A new method to retrieve rainfall intensity level from rain-contaminated X-band marine radar image

In this study, a new methodcombining wave-number energy spectrum (WES) and Genetic algorithm-back propagation neural network (GA-BPNN) isproposed to retrieve the rainfall intensity level from rain-contaminated X-bandmarine radar image. Since the intensity of spatialrainfall can be reflected by the distribution of energy in the wavenumberfrequency domain, the obtained WES is divided into three wavenumber segments(low, medium and high wavenumber segments), and the ratio of the wavenumber ineach wavenumber segment to the total wavenumber is calculated separately as the characteristicparameters. Based on the excellent networkconvergence speed and data prediction accuracy of the GA-BPNN, these calculatedparameters are input into the constructed GA-BPNN for training to complete thetask of rainfall intensity level retrieval. The proposed method is tested usingdata collected at the ocean observation station of Haitan Island in PingtanCounty. Referring to the actual rainfallintensity synchronously recorded by the rain gauge, the retrieval accuracy ofthe proposed method is 97.4%, which is 4.3% higher than that of back propagation neural network (BPNN) not optimizedby Geneticalgorithm(GA). In addition, compared with theretrieval performance of the ratio of zero intensity to echo (RZE) method basedon the occlusion area of radar image, the retrievalaccuracy of the proposed method is improved by about 12.9%.