设为首页
加入收藏
A Method of Short-time Strong Rainfall Forecasting During Pre-rainy Season in Fujian Based on ECMWF Productions
-
摘要: 利用2014—2016年福建省1605个自动气象站逐时降水资料和ECMWF全球模式细网格预报产品，分析福建省前汛期短时强降水发生背景下模式预报物理量的分布特征，并基于阈值判定的方法建立短时强降水预报模型。结果表明：福建省内陆县市前汛期短时强降水发生频次较高，沿海县市发生频次低，且日变化特征表现出双峰结构。箱型图差异指数（Ibd）在评估相关变量对于区分短时强降水发生与否的敏感程度有较好的作用，比湿、整层可降水量等水汽变量Ibd最为显著，K指数、对流有效位能等变量的Ibd仅次于水汽变量，说明模式预报变量对于预测短时强降水有较好的表征作用。针对短时强降水事件的物理量集合，采用剔除异常值后的最小值作为判定阈值，通过训练集分析结果客观订正对流有效位能和3 h降水量两个高Ibd变量的阈值，建立潜势预报模型。对于福建省西部的关键区，检验集白天时段12 h时间分辨率预报TS评分可达0.5，夜间时段约为0.3。对于福建省进行分区建模预报，检验集预报结果显示白天时段比夜间准确率高、内陆县市比沿海县市准确率高。Abstract: Distribution features of model variables accompanied with short-time strong rainfall events are investigated based on hourly precipitation data from 1605 automatic weather stations and ECMWF 0.125°×0.125° fine grid model products, and a method of short-time strong rainfall forecasting based on threshold determination is established. Results show that short-time strong rainfall occur more frequently in Fujian inland area and less in Fujian coastal area during pre-rainy season, and the diurnal variation exhibits double peaks with the notable one at 1700 BT and the inapparent one at 0500 BT. The box difference index is useful to check whether a variable could differentiate short-time strong rainfall events well. The box difference index of humidity variables like specific humidity at 925 hPa and total column water vapor are most prominent followed by K index and convective available potential energy (CAPE) which shows these variables have good performances in distinguishing short-time strong rainfall events. Some variables like temperature difference between 850 hPa and 500 hPa and temperature change in 24 h at 500 hPa perform poorly in differentiating short-time strong rainfall events.The minimum threshold method based on the minimum values of variables after eliminating outliers works well in judging short-time strong rainfall events, which could decrease vacancy forecast rate effectively through increasing missing forecast rate appropriately compared with adopting real minimum values as threshold. In the key area (25.9°-27.1°N, 116.4°-117.4°E), TS (threaten score) of validation set in 2016 with 12 h interval reaches 0.5 at daytime and 0.3 at nighttime just based on the minimum threshold method. Revising threshold of variables with high box difference index values could improve the accuracy with the nighttime TS of validation set in 2016 increasing from 0.3 to 0.34. TS of 2016 is relatively lower compared with that of 2014-2015, and the cause may be that short-time strong rainfalls happen much more frequently in 2016 which is a very strong El Niño year.To establish a potential forecast model of short-time strong rainfall during pre-rainy season, Fujian is divided into grids of 1°×1°, and minimum threshold method is applied in each grid followed by threshold revise of variables with high box difference index values. This model could analyze all kinds of variables comprehensively besides those considered by weather forecasters. TS with 12 h interval at daytime mainly ranges from 0.3 to 0.5 while TS at nighttime is relatively lower. TS in inland area is much better than coastal area both at daytime and nighttime mainly because short-time strong rainfall occurs more frequently in inland area than coastal area during pre-rainy season climatologically.
