Classification and quantification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems and in situ verification

Fuzzy logic and Neuro-Fuzzy systems for the classification of hydrometeor type based on polarimetric radar measurements is developed. The hydrometeor classification system is implemented where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the fuzzy sets in the fuzzy logic system. Five radar measurements, namely, horizontal reflectivity (Z_H ), differential reflectivity (Z_DR), differential propagation phase shift (K_DP), correlation coefficient (ρ_HV(0)), and linear depolarization ratio (LDR), and corresponding altitude have been used as input variables to the hydrometeor classifier. The output is one of the many possible hydrometeor types, namely (1) drizzle, (2) rain, (3) dry and low density snow, (4) dry and high density crystals, (5) wet and melting snow, (6) dry graupel, (7) wet graupel, (8) small hail, (9) large hail, and (10) mixture of rain and hail. The Neuro-Fuzzy classifier is more advantageous than a simple Neural Network or a fuzzy logic classifier because it is transparent rather than a “black box” (unlike a neural network), and can learn the parameters of the system from the past data (unlike a fuzzy logic system). The Neuro-Fuzzy hydrometeor classifier has been applied to several case studies and the results are compared against in-situ observations.; A novel scheme of adaptively updating the structure and parameters of the neural network for rainfall estimation is presented. This adaptive neural network scheme enables the network to implement the nonstationary relationship between radar measurements and precipitation estimation with change of season and other environment conditions, and also can incorporate new information, without re-training the complete network from the beginning. It was shown that the adaptive neural network is much faster, more efficient and convenient for real time rainfall estimation to be used with WSR-88D.; Another important issue for the application of radar rainfall algorithm is the detection of rain/no-rain conditions on the ground. Vertical reflectivity profiles of radar observations are used as input variables to the rain/no-rain determination. Radar data and ground raingage measurements are used to train the neural network. Results indicate that rain/no-rain conditions on the ground can be inferred from the procedure developed in this paper fairly accurately. It is shown that by using rain/no-rain classification scheme the accuracy of rainfall accumulation estimates can be improved greatly. (Abstract shortened by UMI.)...