A stochastic and non‐linear representation of model uncertainty in a convective‐scale ensemble prediction system

Abstract

AbstractAccurately addressing model uncertainties with a consideration of the enhanced effect of non‐linearities in a high‐resolution convective‐scale system is a crucial issue for performing convection‐allowing ensemble prediction systems (CAEPSs). In this study, a conditional non‐linear–stochastic perturbation method is developed to simultaneously consider both a stochastic and a non‐linear representation of model uncertainties associated with physics parameterization in the Global and Regional Assimilation and Prediction Enhanced System (GRAPES)‐CAEPS with a horizontal resolution of 3 km. The non‐linear forcing singular vector (NFSV) for a non‐linear representation of model uncertainties and the Stochastically Perturbed Parameterization Tendencies (SPPT) scheme for a stochastic representation of model uncertainties, are applied. Two experiments were carried out over South China for a month (May 1–30, 2020), one with a SPPT scheme and the other with a non‐linear–stochastic perturbation using a combination of SPPT and NFSV schemes. The combination of SPPT and NFSV schemes is compared with the SPPT scheme alone to investigate whether the conditional non‐linear–stochastic perturbation method that combines non‐linear and stochastic schemes can represent model uncertainty better than the traditional stochastic SPPT approach. The results show that combining the NFSV and SPPT schemes improves the overall probabilistic skill and has an advantage over the SPPT scheme, which may imply that adding additional state‐independent non‐linear noise contributes to a more comprehensive characterization of model error for representing model uncertainties in CAEPSs. This discovery sheds light on the design and development of model perturbation strategies for convective‐scale ensembles in the future.