Research on Reference State Deduction Methods of Different Dimensions  

作　　者：Yong SU Xueshun SHEN Hongliang ZHANG Xingliang LI 

机构地区：[1]CMA Earth System Modeling and Prediction Centre,China Meteorological Administration(CMA),Beijing,100081,China

出　　处：《Journal of Meteorological Research》2025年第1期100-115,共16页气象学报(英文版)

基　　金：Supported by the National Natural Science Foundation of China(42090032 and 42275168).

摘　　要：The atmospheric motion is inherently nonlinear.The high-impact weather events that people concern are generally determined by small-and medium-scale systems overlaid on the large-scale circulation.The accumulation of seemingly minor computational errors can significantly impact the model’s predictive capabilities.When solving these equations,the flow field is commonly separated into basic flow and perturbation flow through the introduction of a reference state.This approach solves the problem of“small differences between large numbers”in terms such as the pressure gradient force(PGF)and improves the spatial discretization accuracy of the model.This paper first reviews the development of zero-dimensional(0D),one-dimensional(1D),two-dimensional(2D),three-dimensional(3D),and four-dimensional(4D)reference state deduction methods.Then,it details the implementation of these different dimensional reference state deduction methods within the context of the Global Regional Assimilation and Prediction System Global Forecast System(GRAPESGFS)model of China Meteorological Administration(CMA).Furthermore,the accuracy of the different dimensional reference states is tested through multiple benchmark tests.The results demonstrate that the high-dimensional reference state provides a closer approximation to the real atmosphere across various altitudes and latitudes,resulting in a more comprehensive and effective improvement in discretization accuracy.Finally,the paper offers suggestions on issues related to reference state deduction.

关 键 词：Global Regional Assimilation and Prediction System(GRAPES) reference state pressure gradient force(PGF) LINEARIZATION 

分 类 号：O17[理学—数学]