设为首页
加入收藏
A Comparison of Optimal-score-based Correction Algorithms of Model Precipitation Prediction
-
摘要: 使用2013年1月1日-2016年1月7日全国气象站观测资料，应用准对称混合滑动训练期，不改变雨带预报位置和形态，基于模式降水预报订正结果的TS评分最优化及ETS评分最优化，分别设计最优TS评分订正法（OTS）和最优ETS评分订正法（OETS）确定预报日各级降水订正系数，对2014-2015年降水数值预报进行分级订正，并与频率匹配法（FM）对比。结果表明：在24 h累积降水的多个预报时效订正中，无论是对欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心和中国气象局的全球模式降水预报，还是对4个模式的简单多模式平均，OTS和OETS较FM在TS评分和ETS评分等传统降水检验指标上均更优秀，其中OTS在所有时效均能提高模式降水预报质量，为三者最优。在概率空间的稳定公平误差评分方面，OTS在各时效、各单模式及多模式平均等方面优势明显。在预报员对应参考时效上，OTS在24~168 h的24 h累积降水预报中的TS评分也优于主观预报。Abstract: Based on data from national meteorological stations, one year quasi-symmetrical mixed running training period (QSRTP), and precipitation prediction from CMA (T639), ECMWF, NCEP, JMA, both optimal threat score (OTS) method and optimal equitable threat score (OETS) method are designed to conduct a comparison experiment on correction algorithms for model precipitation with frequency matching (FM) method. Through classification correction, three methods are used merely to calibrate model precipitation amount with the predicted rain-belt location and shape kept unchanged. The OTS method figures out correction coefficients of different precipitation classes by optimizing threat score (TS) of corrected precipitation within training period. OETS is similar to OTS but achieved by optimizing ETS. Correction experiments are conducted twice a day with forecast time at 0000 UTC and 1200 UTC, respectively. To consider seasonal background, 20 days before the forecast day and 20 days after the same day in the previous year are adopted to constitute training period. For each national meteorological station, there are 80 samples in total. The correction experiment shows that for either precipitation products of ECMWF, JMA, NCEP, CMA, or their ensemble mean, both OTS and OETS show much better performance than FM in 24 h accumulated precipitation classification calibration with different lead time according to traditional verification methods like TS and ETS. In particular, OTS is the best and can improve precipitation prediction in all lead times. After correction, both OTS and OETS incline to forecast larger precipitation area than observation for most classes but less precipitation amounts. Compared to FM, both methods tend to produce a little higher false alarm rates in middle and low classes, which is much less than the reduced missing rate, thereby leading to a higher threat score. In terms of ECMWF correction, OTS and OETS have a relatively stable Bias score of 1.1, although there are much fewer samples in high class. By contrast, FM produces an unstable Bias score, especially in maximum class with score over 2.2, indicating an excessively high missing rate. As for stable equitable error in probability space (SEEPS), OTS has superiorities over all lead times, all single models and multi-model mean. Furthermore, TS of corrected ECMWF precipitation using OTS method in 2015 are also better than subjective forecast from all aspects, with national averaged threat score of 1 d rainstorm forecast reaching 0.194.