-
图 1 福建省2009—2016年前汛期短时强降水的空间频次分布(a)和日变化(b)特征
(黑色矩形框代表关键区)
Fig. 1 The spatial distribution(a) and diurnal variation(b) of short-time strong rainfall events during pre-rainy season from 2009-2016 in Fujian
(black box denotes the key area)
图 2 2014—2015年前汛期夜间时段关键区内短时强降水发生和不发生时的K指数分布箱型图
(黑点代表异常值)
Fig. 2 The boxplot of K index with short-time strong rainfall happened or not at nighttime during pre-rainy season in 2014-2015
(the black point denotes the outlier)
图 3 2014—2015年前汛期白天时段关键区内是否发生短时强降水时ECMWF模式预报物理量的箱型图分布
(黑点代表异常值)
Fig. 3 The boxplot of ECMWF variables with short-time strong rainfall happened or not at daytime during pre-rainy season from 2014-2015
(the black point denotes the outlier)
图 4 基于Ibd阈值和变量阈值百分位数变化的2014—2015年前汛期关键区白天时段(a)和夜间时段(b)3 h时间分辨率预报TS评分
Fig. 4 TS at daytime(a) and nighttime(b) varied with Ibd threshold and variable percentile threshold with time interval of 3 h during pre-rainy season in 2014-2015
图 5 福建省前汛期短时强降水12 h时间分辨率预报TS评分
(a)2014—2015年白天时段，(b)2014—2015年夜间时段，(c)2016年白天时段，(d)2016年夜间时段
Fig. 5 TS of short-time strong rainfall prediction during pre-rainy season with time interval of 12 h
(a)daytime in 2014-2015, (b)nighttime in 2014-2015, (c)daytime in 2016, (d)nighttime in 2016
图 6 2016年5月9日夜间时段模型预报结果(a)和ECMWF模式12 h累积降水量预报(b)
Fig. 6 The forecast of short-time strong rainfall with method in this study(a) and 12 h precipitation from ECMWF(b) at nighttime on 9 May 2016
表 1 关键区内模式预报变量的Ibd
Table 1 Ibd of variables in the key area
物理量 Ibd 白天 夜间 整层可降水量 0.44 0.49 925 hPa比湿 0.44 0.43 3 h降水量 0.42 0.49 850 hPa比湿 0.42 0.43 925 hPa露点 0.41 0.40 500 hPa垂直速度 0.39 0.40 850 hPa露点 0.37 0.35 K指数 0.36 0.35 对流有效位能 0.33 0.42 海平面气压 0.30 0.32 700 hPa垂直速度 0.29 0.36 500 hPa位势高度 0.28 0.23 850 hPa散度 0.22 0.19 200 hPa散度 0.16 0.23 850 hPa与500 hPa温差 0.14 0.06 500 hPa 24 h变温 0.04 0.03 
下载: 导出CSV
表 2 仅采用最小阈值法针对关键区短时强降水的预报准确性检验
Table 2 The forecast verificaiton of short-time strong rainfall in the key area only using minimum threshold method
时段 2014—2015年 2016年 N1 N2 N3 N4 TS评分 N1 N2 N3 N4 TS评分 白天时段(3 h时间分辨率) 95 25 189 419 0.307 36 31 121 176 0.191 夜间时段(3 h时间分辨率) 59 13 230 426 0.195 24 27 114 199 0.145 白天时段(12 h时间分辨率) 51 16 31 84 0.520 31 10 20 30 0.508 夜间时段(12 h时间分辨率) 31 9 60 82 0.310 17 10 29 35 0.303
下载: 导出CSV
表 3 同表 2，但潜势预报方法中加入对流有效位能和3 h降水量阈值的客观订正
Table 3 The same as in Table 2, but revising the threshold of convective available potential energy and 3 h preicipitation on the basis of minimum threshold method
