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Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX
Author(s) -
Zhang Xubin
Publication year - 2018
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3411
Subject(s) - downscaling , predictability , parametrization (atmospheric modeling) , data assimilation , probabilistic logic , quantitative precipitation forecast , climatology , percentile , environmental science , meteorology , precipitation , forecast skill , standard deviation , mathematics , computer science , statistics , geology , geography , physics , quantum mechanics , radiative transfer
As a preliminary attempt to cope with the low predictability of heavy rainfall over southern China in the pre‐summer rainy season, an experimental convection‐permitting ensemble prediction system (GM‐CPEPS) based on the Global/Regional Assimilation and Prediction System (GRAPES) is developed. GM‐CPEPS produces 12 h forecasts at 0.03° horizontal resolution based on 16 perturbed members. Perturbations from downscaling, ensemble of data assimilation, time‐lagged scheme and topography are combined to generate the initial perturbations. Sea‐surface temperature is perturbed and a combination of downscaling and balanced random perturbations is used to perturb the lateral boundary conditions. Stochastically perturbed parametrization tendencies, multi‐physics, and perturbed parameters are all implemented. In this study, GM‐CPEPS was verified over a 15‐day period during the Southern China Monsoon Rainfall Experiment (SCMREX) in May 2014. It was indicated that GM‐CPEPS provided estimates of forecast uncertainty that are comparable to some international peers. Compared with the control forecasts (DET), some deterministic guidance, including the forecast distribution with 90th percentile, probability‐matched mean, and linear combination of both (NPM), showed advantages in forecasting moderate and heavy rainfall; and the optimal‐member technique was superior in reducing bias. Probabilistic guidance demonstrated the value over DET in detecting potential threats of severe weather, with both the optimal‐probability and neighbourhood‐probability technique leading to improvements in predicting lighter rainfall. Two cases were used to display the deterministic and probabilistic guidance intuitively and to illustrate corresponding advantages and drawbacks.