微波辐射计在对流天气预报中的应用

选取2016—2018年每年4—9月份RPG-HATPRO型42通道微波辐射计观测的不稳定指数参数(K、SI、CAPE、LI)及水汽参数(IWV、LWP),研究得出各参数触发雷雨大风、短时强降水的阈值条件为K>37℃、SI<-1℃、IWV>60 kg/m~2、LWP>400 g/m~2,而LI、CAPE无法对3种天气类型进行区分。利用费舍判别分析方法,将不稳定指数参数及水汽参数作为预报因子,建立预报方程并进行检验,结果表明:二级判别方程预测对流天气的准确率为76%,可以作为预报对流天气的辅助工具;多级判别方程不能很好地区分3种天气类型,但将其作为修正后的二级判别方程使用,能提高对流天气的测中概率。     

Seasonal Prediction for May Rainfall over Southern China Based on the NCEP CFSv2

In this study, we assess the prediction for May rainfall over southern China (SC) by using the NCEP CFSv2 outputs. Results show that the CFSv2 is able to depict the climatology of May rainfall and associated circulations. However, the model has a poor skill in predicting interannual variation due to its poor performance in capturing related anomalous circulations. In observation, the above-normal SC rainfall is associated with two anomalous anticyclones over the western tropical Pacific and northeastern China, respectively, with a low-pressure convergence in between. In the CFSv2, however, the anomalous circulations exhibit the patterns in response to the El Ni?o-Southern Oscillation (ENSO), demonstrating that the model overestimates the relationship between May SC rainfall and the ENSO. Because of the onset of the South China Sea monsoon, the atmospheric circulation in May over SC is more complex, so the prediction for May SC rainfall is more challenging. In this study, we establish a dynamic-statistical forecast model for May SC rainfall based on the relationship between the interannual variation of rainfall and large-scale ocean-atmosphere variables in the CFSv2. The sea surface temperature anomalies (SSTAs) in the northeastern Pacific and the central-eastern equatorial Pacific, and the 500-hPa geopotential height anomalies over western Siberia in previous April, which exert great influence on the SC rainfall in May, are chosen as predictors. Furthermore, multiple linear regression is employed between the predictors obtained from the CFSv2 and observed May SC rainfall. Both cross validation and independent test show that the hybrid model significantly improve the model''s skill in predicting the interannual variation of May SC rainfall by two months in advance.     

2020年5月31日珠三角暴雨过程的中尺度特征

利用NCEP-FNL资料、国家级地面观测站和区域自动站资料,以及新一代多普勒天气雷达产品等资料,对2020年5月31日发生在珠三角地区且预报偏差较大的暴雨过程的中尺度系统生成环境条件、触发机制以及雷达回波演变特征进行分析,结果表明:该次暴雨过程是在高层辐散、中层弱波动过境、低层西南季风的有利天气背景下,由弱冷空气南下渗透,形成长时间维持的地面中尺度辐合系统,不断触发对流造成的.西南季风气流的风速脉动为对流提供水汽和不稳定能量,使对流不断发展增强.强降雨过程以低质心的暖云降水为主,强降雨后期东莞出现嵌有气旋式切变的强对流单体和较长生命史的弱HP型超级单体.     

SEASONAL PREDICTIONS FOR SPRING AND AUTUMN SURFACE AIR TEMPERATURES OVER SOUTHERN CHINA BY THE NCEP CFSV2

In this study, we investigate the variations of spring and autumn air temperatures in southern China (SC) and associated atmospheric circulation patterns. During the boreal spring, the SC air temperature is mainly influenced by tropical sea surface temperature anomalies (SSTAs). On the one hand, the El Ni?o SSTA pattern may induce a stronger-than-normal western Pacific subtropical high, which leads to warming in SC. On the other hand, the warm SSTAs in the tropical Indian Ocean may trigger anomalous Rossby wave trains, which propagate northeastward and result in anomalously high temperature in SC. During the boreal autumn, however, the SC temperature is more likely affected by mid-latitude atmospheric circulation, such as the wave trains forced by the North Atlantic SSTAs. The NCEP Climate Forecast System version 2 (CFSv2) is able to capture the climatology of SC air temperatures during both spring and autumn. For interannual variation, the CFSv2 shows a good skill for predicting the SC temperature in spring, due to the model’s good performance in capturing the associated atmospheric circulation anomalies as responses to tropical SSTAs, in spite of the overestimated relationship with the El Ni?o–Southern Oscillation (ENSO). However, the model has a poor skill for predicting the SC temperature in autumn, primarily due to the unrealistic prediction of its relationship with the ENSO.