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Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable.

This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution.

The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.