-
图 1 TS评分或ETS评分提高的不同情况示意图
(a) 预报站点数与实况相同，(b) 预报站点数多于实况，(c) 预报站点数少于实况
Fig. 1 qIllustration of different conditions of TS or ETS performance improvement
(a) the number of forecast stations is in consistent with the observation, (b) the number of forecast stations is larger than the observation, (c) the number of forecast stations is less than the observation
图 3 2014—2015年ECMWF降水预报和FM，OTS，OETS 3种算法订正的24 h累积降水24 h预报的TS评分 (a)、ETS评分 (b)、空报率 (c)、漏报率 (d)、预报偏差 (e) 和HSS技巧评分 (f)
Fig. 3 TS (a), ETS (b), false alarm rate (c), miss rate (d), Bias (e) and HSS (f) of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time of 24 h during 2014-2015
图 4 2014—2015年ECMWF降水预报和FM，OTS，OETS 3种算法订正的0.1 mm (a)，10 mm (b)，25 mm (c) 和50 mm (d)24 h累积降水24~240 h预报的TS评分
Fig. 4 TS of 0.1 mm (a), 10 mm (b), 25 mm (c) 和50 mm (d) of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time from 24 h to 240 h during 2014-2015
图 5 2014—2015年ECMWF降水预报和FM，OTS，OETS 3种算法的24 h累积降水24~240 h预报的SEEPS技巧评分
Fig. 5 SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on ECMWF with lead time from 24 h to 240 h during 2014-2015
图 6 2014—2015年JMA (a)、NCEP (b) 和T639(c) 降水预报及FM，OTS，OETS3种算法订正的24 h累积降水24 h预报TS评分
Fig. 6 TS of 24 h accumulative precipitation by FM, OTS and OETS based on JMA (a), NCEP (b) and T639(c) with lead time of 24 h during 2014-2015
图 7 2014—2015年简单多模式降水预报平均和FM，OTS，OETS 3种算法订正的24 h累积降水24 h预报TS评分
Fig. 7 TS of 24 h accumulative precipitation by FM, OTS and OETS based on ensemble mean forecasts with lead time of 24 h during 2014-2015
图 8 2014—2015年简单多模式降水预报平均和FM，OTS，OETS 3种算法订正的24 h累积降水24~72 h预报SEEPS技巧评分
Fig. 8 SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on ensemble mean forecasts with lead time from 24 h to 72 h during 2014-2015
表 1 2014—2015年FM，OTS和OETS 3种算法对JMA，NCEP及T639降水预报订正的24 h累积降水24~72 h预报的SEEPS技巧评分
Table 1 SEEPS skill scores of 24 h accumulative precipitation by FM, OTS and OETS based on JMA, NCEP and T639 with lead time from 24 h to 72 h during 2014-2015
模式 算法 24 h预报 48 h预报 72 h预报 JMA 未订正 0.562 0.506 0.466 FM 0.626 0.562 0.504 OTS 0.640 0.572 0.513 OETS 0.632 0.567 0.511 NCEP 未订正 0.525 0.493 0.474 FM 0.572 0.533 0.495 OTS 0.594 0.556 0.526 OETS 0.588 0.550 0.519 T639 未订正 0.512 0.472 0.405 FM 0.567 0.518 0.425 OTS 0.602 0.546 0.453 OETS 0.599 0.541 0.451 
下载: 导出CSV
表 2 2015年中国国家气象中心预报员与ECMWF_OTS 24 h累积降水TS评分对比
Table 2 TS comparisons of 24 h accumulative precipitation between China NMC forecasters and ECMWF_OTS all over China stations in 2015
起报时间 预报时效/h 小雨 中雨 大雨 暴雨 预报员 ECMWF_OTS 预报员 ECMWF_OTS 预报员 ECMWF_OTS 预报员 ECMWF_OTS 24 0.594 0.608 0.395 0.414 0.281 0.302 0.175 0.189 48 0.572 0.590 0.359 0.376 0.250 0.263 0.147 0.153 72 0.552 0.565 0.328 0.335 0.225 0.233 0.118 0.136 00:00 96 0.522 0.538 0.298 0.305 0.198 0.206 0.094 0.118 120 0.500 0.514 0.271 0.276 0.168 0.178 0.073 0.093 144 0.473 0.486 0.242 0.244 0.145 0.148 0.080 0.083 168 0.442 0.448 0.208 0.214 0.124 0.133 0.050 0.062 24 0.590 0.609 0.392 0.414 0.285 0.304 0.186 0.199 12:00 48 0.574 0.584 0.354 0.372 0.255 0.264 0.158 0.163 72 0.550 0.559 0.323 0.337 0.222 0.236 0.125 0.150
