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Stable speed for gmd speed time3/24/2024 In this study, a group of deep residual multilayer perceptrons with strong nonlinear fitting ability is designed to learn a parameterization scheme from cloud-resolving model outputs. In realistic configurated models, developing a machine learning parameterization scheme remains a challenging task. At present, most of the existing studies are on aqua-planet and idealized models, and the problems of simulated instability and climate drift still exist. In recent years, due to the rapid development of data science, Machine learning (ML) parameterizations for convection and clouds have been proven the potential to perform better than conventional parameterizations. In climate models, subgrid parameterizations of convection and cloud are one of the main reasons for the biases in precipitation and atmospheric circulation simulations.
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