Estimation of rain parameters from spectral moments of L-band wind profiler using Multi-Layer Perceptron Network model

Two Multi-Layer Perceptron (MLP) models are developed to estimate the radar reflectivity factor (dBZ) and rain intensity (R) from the spectral moments of an L-band wind profiler. Out of the four spectral moment inputs of the MLP models, the backscattered power (P) and Doppler velocity (VD) were found to have better correlation with the rain parameters. The model results were validated with the Joss Waldvogel Disdrometer (JWD) observations. For the training and validation data sets of dBZ, the root mean square error (rmse) of estimated and observed data sets were found to be 5.05 and 5.32 dBZ with correlation coefficients of 0.90 and 0.86, respectively. Similarly, for the training and validation data sets of R, the rmse for estimated and observed values were found to be 4.27 and 7.74 mmh respectively with correlation coefficients of 0.95 and 0.78. The developed models were validated with a rain event on 22 June 2000, that consisted of rain from both convective and stratiform regimes. The error between the estimated and observed rain accumulation was found to be ∼ 5%. The height profiles of estimated dBZ were able to identify the bright band during stratiform rain by virtue of high values of reflectivity gradient at around 4.0 km height. Though ambiguity in the estimation of R was observed at bright band level, overall estimated parameters were in good agreement with general characteristics of convective and stratiform rain.