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Nowcasting & Warning Operational System in the Middle Reaches of the Yangtze with Its Implementation
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摘要: 针对长江中游强风暴天气特点和现代预报业务需求，在借鉴世界临近预报系统, 特别是美国的Auto-Nowcaster和WDSS-II以及英国的GANDOLF等先进经验的基础上，以我国多普勒天气雷达网为重要技术手段，结合数值预报等信息资源，于2007年研究建成长江中游临近预报业务系统 (MYNOS)。MYNOS主要技术方法包括：雷达与雨量计实时同步积分结合的降水估算方法 (RASIM)，雷达反演参量与中尺度模式输出物理量相结合的强风暴性质自动识别和追踪技术，基于暴雨回波生命史特性约束下的多尺度合成降水量临近预报，基于数值预报模式和模糊逻辑学的强对流天气分类落区潜势预报，集GIS功能并整合各种定量监测与预警产品于一体的短时预报工作站。MYNOS已成为短时临近预报业务的支撑平台，其中实时生成的流域定量降水估算与临近预报、强对流天气分类潜势诊断与识别预警产品等成为日常预报业务的重要参考依据。
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关键词:
- 长江中游临近预报业务系统;
- 强对流;
- 定量监测;
- 自动识别
Abstract: In order to meet the need for modern operational forecasting of severe storm events in the middle reaches of the Yangtze, MYNOS (Nowcasting & warning Operational System in the Middle reaches of theYangtze), an advanced and useful nowcasting system, is originally established in 2007 based on the experiences of the advanced nowcasting systems Auto-Nowcaster and WDSS-II of USA and GANDOLF of UK. MYNOS combines the resources of new generation radar network in China with the data from numerical weather prediction. Several advanced techniques and methods are developed and adopted as follows: Quality control of radar reflectivity field and the precipitation echo classification are achieved by identifying the structures of the vertical gradient and horizontal textures of radar reflectivity echoes. Real time formation technique of vertical reflectivity profile (VPR) is developed and used for vertical calibration of precipitation reflectivity factor. Important concepts of "quasi same-rain-volume sample" and "hourly equivalent reflectivity factor" are proposed, and the synchronously integrated method of radar and rain gauge (RASIM) is established. The cell gravity potential energy, as an important physical component of radar for describing the life span of storms, is proposed. The technique for automatic identification and tracking of severe weather is developed by means of radar derived parameters and the meso-scale output of physical parameters. The multi-scale characteristics of storm echoes through their life courses are analyzed and the echo filtering technique is studied, and the multi-scale precipitation nowcasting confined to the life time of each scale echo is realized. Potential forecasting products for severe convection meteorological phenomena (torrential rain, hail, thunderstorm, etc.) are developed based on numerical models and fuzzy logics. The problem of image registration and animation in ordinary GIS (Geographic Information System) is solved by introducing custom layer, and the nowcasting workstation equipped with the function of GIS along with numerical prediction, radar, satellite, lightning data and various quantitative monitoring, forecasting and warning products is developed from the bottom. MYNOS is put into operational experiments in May 2006 and has been in concentional operation since the flood season of 2007. The real-time generated output of MYNOS, such as valley quantitative precipitation estimation and nowcasting, strong convective weather classified potential diagnosis and identification warning products, provide an important foundation and reference for routine nowcasting operation.-
Key words:
- MYNOS;
- strong convection;
- quantitative detection;
- automatic identification
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图 2 2008年6月3日19：30 (北京时，下同) 黄冈地区强对流天气识别产品
(椭圆圈表示风暴水平投影面范围，如同时识别出冰雹和大风天气则只显示冰雹符号)
Fig. 2 Identification products of strong convective weather in Huanggang Region at 1930 BT 3 Jun 2008
(ellipse denotes the range of horizontal projection plane of wind storm, if both hail and gale are identified at the same time, only hail symbol is signed)
图 3 2003年7月8日07:00武汉天气雷达1 h降水外推预报和小时降水实况
(a) 不分尺度外推预报，(b) 分尺度外推预报
Fig. 3 1-h precipitation extrapolated prediction of Wuhan radar and the observed hourly precipitation at 0700 BT 8 Jul 2003
(a) non-separated scaling extrapolated prediction, (b) multi-scale extrapolated prediction
图 4 强对流天气分类潜势诊断模糊逻辑算法流程示意图
Fig. 4 Fuzzy logic algorithm flowchart of strong convective weather classified potential diagnosis
表 1 预报对象、模式输出物理量及其权重
Table 1 Prediction objects, model output physical quantities and their weights
预报对象 模式输出物理量 权重系数 雷暴 对流有效位能 0.66 K指数 0.41 对流抑制能量 0.40 850 hPa涡度ζ850 0.31 850 hPa与700 hPa平均相对湿度F87 0.20 雷暴大风 对流有效位能 0.62 K指数 0.60 对流抑制能量 0.30 850 hPa涡度ζ850 0.40 850 hPa与700 hPa平均相对湿度F87 0.20 700 hPa与500 hPa平均相对湿度F75 0.40 500 hPa与200 hPa平均相对湿度F52 0.30 冰雹 对流有效位能 0.60 K指数 0.60 对流抑制能量 0.40 850 hPa涡度ζ850 0.30 850 hPa与700 hPa平均相对湿度F87 0.20 700 hPa与500 hPa平均相对湿度F75 0.20 500 hPa与200 hPa平均相对湿度F52 0.40 0℃温度层高度 0.60 -20℃温度层高度 0.38 海拔高度(非模式输出量) 0.20 短时暴雨 K指数 0.50 850 hPa与700 hPa平均相对湿度F87 0.60 700 hPa与500 hPa平均相对湿度F75 0.60 500 hPa与200 hPa平均相对湿度F52 0.50 850 hPa水汽通量散度 0.60 850 hPa假相当位温 0.50 300 hPa与850 hPa涡度差 0.50 
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