集合、同化思想在大气科学中的渗透  

Application and Progress of Ensemble and Assimilation for the Atmospheric Science

作　　者：薛建军[1,2] 贾朋群 肖子牛[2] Xue Jianjun;Jia Pengqun;Xiao Ziniu(China Meteorological Administration Training Centre,Beijing 100081;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,Institute ofAtmospheric Physics,Chinese Academy of Sciences,Beijing 100029)

机构地区：[1]中国气象局气象干部培训学院,北京100081 [2]中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京100029

出　　处：《气象科技进展》2022年第6期64-72,共9页Advances in Meteorological Science and Technology

基　　金：国家自然科学基金(42142009);中国气象局软科学研究重点项目(2022ZDIANXM20)。

摘　　要：模式误差、初始误差以及非线性系统的不稳定性共同导致了大气、海洋及其耦合系统预报预测的不确定。为了尽可能减少这些误差对数值预报的影响,集合、同化的思想和方法“应需而生”并得以在现代天气气候预报预测中不断发展应用。本文简要回顾了集合预报、资料同化的理论方法的主要发展历程及应用,介绍了ECMWF等机构在相关领域里的一些新的研发方向及进展。随着相关理论和技术的日臻成熟,传统方法短期内难以取得新的重大进展,而以人工智能等为代表的新兴技术与气象的融合应用受到关注并有望取得突破。The combined effects of model errors,initial condition errors and the instability of nonlinear systems lead to uncertainties in forecasting for the atmosphere,ocean and their coupled systems.In order to reduce the above negative effects on numerical weather prediction as much as possible,the methods of ensemble and assimilation have been proposed and continuously developed and applied in modern weather and climate prediction.This paper reviews the histories and applications of the main theories and methods for ensemble forecasting and data assimilation.We also introduce some progress and academic frontiers carried out by relevant institutions such as ECMWF.With the development of the theories and methods,limited space is left for traditional methods to achieve significant progress in the short term,while the fusion of emerging technologies such as artificial intelligence(AI)has attracted widespread attention and is expected to make breakthroughs.

关 键 词：数值预报 集合 同化 人工智能 气象 

分 类 号：P456.7[天文地球—大气科学及气象学]