Stochastically Perturbed Parameterizations for the Process-Level Representation of Model Uncertainties in the CMA Global Ensemble Prediction System

• Published in: Journal of Meteorological Research Vol. 36; no. 5; pp. 733 - 749
• Main Authors: Peng, Fei, Li, Xiaoli, Chen, Jing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2022; National Meteorological Centre,China Meteorological Administration,Beijing 100081; CMA Earth System Modeling and Prediction Centre,China Meteorological Administration(CMA),Beijing 100081; State Key Laboratory of Severe Weather,China Meteorological Administration,Beijing 100081
• Subjects: Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Earth and Environmental Science; Earth Sciences; Geophysics and Environmental Physics; Meteorology; Original Paper; model uncertainty; stochastic physics; global ensemble forecast; perturbation structure; parameter perturbation
• ISSN: 2095-6037; 2198-0934
• DOI: 10.1007/s13351-022-2011-8

To represent model uncertainties at the physical process level in the China Meteorological Administration global ensemble prediction system (CMA-GEPS), a stochastically perturbed parameterization (SPP) scheme is developed by perturbing 16 parameters or variables selected from three physical parameterization schemes for the planetary boundary layer, cumulus convection, and cloud microphysics. Each chosen quantity is perturbed independently with temporally and spatially correlated perturbations sampled from log-normal distributions. Impacts of the SPP scheme on CMA-GEPS are investigated comprehensively by using the stochastically perturbed parametrization tendencies (SPPT) scheme as a benchmark. In the absence of initial-condition perturbations, perturbation structures introduced by the two schemes are investigated by analyzing the ensemble spread of three forecast variables’ physical tendencies and perturbation energy in ensembles generated by the separate use of SPP and SPPT. It is revealed that both schemes yield different perturbation structures and can simulate different sources of model uncertainty. When initial-condition perturbations are activated, the influences of the two schemes on the performance of CMA-GEPS are assessed by calculating verification scores for both upper-air and surface variables. The improvements in ensemble reliability and probabilistic skill introduced by SPP and SPPT are mainly located in the tropics. Besides, the vast majority of the reliability improvements (including increases in ensemble spread and reductions in outliers) are statistically significant, and a smaller proportion of the improvements in probabilistic skill (i.e., decreases in continuously ranked probability score) reach statistical significance. Compared with SPPT, SPP generally has more beneficial impacts on 200-hPa and 2-m temperature, along with 925-hPa and 2-m specific humidity, during the whole 15-day forecast range. For other examined variables, such as 850-hPa zonal wind, 850-hPa temperature, and 700-hPa humidity, SPP tends to yield more reliable ensembles at lead times beyond day 7, and to display comparable probabilistic skills with SPPT. Both SPP and SPPT have small impacts in the extratropics, primarily due to the dominant role of the singular vectors-based initial perturbations.