时段 2014—2015年 2016年 N1 N2 N3 N4 TS评分 N1 N2 N3 N4 TS评分 白天时段(3 h时间分辨率) 86 34 150 458 0.319 33 34 92 205 0.208 夜间时段(3 h时间分辨率) 50 22 101 555 0.289 14 37 45 268 0.146 白天时段(12 h时间分辨率) 48 19 25 90 0.522 30 11 18 32 0.508 夜间时段(12 h时间分辨率) 30 10 36 106 0.395 14 13 14 50 0.341
下载: 导出CSV
表 4 同表 2，但为基于华东区域中尺度模式的关键区短时强降水2016年预报检验
Table 4 The same as in Table 2, but verification of Shanghai Typhoon Institute-WRF ADAS Real-time Modeling System in the key area with data of 2016
时段 2016年 N1 N2 N3 N4 TS评分 白天时段(3 h时间分辨率) 16 51 32 265 0.161 夜间时段(3 h时间分辨率) 16 35 27 286 0.205 白天时段(12 h时间分辨率) 20 21 10 40 0.392 夜间时段(12 h时间分辨率) 9 18 9 55 0.250
下载: 导出CSV
-
[1] Zhang H, Zhai P M.Temporal and spatial characteristics of extreme hourly precipitation over eastern China in the warm season.Adv Atmos Sci, 2011, 28(5):1177-1183. doi: 10.1007/s00376-011-0020-0 [2] 韩宁, 苗春生.近6年陕甘宁三省5-9月短时强降水统计特征.应用气象学报, 2013, 23(6):691-701. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120606&flag=1 [3] 何晗, 谌芸, 肖天贵, 等.冷涡背景下短时强降水的统计分析.气象, 2015, 41(12):1466-1476. doi: 10.7519/j.issn.1000-0526.2015.12.004 [4] 阿不力克木, 王元, 王亦平, 等.一次强对流天气及其中短时强降水的成因分析.气象科学, 2014, 34(4):457-464. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=QXKX201404015&dbname=CJFD&dbcode=CJFQ [5] 段鹤, 夏文梅, 苏晓力, 等.短时强降水特征统计及临近预警.气象, 2014, 40(10):1194-1206. doi: 10.7519/j.issn.1000-0526.2014.10.004 [6] 史锐, 程明虎, 崔哲虎, 等.用反射率因子垂直廓线联合雨量计校准估测夏季区域强降水.应用气象学报, 2005, 16(6):737-745. doi: 10.3969/j.issn.1001-7313.2005.06.004 [7] 郜彦娜, 何立富.2011年7月12-20日华北冷涡阶段性特.应用气象学报, 2013, 24(6):704-713. doi: 10.3969/j.issn.1001-7313.2013.06.007 [8] 陈明轩, 俞小鼎, 谭晓光, 等.北京2004年"7.11"突发性对流强降水的雷达回波特征分析.应用气象学报, 2006, 17(3):333-345. doi: 10.3969/j.issn.1001-7313.2006.03.011 [9] 常煜, 马素艳, 仲夏.内蒙古夏季典型短时强降水中尺度特征.应用气象学报, 2018, 29(2):232-244. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180209&flag=1 [10] 陈炯, 郑永光, 张小玲, 等.中国暖季短时强降水分布和日变化特征及其与中尺度对流系统日变化关系分析.气象学报, 2013, 71(3):367-382. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=QXXB201303003&dbname=CJFD&dbcode=CJFQ [11] 陈秋萍, 陈敏艳, 刘爱鸣, 等.登陆福建热带气旋短时强降水特征.气象研究与应用, 2016, 37(3):24-28. doi: 10.3969/j.issn.1673-8411.2016.03.013 [12] 魏晓雯, 梁萍, 何金海.上海地区不同类型短时强降水的大尺度环流背景特征分析.气象与环境科学, 2016, 39(2):69-75. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=HNQX201602010&dbname=CJFD&dbcode=CJFQ [13] 杨波, 孙继松, 毛旭, 等.北京地区短时强降水过程的多尺度环流特征.气象学报, 2016, 74(6):919-934. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=QXXB201606008&dbname=CJFD&dbcode=CJFQ [14] 许爱华, 孙继松, 许东蓓, 等.中国中东部强对流天气的天气形势分类和基本要素配置特征.气象, 2014, 40(4):400-411. doi: 10.3969/j.issn.1671-1742.2014.04.013 [15] 仇娟娟, 何立富.苏沪浙地区短时强降水与冰雹天气分布及物理量特征对比分析.