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.

Zhizhen XU;Jing CHEN;Mu MU;Guokun DAI;Yanan MA

Department of Atmospheric and Oceanic Sciences& Institute of Atmospheric Sciences, Fudan University,Shanghai 200438,China;Numerical Weather Prediction Center,China Meteorological Administration,Beijing 100081,China;Chinese Academy of Meteorological Sciences,China Meteorological Administration,Beijing 100081,China

大气科学进展（英文版）

[1]Zhizhen XU;Jing CHEN;Mu MU;Guokun DAI;Yanan MA-.A Nonlinear Representation of Model Uncertainty in a Convective-Scale Ensemble Prediction System)[J].大气科学进展（英文版）,2022(09):1432-1450

Allowing,CAEPS,NFSV,CAEPSs

Nonlinear,Representation,Model,Uncertainty,Convective,Scale,Ensemble,Prediction,System,How,accurately,address,model,uncertainties,consideration,rapid,nonlinear,growth,characteristics,convection,allowing,system,crucial,issue,performing,scale,ensemble,forecasts,In,this,study,new,perturbation,technique,convective,developed,representation,errors,Global,Regional,Assimilation,Enhanced,GRAPES,Convection,forcing,singular,vector,approach,that,conditional,optimal,CNOP,applied,construct,Three,experiments,performed,One,them,CTL,without,adding,any,other,two,perturbed,which,by,different,groups,radii,test,sensitivity,magnitude,Verification,results,show,achieve,overall,improvement,produce,more,skillful,compared,indicates,can,used,effective,Additionally,large,constraints,generally,performs,better,than,small,verification,upper,air,surface,weather,variables,But,precipitation,light,heavier,indicating,events,must,carefully,selected,findings,above,lay,foundation,design,methods,future

0.449891