A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System  

作　　者：Zhizhen XU Jing CHEN Mu MU Guokun DAI Yanan MA 

机构地区：[1]Department of Atmospheric and Oceanic Sciences&Institute of Atmospheric Sciences,Fudan University,Shanghai 200438,China [2]Numerical Weather Prediction Center,China Meteorological Administration,Beijing 100081,China [3]Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081,China

出　　处：《Advances in Atmospheric Sciences》2022年第9期1432-1450,共19页大气科学进展（英文版）

基　　金：supported by the National Key Research and Development (R&D) Program of the Ministry of Science and Technology of China (Grant No. 2021YFC3000902)

摘　　要：How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts.In this study,a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System(GRAPES)Convection-Allowing Ensemble Prediction System(CAEPS).The nonlinear forcing singular vector(NFSV)approach,that is,conditional nonlinear optimal perturbation-forcing(CNOP-F),is applied in this study,to construct a nonlinear model perturbation method for GRAPES-CAEPS.Three experiments are performed:One of them is the CTL experiment,without adding any model perturbation;the other two are NFSV-perturbed experiments,which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint.Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment,which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts.Additionally,the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables.But for precipitation verification,the NFSV-S experiment performs better in forecasts for light precipitation,and the NFSV-L experiment performs better in forecasts for heavier precipitation,indicating that for different precipitation events,the perturbation magnitude constraint must be carefully selected.All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.

关 键 词：Convection-Allowing Ensemble Prediction System model uncertainty nonlinear forcing singular vector 

分 类 号：P45[天文地球—大气科学及气象学]