wradlib.verify.ErrorMetrics¶
-
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 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() #doctest: +SKIP >>> metrics.pprint() #doctest: +SKIP >>> metrics.ix #doctest: +SKIP
See Routine verification measures for radar-based precipitation estimates and Adjusting radar-base rainfall estimates by rain gauge observations.
- obs (
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 |