大连地区ECMWF细网格模式阵风预报误差检验与分析

doi:10.11755/j.issn.1006-7639(2024)-01-0129

摘要/Abstract

摘要：

阵风的预报误差检验对实际工作中的精细化预报订正具有一定的指导意义，同时对精细化预报中如何消除误差日变化的影响提供了借鉴。选取2017—2019年3~72 h逐日逐3 h 欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecast，ECMWF）细网格10 m阵风和10 m平均风预报资料，基于大连地区9个国家气象观测站实况逐3 h极大风资料进行预报误差检验分析。结果表明：按预报风级和实况风级分类的预报误差对比检验均表明ECMWF细网格预报整体偏大，平均误差为0.96 m·s^-1，但具体到各风级时两种分类的预报误差统计结论并不一致，按预报风级分类的检验更符合基于模式预报开展的实际预报工作。以预报为基准统计，各风向、各风级、各站的预报误差均差异明显，风级越大预报偏大的程度越高，风向也表现出随风级增大误差增大的趋势。阵风预报的平均误差具有明显日变化，08：00（北京时，下同）前后误差最大，20：00前后误差最小，主要由10 m平均风的平均误差日变化所致。全部预报个例与实况各时效预报相关系数均在0.7以上，具体到各风级、风向时，各风向相关性均较好，而各风级的相关系数则明显降低，8级及以上风力预报的可信度大幅下降。

关键词: ECMWF, 阵风, 对比分析, 模式检验, 日变化

Abstract:

The error test of gust forecast has a certain guiding significance for the refined forecast correction in practice, and provides a reference for how to eliminate the influence of the daily variation of error in the refined forecast. The 10 m gust and 10 m average wind forecast data of the European Centre for Medium-Range Weather Forecast (ECMWF) are selected from the fine grid for 3-72 h day by day from 2017 to 2019, and based on the real maximum wind data 3 hours by 3 hours of 9 national meteorological observation stations in Dalian area, the error test of forecast is analyzed. The results are as follows: According to the forecast error test based on the forecast and the actual situation, the mean error（ME）of the ECMWF fine grid forecast is 0.96 m·s^-1, which indicates that the forecast is larger on the whole. However, the statistical conclusions of the forecast errors of the two classifications are inconsistent for each wind level, and the test according to the forecast wind level is more consistent with the actual forecast work based on the model forecast. According to the statistical test of the forecast, the forecast errors of each wind direction, each wind level and each station are obviously different. The larger the wind level is, the greater the degree of forecast bias is, and wind direction also shows the trend of error increasing with wind level increase. The average error of gust forecast has obvious daily variation, with the largest error around 08:00 and the smallest error around 20:00, which is mainly caused by the daily variation of the average error of 10 m average wind prediction. The correlation coefficients between all forecast cases and the observations for each predictive aging are above 0.7, and when it comes to each wind level and wind direction, the correlation of each wind direction is good, but the correlation of each wind level is significantly reduced, and the reliability of the wind forecast of the magnitude 8 and above is decreased greatly.

Key words: ECMWF, gust, contrastive analysis, model verification, daily variation

中图分类号:

P457.5

王蕾, 杨景泰, 卞若玢, 隋玉秀, 孙悦程, 周丽丽, 魏瑗瑗. 大连地区ECMWF细网格模式阵风预报误差检验与分析[J]. 干旱气象, 2024, 42(1): 129-136.

WANG Lei, YANG Jingtai, BIAN Ruobin, SUI Yuxiu, SUN Yuecheng, ZHOU Lili, WEI Yuanyuan. Verification and analysis of gust forecast of ECMWF fine grid model in Dalian area[J]. Journal of Arid Meteorology, 2024, 42(1): 129-136.

