class wradlib.verify.ErrorMetrics(obs, est, minval=None)

Compute quality metrics from a set of observations (obs) and estimates (est).

First create an instance of the class using the set of observations and estimates. Then compute quality metrics using the class methods. A dictionary of all available quality metrics is returned using the all method, or printed to the screen using the pprint method.

The ix member variable indicates valid pairs of obs and est, based on NaNs and minval.

Parameters: obs (numpy.ndarray) – array of observations (e.g. rain gage observations) est (numpy.ndarray) – array of estimates (e.g. radar, adjusted radar, …) minval (float) – threshold value in order to compute metrics only for values larger than minval

Examples

>>> obs = np.random.uniform(0, 10, 100)
>>> est = np.random.uniform(0, 10, 100)
>>> metrics = ErrorMetrics(obs, est)
>>> metrics.all() #doctest: +SKIP
>>> metrics.pprint() #doctest: +SKIP
>>> metrics.ix #doctest: +SKIP

 all() Returns a dictionary of all error metrics corr() Correlation coefficient mas() Mean Absolute Error meanerr() Mean Error mse() Mean Squared Error nash() Nash-Sutcliffe Efficiency pbias() Percent bias pprint() Pretty prints a summary of error metrics r2() Coefficient of determination ratio() Mean ratio between observed and estimated rmse() Root Mean Squared Error spearman() Spearman rank correlation coefficient sse() Sum of Squared Errors