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 thepprint
method.The
ix
member variable indicates valid pairs ofobs
andest
, based on NaNs andminval
.- 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.
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