For example, horizontally and vertically polarized electric fields interact differently with contrasting hydrometeors, such as rain or hail, to produce tell-tale signatures in the fields of polarimetric variables. NSSL began accumulating a gamut of polarimetric radar data in 1992. Soon thereafter, in collaboration with the University of Oklahoma, we began to develop an automatic procedure for discrimination of hydrometeors. For a given precipitation type, the polarimetric variables cluster in a specific region. The crux of the classification process is to separate overlapping clusters so that the probability of correct classification is high, while the probability of mis-classification is low.
Classification is based upon weights assigned to the various multiparameter variables.
The choice of weights is founded on previous measurements, physical reasoning, modeling, and sometimes gut feeling. The example in the figure at the top of the page is well-suited to demonstrate the potential of polarimetry for the classification of hydrometeors. This, we believe, is a fundamental condition for accurate determination of precipitation amounts. First, a correct classification needs to be made, and then, appropriate semi-empirical relations should be applied to each class to estimate the corresponding amounts. The results presented here and elsewhere are very promising, yet much testing and comparisons with in-situ measurements are required to evolve the algorithm into a useful tool.