摘要: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/区域同化和预报系统（Global/Regional Assimilation and Prediction System，简称GRAPES）——全球集合预报系统（Global ensemble prediction system，简称GEPS）业务版本研究了4组不同时空尺度（不同OTI和水平分辨率）下的MSVs，从能量模、能量谱、空间剖面等方面分析热带外MSVs特征，并从等压面变量评分、降水评分、降水概率预报等方面评估不同初值的集合预报效果。结果表明：提高MSVs水平分辨率可使其扰动具有较大的增长率，缩短OTI后MSVs能量向上传播的趋势更明显，并可以在中尺度范围产生较大SVs扰动。不同OTI下初始MSVs相似性较低，结构差异较大。从集合预报的结果来看，OTI为24 h试验的集合扰动能量增长较大，集合离散度在预报的0～96 h有明显提升，特别是2 m温度，且近地面要素的outlier评分也有明显改进。进一步分析发现，提高水平分辨率和缩短OTI的MSVs能够提高降水概率预报，而降水评分显示，同一水平分辨率下，OTI越短评分越好，但是提高MSVs的水平分辨率并不一定会提升小雨到中雨量级的降水评分。
关键词:湿奇异向量/
最优时间间隔/
集合预报/
GRAPES-GEPS全球集合预报系统
Abstract: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.
Key words:Moist Singular Vector/
Optimization time interval/
Ensemble prediction/
GRAPES-GEPS Global ensemble prediction system
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