dr

HydroErr.HydroErr.dr(simulated_array, observed_array, replace_nan=None, replace_inf=None, remove_neg=False, remove_zero=False)

Compute the the refined index of agreement (dr).

../_images/dr.png

Range: -1 ≤ dr < 1, does not indicate bias, larger is better.

Notes: Reformulation of Willmott’s index of agreement. This metric was created to address issues in the index of agreement and the Nash-Sutcliffe efficiency metric. Meant to be a flexible metric for use in climatology.

Parameters:
  • simulated_array (one dimensional ndarray) – An array of simulated data from the time series.
  • observed_array (one dimensional ndarray) – An array of observed data from the time series.
  • replace_nan (float, optional) – If given, indicates which value to replace NaN values with in the two arrays. If None, when a NaN value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • replace_inf (float, optional) – If given, indicates which value to replace Inf values with in the two arrays. If None, when an inf value is found at the i-th position in the observed OR simulated array, the i-th value of the observed and simulated array are removed before the computation.
  • remove_neg (boolean, optional) – If True, when a negative value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
  • remove_zero (boolean, optional) – If true, when a zero value is found at the i-th position in the observed OR simulated array, the i-th value of the observed AND simulated array are removed before the computation.
Returns:

The refined index of agreement.
Return type:

float

Examples

>>> import HydroErr as he
>>> import numpy as np

>>> sim = np.array([5, 7, 9, 2, 4.5, 6.7])
>>> obs = np.array([4.7, 6, 10, 2.5, 4, 7])
>>> he.dr(sim, obs)
0.847457627118644

References

  • Willmott, C.J., Robeson, S.M., Matsuura, K., 2012. A refined index of model performance. International Journal of Climatology 32(13) 2088-2094.