Rain-Type Classification From Microwave Satellite Observations Using Deep Neural Network Segmentation

The understanding of different characteristics of the stratiform and convective system is important for meteorological research, including precipitation retrievals from satellite observations, precipitation parameterization in numerical prediction models, and precipitation climatology. In this study, the possibility of discriminating rain types using deep learning techniques is examined using satellite microwave observation data. Herein, we build two types of deep neural networks for rain type classification, i.e., U-net (RTC-U-net) and fully connected neural network (RTC-fcNN). Experiments are performed with the data obtained from two sensors onboard the global precipitation measurement (GPM) satellite. The brightness temperatures observed from the GPM microwave imager (GMI) are used as input features, whereas rain types retrieved by dual-frequency precipitation radar (DPR) are used as labels. We generated the merged 40-
$\times $
-40 pixel data for training and testing the data set by cropping the GMI observed fields overlapping the DPR observations. Overall, the proposed RTC-U-net and RTC-fcNN effectively classify four rain types, i.e., no-rain, stratiform, convective, and others. The total accuracy for the four rain types is 0.95, and the weighted mean of precision, recall, and f-score for stratiform and convective are 0.76, 0.68, and 0.71, respectively. Results show that the proposed techniques can extract the characteristics of each rain type from the data itself. To the best of our knowledge, this study is the first to apply a segmentation method with U-net for rain type classification.