ens_mae

hydrostats.ens_metrics.ens_mae(obs, fcst_ens=None, remove_zero=False, remove_neg=False)

Calculate the mean absolute error between observed values and the ensemble mean.

Parameters:
  • obs (1D ndarray) – Array of observations for each start date.
  • fcst_ens (2D ndarray) – Array of ensemble forecast of dimension n x M, where n = number of start dates and M = number of ensemble members.
  • remove_neg (bool) – If True, when a negative value is found at the i-th position in the observed OR ensemble array, the i-th value of the observed AND ensemble array are removed before the computation.
  • remove_zero (bool) – If true, when a zero value is found at the i-th position in the observed OR ensemble array, the i-th value of the observed AND ensemble array are removed before the computation.
Returns:

The mean absolute error between the observed time series data and the ensemble mean.
Return type:

float

Examples

>>> import numpy as np
>>> import hydrostats.ens_metrics as em
>>> np.random.seed(3849590438)

Creating an observed 1D array and an ensemble 2D array

>>> ensemble_array = (np.random.rand(100, 52) + 1) * 100  # 52 Ensembles
>>> observed_array = (np.random.rand(100) + 1) * 100

Computing the ME between the ensemble mean and the observed data. Note that because the data is random the errors cancel out, leaving a low ME value.

>>> em.ens_mae(obs=observed_array, fcst_ens=ensemble_array)
26.35428724003365