wradlib.adjust.AdjustMultiply

class wradlib.adjust.AdjustMultiply(obs_coords, raw_coords, nnear_raws=9, stat='median', mingages=5, minval=0.0, mfb_args=None, ipclass=<class 'wradlib.ipol.Idw'>, **ipargs)

Gage adjustment using a multiplicative error model

First, an instance of AdjustMultiply has to be created. Calling this instance then does the actual adjustment. The motivation behind this performance. In case the observation points are always the same for different time steps, the computation of neighbours and inverse distance weights only needs to be performed once during initialisation.

AdjustMultiply 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.

Note

Inherits from wradlib.adjust.AdjustBase

For a complete overview of parameters for the initialisation of adjustment objects, as well as an extensive example, please see wradlib.adjust.AdjustBase.

Returns:

output (numpy.ndarray) – array of adjusted radar values

__init__(obs_coords, raw_coords, nnear_raws=9, stat='median', mingages=5, minval=0.0, mfb_args=None, ipclass=<class 'wradlib.ipol.Idw'>, **ipargs)

Methods

__init__(obs_coords, raw_coords[, ...])
xvalidate(obs, raw)	Leave-One-Out Cross Validation, applicable to all gage adjustment classes.