Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

• Published in: Advances in atmospheric sciences Vol. 37; no. 4; pp. 328 - 346
• Main Authors: Xu, Zhizhen, Chen, Jing, Jin, Zheng, Li, Hongqi, Chen, Fajing
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.04.2020; Springer Nature B.V; Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China%Numerical Weather Prediction Center, China Meteorological Administration, Beijing 100081, China%Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
• Subjects: Air; Atmospheric Sciences; Backscatter; Backscattering; Boundary layers; Convection; Cytotoxicity; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Grapes; Kinetic energy; Lymphocytes T; Mathematical models; Mesoscale systems; Meteorology; Microphysics; Original Paper; Parameterization; Physics; Regional development; Stochastic methods; Surface boundary layer; Surface layers; Uncertainty; Verification; Wavelength; Weather forecasting; Wind fields; Wind speed; stochastically perturbed parameterization; model uncertainty; multi-stochastic physics approaches; 集合预报; 模式不确定性; 随机参数扰动; ensemble prediction; 多种模式随机扰动方案
• ISSN: 0256-1530; 1861-9533
• DOI: 10.1007/s00376-020-9171-1

To represent model uncertainties more comprehensively, a stochastically perturbed parameterization (SPP) scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes, as well as the stochastically perturbed parameterization tendencies (SPPT) scheme, and the stochastic kinetic energy backscatter (SKEB) scheme, is applied in the Global and Regional Assimilation and Prediction Enhanced System—Regional Ensemble Prediction System (GRAPES-REPS) to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes. Six experiments are performed for a summer month (1–30 June 2015) over China and multiple verification metrics are used. The results show that: (1) All stochastic experiments outperform the control (CTL) experiment, and all combinations of stochastic parameterization schemes perform better than the single SPP scheme, indicating that stochastic methods can effectively improve the forecast skill, and combinations of multiple stochastic parameterization schemes can better represent model uncertainties; (2) The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill; (3) Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed. SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields, and it contributes most to the improvement in spread and outliers for wind; (4) The introduction of SPP has a positive added value, and does not lead to large changes in the evolution of the kinetic energy (KE) spectrum at any wavelength; (5) The introduction of SPPT and SKEB would cause a 5%–10% and 30%–80% change in the KE of mesoscale systems, and all three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the KE of mesoscale systems. This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.