
#       [ 0.456191,  0.992705,  0.999728, -0.209711,  0.984332],
#       [-0.738775, -0.820627,  0.190332, -0.780365,  0.27864 ],
#       [ 0.908445,  0.744518,  0.348995, -0.993572, -0.999234]])
#Coordinates:
#  * lat      (lat) int64 0 1 2 3
#  * lon      (lon) int64 0 1 2 3 4

r_p_value = xs.pearson_r_p_value(obs, fct, 'time')

rmse = xs.rmse(obs, fct, 'time')

mse = xs.mse(obs, fct, 'time')

mae = xs.mae(obs, fct, 'time')

# You can also specify multiple axes for deterministic metrics
r = xs.pearson_r(obs, fct, ['lat', 'lon'])

# probabilistic
crps_ensemble = xs.crps_ensemble(obs, fct)

crps_gaussian = xs.crps_gaussian(obs, fct.mean('time'), fct.std('time'))

threshold_brier_score = xs.threshold_brier_score(obs, fct, 0.7)

# You can also use xskillscore as a method of your dataset.
ds = xr.Dataset()
ds['obs_var'] = obs
ds['fct_var'] = fct

# This is the equivalent of r = xs.pearson_r(obs, fct, 'time')
r = ds.xs.rmse('obs_var', 'fct_var', 'time')

# If fct is not a part of the dataset, inputting a separate
# DataArray as an argument works as well
ds = ds.drop('fct_var')
r = ds.xs.rmse('obs_var', fct, 'time')