GRAPES-GEPS全球集合预报系统湿奇异向量的时空尺度敏感性研究

湿奇异向量(Moist Singular Vectors,简称MSVs)是包含了湿物理切线性过程计算得到的奇异向量.研究MSVs对最优化时间间隔(optimization time interval,简称OTI)及模式水平分辨率的敏感性对提高集合预报效果至关重要.本文基于中国气象局数值预报中心自主研发的全球/区域同化和...

A Sensitivity Study of the Moist Singular Vectors to Temporal and Spatial Scales in GRAPES-GEPS Global Ensemble Prediction System

The Singular Vectors (SVs) that include the linearized moist physical process in calculations are called Moist SVs (MSVs). The sensitivity study of MSVs to horizontal resolutions and optimization time intervals (OTI) is important for the ensemble forecasting system. Based on the operational version of Global/Regional Assimilation and Prediction System-Global ensemble prediction system (GRAPES-GEPS), which is independently developed by the China meteorological administration’s numerical forecast center, this paper analyzes the characteristics of the subtropical MSVs and their ensemble forecasts under four groups of experiments with different horizontal and temporal resolutions. The characteristics of MSVs in terms of energy norm, energy spectrum and spatial profile are analyzed, and the evaluation of the ensemble forecast with the four groups of experiments is made in terms of isopressure variable scores, precipitation scores, and precipitation probability predictions. An increase in the horizontal resolution of MSVs leads to an increase in the growth rate of their perturbation. The upward propagation of MSV energy is more obvious than the downward propagation with the reduced OTI, which also produces relatively large SV perturbations in the mesoscale ranges. Under different OTIs, the initial MSVs are less similar to each other and their structures are different from each other. From the perspective of ensemble forecasting, the average ensemble perturbed energy with the 24-h OTI increases greatly, and the ensemble spread is improved for the 0- to 96-h prediction, especially for the 2-m temperature and the outlier scores of the near-surface variables. It is further found that increasing the horizontal and temporal resolutions can improve the precipitation probability prediction. The precipitation scores show that at the same spatial resolution, the shorter the OTI, the better the scores, while increasing the horizontal resolution of the MSVs fails to improve the precipitation scores for the light to moderate rains.