Satellite‐Based Fully Connected Neural Network Heating (FCNH) Algorithm for Estimating Latent Heating Rate Inside Storms

Abstract

AbstractLatent heat (LH) released from precipitation during the water phase change process is the primary energy source driving atmospheric circulation. Current satellite LH retrieval algorithms are mainly physical‐based or lookup table‐based. In this study, a fully connected neural network LH algorithm (FCNH) was developed and tested by weather research and forecasting model (WRF) simulations and global precipitation measurement (GPM) satellite observations. FCNH uses three types of modules: feature representation, feature fusion, and regression. Using satellite observable vertical derivation of precipitation rate () and air temperature (T) as inputs into FCNH achieved the best LH retrieval performance; increasing the number of input variables covering environmental or precipitation characteristics degraded the retrieval accuracy. Compared to the WRF simulated true LH, the FCNH retrieval captured the main features of horizontal and vertical structures with high correlation coefficients and showed improved performance over the associated physical‐based LH algorithm on the same inputs. The FCNH algorithm can alleviate the overestimation of cooling near the surface and the overestimation of positive heating in the mixing layer. The LH retrievals from FCNH and the other three algorithms using inputs of GPM observations were compared, and all achieved basically consistent results. This study is the first attempt to use an artificial neural network method for satellite remote sensing of LH inside precipitation clouds. It promotes understanding of the learning efficiency, accuracy, and limitations of using a fully connected neural network to retrieve LH.