Verification#

Verification mainly refers to the comparison of radar-based precipitation estimates to ground truth.

ErrorMetrics

Compute quality metrics from a set of observations (obs) and estimates (est).

PolarNeighbours

For a set of projected point coordinates, extract the neighbouring bin values from a data set in polar coordinates.

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 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.

corr()[source]#

Correlation coefficient

r2()[source]#

Coefficient of determination

spearman()[source]#

Spearman rank correlation coefficient

nash()[source]#

Nash-Sutcliffe Efficiency

sse()[source]#

Sum of Squared Errors

mse()[source]#

Mean Squared Error

rmse()[source]#

Root Mean Squared Error

mas()[source]#

Mean Absolute Error

meanerr()[source]#

Mean Error

ratio()[source]#

Mean ratio between observed and estimated

pbias()[source]#

Percent bias

all()[source]#

Returns a dictionary of all error metrics

pprint()[source]#

Pretty prints a summary of error metrics

class wradlib.verify.PolarNeighbours(r, az, site, crs, x, y, *, nnear=9)[source]#

Bases: object

For a set of projected point coordinates, extract the neighbouring bin values from a data set in polar coordinates.

Use as follows:

First, create an instance of PolarNeighbours by passing all the information needed to georeference the polar radar data to the points of interest (see parameters).

Second, use the method extract in order to extract the values from a data array which corresponds to the polar coordinates.

Parameters

Examples

See Routine verification measures for radar-based precipitation estimates.

extract(vals)[source]#

Extracts the values from an array of shape (azimuth angles, range gages) which correspond to the indices computed during initialisation

Parameters

vals (numpy.ndarray) – array of shape (…, number of azimuth, number of range gates)

Returns

output (numpy.ndarray) – array of shape (…, number of points, nnear)

get_bincoords()[source]#

Returns all bin coordinates in map projection

Returns

output (tuple) – array of x coordinates, array of y coordinates

get_bincoords_at_points()[source]#

Returns bin coordinates only in the neighbourhood of points

Returns

output (tuple) – array of x coordinates, array of y coordinates