A comparison of the stochastic and nonlinear representations of model uncertainty in a convection‐allowing ensemble prediction system

Abstract Comprehensively representing model uncertainties with a consideration of randomness and nonlinearity in physics parameterizations is a crucial issue in convection‐allowing ensemble prediction systems (CAEPSs). In this study, the nonlinear forcing singular vector (NFSV) for a nonlinear representation of model uncertainties and the stochastically perturbed parameterization tendencies (SPPT) scheme for a stochastic representation of model uncertainties, are evaluated and compared in the China Meteorological Administration (CMA)‐CAEPS with a horizontal resolution of 3 km. A conditional nonlinear–stochastic perturbation method is also used to consider both stochastic and nonlinear representations of model uncertainties. Three experiments were carried out over South China for a month (1–30 May 2020), one with an SPPT scheme, one with a NFSV scheme and another one with a nonlinear–stochastic perturbation using a combination of SPPT and NFSV schemes. The combination of SPPT and NFSV schemes is compared to the NFSV and SPPT scheme to investigate whether the conditional nonlinear–stochastic model perturbation method, which combines nonlinear and stochastic schemes, can better represent model uncertainty than the SPPT and NFSV approaches. The results show that the nonlinear perturbation NFSV scheme has a certain advantage over the SPPT scheme, and further combining the NFSV and SPPT schemes improves overall probabilistic forecasting skill and has an advantage over using only the NFSV or SPPT scheme, which may imply the positive impact of using the nonlinear model perturbation scheme in the CAEPSs, and that considering both a stochastic and a nonlinear representation of model uncertainties contributes to a more comprehensive representation of model error in CAEPSs. This discovery sheds light on the design and development of model perturbation strategies for future convective‐allowing ensemble prediction.