Classification of convective/stratiform echoes in radar reflectivity observations using a fuzzy logic algorithm

Abstract

AbstractIn radar reflectivity observations, the convective and stratiform rain types always have poorly defined boundaries, which caused problem for rain classification. A fuzzy logic (FL) algorithm is developed to classify convective and stratiform rainfall based on the radar reflectivity observations in the next three steps: First, the algorithm is calibrated on Hefei Doppler radar site in China. Four features are selected based on a dataset for calibration, which spanned the period from 29 June to 23 July 2003; and the features basically represent a subjective choice of characteristics that are expected to distinguish different rain types. In the second step, membership functions are used to determine the degree to which each feature belongs to each rain type in the fuzzification process. Finally, the degree of fuzzification for each input feature is multiplied by predetermined weighting coefficients. The weighted degrees of the fuzzification are aggregated to produce a single value for each rain type. The aggregation can represent the possibility of classified rainfall type, and larger value reveals the higher potential for a particular class. The FL algorithm has been applied to four typical independent individual cases collected by Hefei Doppler radar, which have not been included in the database for calibration. Results show that the classification using the proposed FL algorithm is physically reasonable according to the analysis of three‐dimensional radar reflectivity patterns, implying that the proposed FL algorithm has a great potential for the precipitation classification.