wradlib.verify.ErrorMetrics#

class wradlib.verify.ErrorMetrics(obs, est, minval=None)[source]#

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() 
>>> metrics.pprint() 
>>> metrics.ix

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

__init__(obs, est, minval=None)[source]#

Methods

__init__(obs, est[, minval])

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