气象, 2013, 39(5):577-584. doi: 10.3969/j.issn.1674-7097.2013.05.008 [16] 何立富, 周庆亮, 谌芸, 等.国家级强对流潜势预报业务进展与检验评估.气象, 2011, 37(7):777-784. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=QXXX201107002&dbname=CJFD&dbcode=CJFQ [17] 樊李苗, 俞小鼎.中国短时强对流天气的若干环境参数特征分析.高原气象, 2013, 32(1):156-165. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=GYQX201301017&dbname=CJFD&dbcode=CJFQ [18] 庞古乾, 伍志方, 叶爱芬, 等.珠三角地区前后汛期强对流过程物理量指数对比分析及阈值选取.热带气象学报, 2012, 28(6):919-923. http://d.old.wanfangdata.com.cn/Periodical/rdqxxb201206014 [19] Doswell Ⅲ C A, Brooks H E, Maddox R A.Flash flood forecasting:An ingredients-based methodology.Wea Forecasting, 1996, 11(4):560-581. doi: 10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2 [20] 沈澄, 孙燕, 魏晓奕, 等.基于物理量参数的江苏短时强降水预报模型的研究.气象, 2016, 42(5):557-566. http://d.old.wanfangdata.com.cn/Periodical/qx201605005 [21] 田付友, 郑永光, 张涛, 等.短时强降水诊断物理量敏感性的点对面检验.应用气象学报, 2015, 26(4):385-396. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20150401&flag=1 [22] 张莹, 袁志勇, 徐春阳, 等.短时强降水历史个例查询与潜势预报平台.气象科学, 2012, 32(12):149-154. http://d.old.wanfangdata.com.cn/Periodical/qxkx2011z1024 [23] 谌志刚, 张羽, 刘云香, 等.基于天气形势分型的珠江三角洲强对流潜势预报方法.气象科技, 2017, 45(3):460-468. http://d.old.wanfangdata.com.cn/Periodical/qxkj201703010 [24] 曾明剑, 王桂臣, 吴海英, 等.基于中尺度数值模式的分类强对流天气预报方法研究.气象学报, 2015, 73(5):868-882. http://d.old.wanfangdata.com.cn/Periodical/qxxb201505005 [25] 雷蕾, 孙继松, 王国荣, 等.基于中尺度数值模式快速循环系统的强对流天气分类概率预报试验.气象学报, 2012, 70(4):752-765. http://d.old.wanfangdata.com.cn/Periodical/qxxb201204012 [26] 吴启树, 韩美, 郭弘, 等.MOS温度预报中最优训练期方案.应用气象学报, 2016, 27(4):426-434. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20160405&flag=1 [27] 吴启树, 韩美, 刘铭, 等.基于评分最优化的模式降水预报订正算法对比.应用气象学报, 2017, 28(3):306-317. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20170305&flag=1 [28] 伍红雨, 杨崧, 蒋兴文.华南前汛期开始日期异常与大气环流和海温变化的关系.气象学报, 2015, 73(2):319-330. http://d.old.wanfangdata.com.cn/Periodical/qxxb201502008 [29] 林爱兰, 李春晖, 郑彬, 等.广东前汛期持续性暴雨的变化特征及其环流形势.气象学报, 2013, 71(4):628-642. http://d.old.wanfangdata.com.cn/Periodical/qxxb201304004 [30] Fu B, Peng M S, Li T, et al.Developing versus nondeveloping disturbances for tropical cyclone formation.Part Ⅱ:Western North Pacific.Mon Wea Rev, 2012, 140(4):1067-1080. doi: 10.1175/2011MWR3618.1 [31] 唐文苑, 周庆亮, 刘鑫华, 等.国家级强对流天气分类预报检验分析.气象, 2017, 43(1):67-76. http://d.old.wanfangdata.com.cn/Periodical/qx201701007 [32] 毕宝贵, 张小玲, 代刊.2016年超强厄尔尼诺背景下的强对流和极端降水天气特点.科学通报, 2017, 62(9):928-937. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QKC20172017041000111339 -
点击查看大图
计量
- 摘要浏览量: 3534
- HTML全文浏览量: 1274
- PDF下载量: 261
- 被引次数: 0
