wradlib.verify.ErrorMetrics#
- class wradlib.verify.ErrorMetrics(obs, est, *, minval=None)[source]#
Bases:
object
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.
|
Returns a dictionary of all error metrics |
|
Correlation coefficient |
|
Mean Absolute Error |
|
Mean Error |
|
Mean Squared Error |
|
Nash-Sutcliffe Efficiency |
|
Percent bias |
|
Pretty prints a summary of error metrics |
|
Coefficient of determination |
|
Mean ratio between observed and estimated |
|
Root Mean Squared Error |
|
Spearman rank correlation coefficient |
|
Sum of Squared Errors |