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wradlib Documentation, Release 0.1.1 >>> line2 = pl.plot(obs_coords, obs, ’ro’, label="gage obs") >>> line3 = pl.plot(radar_coords, add_adjusted, ’-’, color="red", label="adjusted by AdjustAdd") >>> line4 = pl.plot(radar_coords, mult_adjusted, ’-’, color="green", label="adjusted by AdjustMu >>> line5 = pl.plot(radar_coords, mfb_adjusted, ’-’, color="orange", label="adjusted by AdjustMF >>> line6 = pl.plot(radar_coords, mixed_adjusted, ’-’, color="blue", label="adjusted by AdjustMi >>> pl.legend() >>> pl.show() Methods xvalidate(obs, raw) Leave-One-Out Cross Validation, applicable to all gage adjustment classes. 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 __call__(obs, raw[, targets, rawatobs, ix]) xvalidate(obs, raw) Return the field of raw values adjusted by obs. Leave-One-Out Cross Validation, applicable to all gage adjustment classes. wradlib.adjust.AdjustAdd.__call__ AdjustAdd.__call__(obs, raw, targets=None, rawatobs=None, ix=None) Return the field of raw values adjusted by obs. Parameters obs : array of floats Gage observations raw : array of floats Raw unadjusted radar rainfall targets : (INTERNAL) array of floats Coordinate pairs for locations on which the final adjustment product is interpolated Defaults to None. In this case, the output locations will be identical to the radar coordinates rawatobs : (INTERNAL) array of floats 74 Chapter 3. Library Reference
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
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