wradlib Documentation - Bitbucket

wradlib Documentation, Release 0.1.1
For internal use from AdjustBase.xvalidate only (defaults to None)
ix : (INTERNAL) array of integers
For internal use from AdjustBase.xvalidate only (defaults to None)
wradlib.adjust.AdjustAdd.xvalidate
AdjustAdd.xvalidate(obs, raw)
Leave-One-Out Cross Validation, applicable to all gage adjustment classes.
This method will be inherited to other Adjust classes. It should thus be applicable to all adjustment procedures
without any modification. This way, the actual adjustment procedure has only to be defined once in the __call__
method.
The output of this method can be evaluated by using the verify.ErrorMetrics class.
Parameters obs : array of floats
raw : array of floats
Returns obs : array of floats
valid observations at those locations which have a valid radar observation
estatobs : array of floats
estimated values at the valid observation locations
wradlib.adjust.AdjustMixed
class wradlib.adjust.AdjustMixed(obs_coords, raw_coords, nnear_raws=9, stat=’median’, mingages=5,
minval=0.0, Ipclass=, **ipargs)
Gage adjustment using a mixed error model (additive and multiplicative).
The mixed error model assumes that you have both a multiplicative and an additive error term. The intention
is to overcome the drawbacks of the purely additive and multiplicative approaches (see AdjustAdd and Adjust-
Multiply). The formal reprentation of the error model according to [Pfaff2010] is:
R(gage) = R(radar) * (1+delta) + epsilon
delta and epsilon have to be assumed to be independent and normally distributed. The present implementation is
based on a Least Squares estimation of delta and epsilon for each rain gage location. delta and epsilon are then
interpolated and used to correct the radar rainfall field. The least squares implementation uses the equation for
the error model plus the condition to minimize (delta**2 + epsilon**2) for each gage location. The idea behind
this is that epsilon dominates the adjustment for small deviations between radar and gage while delta dominates
in case of large deviations.
Usage: First, an instance of AdjustMMixed has to be created. Calling this instance then does the actual adjustment.
The motivation behind this is performance. In case the observation points are always the same for
different time steps, the computation of neighbours and invserse distance weights only needs to be performed
once during initialisation.
AdjustMixed automatically takes care of invalid gage or radar observations (e.g. NaN, Inf or other typical missing
data flags such as -9999. However, in case e.g. the observation data contain missing values, the computation
of the inverse distance weights needs to be repeated in __call__ which is at the expense of performance.
Parameters obs_coords : array of float
coordinate pairs of observations points
3.13. Gage adjustment 75