Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

doi:10.1007/s00376-020-9171-1

	Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble
	Xu, Zhizhen 2,3,4; Chen, Jing 5; Jin, Zheng 1; Li, Hongqi 5; Chen, Fajing 5
刊名	ADVANCES IN ATMOSPHERIC SCIENCES
	2020-04-01
卷号	37 期号:4 页码:328-346
关键词	ensemble prediction model uncertainty stochastically perturbed parameterization multi-stochastic physics approaches
ISSN号	0256-1530
DOI	10.1007/s00376-020-9171-1
通讯作者	Chen, Jing(chenj@cma.gov.cn)
英文摘要	To represent model uncertainties more comprehensively, a stochastically perturbed parameterization (SPP) scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes, as well as the stochastically perturbed parameterization tendencies (SPPT) scheme, and the stochastic kinetic energy backscatter (SKEB) scheme, is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS) to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes. Six experiments are performed for a summer month (1-30 June 2015) over China and multiple verification metrics are used. The results show that: (1) All stochastic experiments outperform the control (CTL) experiment, and all combinations of stochastic parameterization schemes perform better than the single SPP scheme, indicating that stochastic methods can effectively improve the forecast skill, and combinations of multiple stochastic parameterization schemes can better represent model uncertainties; (2) The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill; (3) Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed. SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields, and it contributes most to the improvement in spread and outliers for wind; (4) The introduction of SPP has a positive added value, and does not lead to large changes in the evolution of the kinetic energy (KE) spectrum at any wavelength; (5) The introduction of SPPT and SKEB would cause a 5%-10% and 30%-80% change in the KE of mesoscale systems, and all three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the KE of mesoscale systems. This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.
资助项目	National Key Research and Development (R & D) Program of China[2018YFC15 07405]
WOS关键词	CONVECTIVE PARAMETERIZATION ; PREDICTION SYSTEM ; BOUNDARY-LAYER ; PRECIPITATION ; SENSITIVITY ; ERROR ; ECMWF ; VERIFICATION ; IMPACT ; SCHEME
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	SCIENCE PRESS
WOS记录号	WOS:000522645000003
资助机构	National Key Research and Development (R & D) Program of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/133256]
专题	中国科学院地理科学与资源研究所
通讯作者	Chen, Jing
作者单位	1.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Fudan Univ, Dept Atmospher & Ocean Sci, Shanghai 200438, Peoples R China 3.Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China 4.China Meteorol Adm, Chinese Acad Meteorol Sci, Beijing 100081, Peoples R China 5.China Meteorol Adm, Numer Weather Predict Ctr, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Xu, Zhizhen,Chen, Jing,Jin, Zheng,et al. Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble[J]. ADVANCES IN ATMOSPHERIC SCIENCES,2020,37(4):328-346.
APA	Xu, Zhizhen,Chen, Jing,Jin, Zheng,Li, Hongqi,&Chen, Fajing.(2020).Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble.ADVANCES IN ATMOSPHERIC SCIENCES,37(4),328-346.
MLA	Xu, Zhizhen,et al."Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble".ADVANCES IN ATMOSPHERIC SCIENCES 37.4(2020):328-346.