摘要
中尺度天气现象对人类的生产生活有重要影响,中尺度数值预报模式是进行数值天气预报的主要工具。受模式分辨率和对天气现象物理机制认识不足的限制,许多复杂天气过程只能用参数化方案来隐式表达,对参数化方案进行研究有助于推动中尺度数值预报模式的模拟和预报效果的不断优化。在探讨国内外典型中尺度数值预报模式特性的基础上,综述了积云对流参数化方案、云微物理参数化方案、边界层参数化方案、陆面过程参数化方案和辐射传输过程参数化方案的研究进展,比较了代表性参数化方案的理论基础和应用场景,发现对独立或组合参数化方案效果的对比试验和改进试验是该领域主要的研究范式。认为参数化方案未来将走向深入探讨各种天气现象的影响因素和物理机制,多要素、多尺度和多方案耦合,更加重视对“灰色区域”的模拟,应用选择更加多元化,并与机器学习技术融合的发展模式。
Mesoscale weather phenomena have a significant impact on human production and life.Mesoscale numerical prediction models are one of the main tools used for numerical weather prediction.Owing to the limitations of model resolution and insufficient understanding of the physical mechanisms of weather phenomena,many complex weather processes can only be implicitly expressed by parameterization schemes,the research of which is helpful in promoting the continuous optimization of the simulation and prediction effects of mesoscale numerical prediction models.Based on the characteristics of typical mesoscale numerical prediction models at home and abroad,the research status of cumulus convective,cloud microphysical,planetary boundary layer,land surface process,and radiation transfer process parameterization schemes,which are important according to the main forms of atmospheric motion,have been summarized.The theoretical basis and application scenarios of representative parameterization schemes are also compared in the present study.The main research paradigms in this field are comparative experiments that include the comparison of different scheme effects in simulating the same weather phenomenon,comparison of the same scheme effects in different weather scenarios,and optimization experiments on independent or combined schemes.In the future,the physical mechanisms of various weather phenomena and their influencing factors will be explored in depth,and parameterization schemes will be coupled at the levels of multiple elements,scales,and schemes.Subsequently,the simulation of the gray area will receive more attention,and the application options of parameterization schemes will be more diversified.Owing to the arrival of the big data era and the high demand for data analysis,parameterization schemes and machine learning will be developed to form a new mechanism driven by methods and data.
作者
刘东海
黄静
刘娟
周扬
秦昆
LIU Donghai;HUANG Jing;LIU Juan;ZHOU Yang;QIN Kun(School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China;Beijing Institute of Applied Meteorology,Beijing 100029,China)
出处
《地球科学进展》
CAS
CSCD
北大核心
2023年第4期349-362,共14页
Advances in Earth Science
基金
国家自然科学基金项目“全球尺度地理多元流的网络化挖掘及关联分析”(编号:42171448)资助
关键词
中尺度数值预报模式
参数化方案
气象预报
气象物理机制
机器学习
Mesoscale numerical prediction model
Parameterization schemes
Meteorological forecast
Meteorological physical mechanism
Machine learning.
作者简介
刘东海(2000-),男,山西偏关人,硕士研究生,主要从事时空大数据分析研究.E-mail:ldhwhdx@whu.edu.cn;通信作者:秦昆(1972-),男,湖北随州人,教授,主要从事时空大数据分析研究.E-mail:qink@whu.edu.cn
