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

• Published in: Quarterly journal of the Royal Meteorological Society Vol. 148; no. 746; pp. 2507 - 2531
• Main Authors: Xu, Zhizhen, Chen, Jing, Mu, Mu, Tao, Lingjiang, Dai, Guokun, Wang, Jingzhuo, Ma, Yanan
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.07.2022; Wiley Subscription Services, Inc
• Subjects: Convection; convection‐allowing ensemble prediction system; model uncertainty; Modelling; Noise control; non‐linear forcing singular vector; Parameterization; Perturbation method; Physics; Predictions; Resolution; stochastically perturbed parameterization tendencies; Uncertainty
• ISSN: 0035-9009; 1477-870X
• DOI: 10.1002/qj.4322

Accurately 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. In this study, a conditional nonlinear‐stochastic perturbation method which combining the nonlinear forcing singular vector (NFSV) and the Stochastically Perturbed Parameterization Tendencies (SPPT) scheme is developed to simultaneously consider both a stochastic and a nonlinear representation of model uncertainty in CAEPSs. The results show that combining the NFSV and SPPT schemes improves the overall probabilistic skill, which may imply that adding additional state‐independent nonlinear noise contributes to a more comprehensive characterization of model error for representing model uncertainties in CAEPSs. Horizontal distribution of NFSVs. The first to fourth columns represent the horizontal distribution of NFSVs of zonal wind tendency (U‐tendency), meridional wind tendency (V‐tendency), temperature tendency (T‐tendency), and humidity tendency (Q‐tendency), respectively. And the first to fifth rows represent the NFSVs at 200, 500, 700, 850, and 1000 hPa respectively.