Journal Article

A Neural-network-based Alternative Scheme to Include Nonhydrostatic Processes in an Atmospheric Dynamical Core

Li, Lijuan, Liu, Li, Xia, Yang, Liu, Hongbo, Huang, Wenyu, Li, Jianghao, Dong, Li, Pu, Ye, Wang, Yong, Wang, Bin, Li, Yiyuan, Xu, Shiming, He, Yujun, Liu, Juanjuan, Xia, Kun, Xia, Wenwen

2024

Overview

Here, a nonhydrostatic alternative scheme (NAS) is proposed for the grey zone where the nonhydrostatic impact on the atmosphere is evident but not large enough to justify the necessity to include an implicit nonhydrostatic solver in an atmospheric dynamical core. The NAS is designed to replace this solver, which can be incorporated into any hydrostatic models so that existing well-developed hydrostatic models can effectively serve for a longer time. Recent advances in machine learning (ML) provide a potential tool for capturing the main complicated nonlinear-nonhydrostatic relationship. In this study, an ML approach called a neural network (NN) was adopted to select leading input features and develop the NAS. The NNs were trained and evaluated with 12-day simulation results of dry baroclinic-wave tests by the Weather Research and Forecasting (WRF) model. The forward time difference of the nonhydrostatic tendency was used as the target variable, and the five selected features were the nonhydrostatic tendency at the last time step, and four hydrostatic variables at the current step including geopotential height, pressure in two different forms, and potential temperature, respectively. Finally, a practical NAS was developed with these features and trained layer by layer at a 20-km horizontal resolution, which can accurately reproduce the temporal variation and vertical distribution of the nonhydrostatic tendency. Corrected by the NN-based NAS, the improved hydrostatic solver at different horizontal resolutions can run stably for at least one month and effectively reduce most of the nonhydrostatic errors in terms of system bias, anomaly root-mean-square error, and the error of the wave spatial pattern, which proves the feasibility and superiority of this scheme.

• Publisher: Science Press,Springer Nature B.V,College of Ocean Sciences,University of Chinese Academy of Sciences,Beijing 100049,China,Innovation Group 311020008,Southern Marine Science and Engineering Guangdong Laboratory,Zhuhai 519000,China,College of Ocean Sciences,University of Chinese Academy of Sciences,Beijing 100049,China%State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China%Ministry of Education Key Laboratory for Earth System Modeling,and Department of Earth System Science,Tsinghua University,Beijing 100084,China%Key Laboratory of Earth System Modeling and Prediction,China Meteorological Administration,Beijing 100081,China%State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China,Shanghai Ecological Forecasting and Remote Sensing Center,Shanghai 200030,China%State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China,Ministry of Education Key Laboratory for Earth System Modeling,and Department of Earth System Science,Tsinghua University,Beijing 100084,China
• Subject: Approximation / Atmospheric Sciences / Dynamic height / Earth and Environmental Science / Earth Sciences / Efficiency / Geophysics/Geodesy / Geopotential / Geopotential height / Laboratories / Machine learning / Meteorology / Neural networks / Original Paper / Potential temperature / Solvers / Temporal variability / Temporal variations / Vertical distribution / Weather forecasting