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.

  • 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


>>> obs = np.random.uniform(0, 10, 100)
>>> est = np.random.uniform(0, 10, 100)
>>> metrics = ErrorMetrics(obs, est)
>>> metrics.all() 
>>> metrics.pprint() 
>>> metrics.ix 

See Routine verification measures for radar-based precipitation estimates and Adjusting radar-base rainfall estimates by rain gauge observations.

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