ResU‐Deep: Improving the Trigger Function of Deep Convection in Tropical Regions With Deep Learning

Abstract

Modeling deep convection accurately in tropical regions is important. However, biases remain in current trigger functions. To alleviate the overestimation of frequency and wrong depiction of the diurnal cycle, we propose a deep convection trigger function, ResU‐Deep, based on the framework of U‐net with three modifications to better suit the problem of deep convection identification: (a) adding the upsampling process into the encoder part, (b) replacing the double convolution block with a residual‐convolutional block, and (c) adding a dynamic weight into the loss function. Thirty‐three environmental variables within tropical regions are used in ResU‐Deep, including 31 features from ECMWF atmospheric reanalysis (ERA5) data set, and two historical convection fields. Tropical Rainfall Measuring Mission 3B42 data set is used as the precipitation observation. Central America, North Africa, South and East Asia, and West Pacific Ocean within 0°∼30°N are selected as the study regions for the high frequency of deep convection activities. ResU‐Deep, incorporating the surrounding information, is separately trained and evaluated in four regions and has the F1‐scores of 58%, 53%, 60%, and 63% for the occurrence, outperforming the single‐column‐based machine learning methods. Also, a unified model has similar performance in four regions. Further comparisons are made with convective available potential energy‐based trigger functions in Southern Great Plains. Results show that ResU‐Deep can capture the trends and peaks of diurnal cycles on complex terrains in large regions. According to feature importance test, the contribution levels of environmental features are different in four regions, indicating the model can learn the mechanisms of deep convection in specific region, thus improving the prediction accuracy.

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“…VanillaNet, a lightweight neural network architecture that emphasizes simplicity, was introduced by Huawei’s Noah’s Ark Lab ( Chen et al., 2023 ). By avoiding complexities like excessive depth, shortcuts, and self-attention mechanisms, VanillaNet achieves a balance of simplicity and performance.…”