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The Regional Objective Precipitation Forecast in North China and Adjacent Areas in Summer
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摘要: 在降水客观分区的基础上，对华北及周边地区进行夏季降水预报。利用2006—2008年的6—8月T213资料和相应时段的实况资料，通过概率回归降水等级方案建模，对2009年和2010年6—8月进行了试报。结果表明：分区建模的降水预报与单站建模预报相比，TS评分在不同时效、不同量级上均有提高，并且在空报和漏报上有较大改善，特别是大量级降水预报改善明显。从因子分析上看，分区建模较单站建模所选因子更丰富，利用了模式产品的有用信息，因此做出了更好的预报。分区建模与模式降水预报的对比分析表明：分区建模的降水预报效果好于模式直接降水预报，空报现象改善明显。Abstract: North China is one of three major summer rainfall areas in eastern China. Precipitation over North China shows the characteristics of obvious emergency and locality. According to the statistics, 80%—90% precipitation occurs in June—August. Sometimes daily precipitation of a rainstorm can account for 50% precipitation amount of that month. Therefore, effective forecast is crucial especially for larger magnitude precipitation. Objective precipitation forecast is a difficult problem in NWP products interpretation at present. Objective precipitation forecast models are always established station by station, but larger magnitude precipitation is rare event for individual station. It is difficult to establish an effective forecast equation for an individual station. Precipitation intensity, spatial and temporal distribution over North China has its own particularity. Due to the regional characteristic, it is difficult to summarize in one model. Objective partitioning can be used in establishment of precipitation forecast model. Similar samples in the weather region are combined together. Regional forecast model is more stable than single-station forecast model, as the number of large-class precipitation samples increases.Seven weather divisions for summer precipitation over North China and adjacent areas are developed through Rotated Empirical Orthogonal Function (REOF) method, defined by the large contours of the seven REOF models. Objective precipitation forecast is based on probability regression precipitation categorical forecast. First, original precipitation is converted to 0 and 1 corresponding categories, and then forecast equations of different categories are developed to calculate each criterions. In real forecasting, the categorical precipitation will be determined through the criterion and the probability forecast of that category. Based on the daily precipitation data of station and T213 NWP products during the summer of 2006—2008, precipitation forecast model over North China and adjacent is established, which covers the domain (32°—42°N, 110°—124°E), including a total of 703 weather stations. Precipitation experiment is carried out for the summer of 2009 and 2010, and analysis of the forecast result indicates that regional method is better than single station method, especially for heavy precipitation. Regional model handles more factors than the single station model, so regional model makes a better prediction. Comparing to model direct forecast, regional forecast result is better, which reduces empty report obviously.
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图 1 华北及周边地区夏季降水分区
Fig. 1 Precipitation regional division over North China and adjacent areas during summer
图 2 2009年和2010年夏季华北及周边地区M1和M2方法降水预报
(a) TS评分，(b) 漏报率，(c) 空报率，(d) 预报偏差
Fig. 2 TS score (a), fault-hitting rate (b), not-hitting rate (c), forecast bias (d) averaged by M1 and M2 in North China and adjacent areas during the summer of 2009 and 2010
图 3 2009年和2010年夏季华北及周边地区M2和DMO方法降水预报
(a) TS评分, (b) 漏报率, (c) 空报率
Fig. 3 TS score (a), fault-hitting rate (b) and not-hitting rate (c) averaged by M2 and DMO in North China and adjacent areas during the summer of 2009 and 2010
图 4 2010年8月16—20日降水预报对比 (a)36 h，(b)60 h
Fig. 4 36-hour (a) and 60-hour (b) forecasts averaged during 16—20 August in 2010
图 5 2010年8月20日00：00 24 h降水实况 (a) 与8月18日12：00 M1方法 (b)、M2方法 (c)、DMO方法 (d) 的36 h降水预报对比
Fig. 5 24-hour rainfall in-situ on 20 August 2010(a) and corresponding 36-hour forecasts on 18 August 2010 of M1(b), M2(c), DMO (d)
表 1 单站建模预报因子统计
Table 1 Predictors statistic of single-station method
序号 小雨 中雨 大雨 暴雨 1 850 hPa Ky指数 850 hPa Ky指数 850 hPa Ky指数 80 hPa经向风的e指数 2 600 hPa x方向的水汽通量 700 hPa经向风的e指数 700 hPa经向风的e指数 850 hPa Ky指数 3 200~400 hPa平均
相对湿度垂直累积量700 hPa Ky指数 500 hPa湿位涡倾斜发展判据 850 hPa纬向风的立方 4 850 hPa温度的水平梯度 400 hPa比湿的立方 1000 hPa湿位涡倾斜发展判据 700 hPa经向风的e指数 5 500 hPa x方向的水汽通量 600 hPa位涡垂直项 400 hPa湿位涡倾斜发展判据 150 hPa经向风的e指数 6 500 hPa 3时次平均相对湿度 500 hPa y方向的水汽通量 400 hPa比湿的立方 250 hPa湿位涡倾斜发展判据 7 850 hPa A指数 850 hPa位涡垂直项 925 hPa湿位涡倾斜发展判据 1000 hPa湿位涡倾斜发展判据 8 850 hPa位涡垂直项 700 hPa比湿的立方 850 hPa湿位涡倾斜发展判据 925 hPa湿位涡倾斜发展判据 9 600 hPa 3时次最大相对湿度 500 hPa x方向的水汽通量 700 hPa Ky指数 500 hPa湿位涡倾斜发展判据 10 700 hPa Ky指数 850 hPa涡度平流 250 hPa湿位涡倾斜发展判据 400 hPa湿位涡倾斜发展判据 
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表 2 区域建模预报因子统计
Table 2 redictor statistic of regional method
序号 小雨 中雨 大雨 暴雨 1 600 hPa比湿 600 hPa比湿 850 hPa Ky指数 500 hPa y方向的水汽通量 2 850 hPa温度的水平梯度 850 hPa Ky指数 500 hPa y方向的水汽通量 700 hPa比湿的立方 3 500 hPa 3时次平均相对湿度 500 hPa y方向的水汽通量 850 hPa u在y方向的水平梯度 850 hPa Ky指数 4 200~400 hPa的平均
相对湿度垂直累积量700 hPa Ky指数 600 hPa x方向的水汽通量 850 hPa位涡垂直项 5 500 hPa比湿的立方 200 hPa与850 hPa之差
假相当位温垂直差400 hPa y方向的水汽通量 400 hPa比湿的立方 6 1000~850 hPa的平均
相对湿度垂直累积量700 hPa比湿的立方 700 hPa比湿的立方 400 hPa y方向的水汽通量 7 500 hPa 3时次最大相对湿度 700 hPa y方向的水汽通量 400 hPa比湿的立方 850 hPa u在y方向的水平梯度 8 600 hPa 3时次最大相对湿度 200~400 hPa的平均
相对湿度垂直累积量600 hPa 3时次最大相对湿度 850~1000 hPa的平均
相对湿度垂直累积量9 850 hPa 3时次平均相对湿度 500 hPa比湿的立方 700 hPa y方向的水汽通量 500 hPa与850 hPa厚度平流之差 10 500 hPa假相当位温 850 hPa温度的水平梯度 600 hPa v风垂直切变 600 hPa x方向的水汽通量
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