下载: 导出CSV
-
[1] 周兵, 赵翠光, 赵声蓉.多模式集合预报技术及其分析与检验.应用气象学报, 2006, 17(增刊Ⅰ):104-109. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2006S1014.htm [2] 赵声蓉.多模式温度集成预报.应用气象学报, 2006, 17(1):52-58. doi: 10.11898/1001-7313.20060109 [3] 林春泽, 智协飞, 韩艳, 等.基于TIGGE资料的地面气温多模式超级集合预报.应用气象学报, 2009, 20(6):706-712. doi: 10.11898/1001-7313.20090608 [4] 范丽军, 符淙斌, 陈德亮.统计降尺度法对华北地区未来区域气温变化情景的预估.大气科学, 2007, 31(5):887-897. [5] 赵声蓉, 裴海瑛.客观定量预报中降水的预处理.应用气象学报, 2007, 18(1):21-28. doi: 10.11898/1001-7313.20070104 [6] 赵声蓉, 赵翠光, 赵瑞霞, 等.我国精细化客观气象要素预报进展.气象科技进展, 2012, 2(5):12-21. [7] 车钦, 赵声蓉, 范广洲.华北地区极端温度MOS预报的季节划分.应用气象学报, 2011, 22(4):429-436. doi: 10.11898/1001-7313.20110405 [8] 刘还珠, 赵声蓉, 陆志善, 等.国家气象中心气象要素的客观预报——MOS系统.应用气象学报, 2004, 15(2):181-191. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040223&flag=1 [9] 马清, 龚建东, 李莉, 等.超级集合预报的误差订正与集成研究.气象, 2008, 34(3):42-48. doi: 10.7519/j.issn.1000-0526.2008.03.007 [10] 王在文, 郑祚芳, 陈敏, 等.支持向量机非线性回归方法的气象要素预报.应用气象学报, 2012, 23(5):562-570. doi: 10.11898/1001-7313.20120506 [11] 胡邦辉, 刘善亮, 席岩, 等.一种Bayes降水概率预报的最优子集算法.应用气象学报, 2015, 26(2):185-192. doi: 10.11898/1001-7313.20150206 [12] 陈博宇, 代刊, 郭云谦.2013年汛期ECMWF集合统计量产品的降水预报检验与分析.暴雨灾害, 2015, 34(1):64-73. http://www.cnki.com.cn/Article/CJFDTOTAL-HBQX201501009.htm [13] 张宏芳, 潘留杰, 杨新.ECMWF、日本高分辨率模式降水预报能力的对比分析.气象, 2014, 40(4):424-432. doi: 10.7519/j.issn.1000-0526.2014.04.004 [14] 李俊, 杜钧, 陈超君.降水偏差订正的频率 (或面积) 匹配方法介绍和分析.气象, 2014, 40(5):580-588. doi: 10.7519/j.issn.1000-0526.2014.05.008 [15] 王海霞, 智协飞.基于TIGGE多模式降水量预报的统计降尺度研究.气象科学, 2015, 35(4):430-437. doi: 10.3969/2014jms.0058 [16] 智协飞, 季晓东, 张璟, 等.基于TIGGE资料的地面气温和降水的多模式集成预报.大气科学学报, 2013, 36(3):257-266. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201303003.htm [17] 孙靖, 程光光, 张小玲.一种改进的数值预报降水偏差订正方法及应用.应用气象学报, 2015, 26(2):173-184. doi: 10.11898/1001-7313.20150205 [18] 周迪, 陈静, 陈朝平, 等.暴雨集合预报-观测概率匹配订正法在四川盆地的应用研究.暴雨灾害, 2015, 34(2):97-104. http://www.cnki.com.cn/Article/CJFDTOTAL-HBQX201502001.htm [19] 李俊, 杜钧, 陈超君."频率匹配法"在集合降水预报中的应用研究.气象, 2015, 41(6):674-684. doi: 10.7519/j.issn.1000-0526.2015.06.002 [20] 王雨, 闫之辉.降水检验方案变化对降水检验评估效果的影响分析.气象, 2007, 33(12):53-61. doi: 10.7519/j.issn.1000-0526.2007.12.008 [21] Rodwell M J, Richardson D S, Hewson T D, et al.A new equitable score suitable for verifying precipitation in numerical weatherprediction.Quart J Roy Meteor Soc, 2010, 136:1344-1363. [22] Haiden T M, Rodwell M J, Richardson D S.Intercomparison of global model precipitation forecast skill in 2010/11 using the SEEPS score.Mon Wea Rev, 2012, 140:2720-2733. doi: 10.1175/MWR-D-11-00301.1 [23] 陈法敬, 陈静."SEEPS"降水预报检验评分方法在我国降水预报中的应用试验.气象科技进展, 2015, 5(5):6-14. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201505006.htm [24] Jolliffe I T, Stephenson D B. 预报检验. 李应林, 译. 北京: 气象出版社, 2016: 58-68. [25] 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. doi: 10.11898/1001-7313.20160405 -
计量
- 摘要浏览量: 5716
- HTML全文浏览量: 1620
- PDF下载量: 1565
- 被引次数: 0
