The Regularized WSM6 Microphysical Scheme and Its Validation in WRF 4D-Var

被引:5
作者
Yang, Sen [1 ]
Li, Deqin [1 ]
Chen, Liqiang [1 ]
Liu, Zhiquan [2 ]
Huang, Xiang-Yu [3 ]
Pan, Xiao [1 ]
机构
[1] China Meteorol Adm, Inst Atmospher Environm, Shenyang 100166, Peoples R China
[2] Natl Ctr Atmospher Res, Boulder, CO 80301 USA
[3] China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China
基金
中国国家自然科学基金;
关键词
4D-Var; data assimilation; linearization; numerical weather prediction; WSM6; VARIATIONAL DATA ASSIMILATION; ECMWF OPERATIONAL IMPLEMENTATION; PART I; MIDLATITUDE CYCLONES; MICROSCALE STRUCTURE; NAVDAS-AR; ADJOINT; SYSTEM; MODEL; PARAMETERIZATION;
D O I
10.1007/s00376-022-2058-6
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
A cold cloud assimilation scheme was developed that fully considers the water substances, i.e., water vapor, cloud water, rain, ice, snow, and graupel, based on the single-moment WSM6 microphysical scheme and four-dimensional variational (4D-Var) data assimilation in the Weather Research and Forecasting data assimilation (WRFDA) system. The verification of the regularized WSM6 and its tangent linearity model (TLM) and adjoint mode model (ADM) was proven successful. Two groups of single observation and real sounding data assimilation experiments were set up to further verify the correctness of the assimilation scheme. The results showed that the consideration of ice, snow, and graupel in the assimilation system of the 4D-Var, as opposed to their omission in the warm rain Kessler scheme, allowed the water substances to be reasonably updated, further improving the forecast. Before it can be further applied in the assimilation of observational data, radar reflectivities, and satellite radiances, the cold cloud assimilation scheme needs additional verification, including using conventional ground and sounding observations in the 4D-Var assimilation system.
引用
收藏
页码:483 / 500
页数:18
相关论文
共 76 条
[1]  
Chen F, 2001, MON WEATHER REV, V129, P569, DOI 10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO
[2]  
2
[3]   Diurnally varying background error covariances estimated in RMAPS-ST and their impacts on operational implementations [J].
Chen, Yaodeng ;
Fang, Kuiming ;
Chen, Min ;
Wang, Hongli .
ATMOSPHERIC RESEARCH, 2021, 257
[4]   A STRATEGY FOR OPERATIONAL IMPLEMENTATION OF 4D-VAR, USING AN INCREMENTAL APPROACH [J].
COURTIER, P ;
THEPAUT, JN ;
HOLLINGSWORTH, A .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 1994, 120 (519) :1367-1387
[5]   Generalized background error covariance matrix model (GEN_BE v2.0) [J].
Descombes, G. ;
Auligne, T. ;
Vandenberghe, F. ;
Barker, D. M. ;
Barre, J. .
GEOSCIENTIFIC MODEL DEVELOPMENT, 2015, 8 (03) :669-696
[6]  
DUDHIA J, 1989, J ATMOS SCI, V46, P3077, DOI 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO
[7]  
2
[8]   Cloud Microphysical Factors Affecting Simulations of Deep Convection During the Presummer Rainy Season in Southern China [J].
Furtado, Kalli ;
Field, Paul R. ;
Luo, Yali ;
Liu, Xi ;
Guo, Zhun ;
Zhou, Tianjun ;
Shipway, Benjamin J. ;
Hill, Adrian A. ;
Wilkinson, Jonathan M. .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2018, 123 (18) :10477-10505
[9]  
Gauthier P, 2001, MON WEATHER REV, V129, P2089, DOI 10.1175/1520-0493(2001)129<2089:IOTDFA>2.0.CO
[10]  
2