图/表 9

图1 大连地区主要站点分布

Fig.1 Distribution of meteorological stations in Dalian area

表1 2017—2019年大连地区9个站点ECMWF细网格阵风预报和相对应的实况数据

Tab.1 The ECMWF gust forecast and the corresponding observation data from 2017 to 2019 at nine stations in Dalian area

	预报						实况
	起报时间	起报时次	时效	时间	平均风风向/（ $°)$	阵风/（m·s^-1）	时间	风向*/ $(°)$	风速*/（m·s^-1）
大连	2017-01-01	08：00	3 h	2017-01-01T11：00	214	7.2	2017-01-01T11：00	231	7.8
大连	2017-01-01	08：00	6 h	2017-01-01T14：00	217	7.3	2017-01-01T14：00	233	8.1
大连	2017-01-01	08：00	9 h	2017-01-01T17：00	212	6.2	2017-01-01T17：00	228	5.8
$⋮ ⋮$							$⋮ ⋮$
长海	2019-12-31	20：00	66 h	2020-01-03T14：00	230	3.1	2020-01-03T14：00	017	3.4
长海	2019-12-31	20：00	69 h	2020-01-03T17：00	269	5.3	2020-01-03T17：00	070	2.7
长海	2019-12-31	20：00	72 h	2020-01-03T20：00	344	9.1	2020-01-03T20：00	354	5.2

表1 2017—2019年大连地区9个站点ECMWF细网格阵风预报和相对应的实况数据

Tab.1 The ECMWF gust forecast and the corresponding observation data from 2017 to 2019 at nine stations in Dalian area

	预报						实况
	起报时间	起报时次	时效	时间	平均风风向/（ $°)$	阵风/（m·s^-1）	时间	风向*/ $(°)$	风速*/（m·s^-1）
大连	2017-01-01	08：00	3 h	2017-01-01T11：00	214	7.2	2017-01-01T11：00	231	7.8
大连	2017-01-01	08：00	6 h	2017-01-01T14：00	217	7.3	2017-01-01T14：00	233	8.1
大连	2017-01-01	08：00	9 h	2017-01-01T17：00	212	6.2	2017-01-01T17：00	228	5.8
$⋮ ⋮$							$⋮ ⋮$
长海	2019-12-31	20：00	66 h	2020-01-03T14：00	230	3.1	2020-01-03T14：00	017	3.4
长海	2019-12-31	20：00	69 h	2020-01-03T17：00	269	5.3	2020-01-03T17：00	070	2.7
长海	2019-12-31	20：00	72 h	2020-01-03T20：00	344	9.1	2020-01-03T20：00	354	5.2

图2 2017—2019年ECMWF细网格3~72 h阵风和实况风级预报平均误差

Fig.2 The ME of wind speed between the ECMWF gust forecast for 3-72 h validity period and the observation data from 2017 to 2019

表2 2017—2019年预报及观测的不同风级阵风站次数统计

Tab.2 Station numbers statistics of gust with different wind levels forecasted and observed from 2017 to 2019

预报	实况									预报合计
预报	3级以下	4级	5级	6级	7级	8级	9级	10级	11级以上	预报合计
3级以下	91 873	16 759	2 508	427	90	5	4	1		111 667
4级	57 983	52 749	15 994	2 122	265	35	4	1		129 153
5级	17 812	40 975	42 319	11 126	1 248	97	18			113 595
6级	2 503	7 042	19 665	21 769	5 281	532	48	3	3	56 846
7级	138	561	1 735	6 253	7 358	2 144	155	11	3	18 358
8级	4	26	71	442	1 649	1 683	268	52		4 195
9级	1	5	17	30	113	168	95	35	6	470
10级			1	4	19	19	11	4		58
11级					5	4	2			11
实况合计	170 314	118 117	82 310	42 173	16 028	4 687	605	107	12

表3 2017—2019年ECMWF细网格3~72 h阵风预报不同风级阵风风速与实况极大风速的相关系数

Tab.3 The correlation coefficients between wind speed of gust with different levels forecasted by the ECMWF for 3-72 h validity period and the observed maximum wind speed from 2017 to 2019

预报时效	预报风级								全部风级
预报时效	3级及以下	4级	5级	6级	7级	8级	9级	10级	全部风级
3 h	0.305	0.316	0.298	0.353	0.376	0.222	0.236*	0.147*	0.831
12 h	0.296	0.305	0.319	0.303	0.324	0.238	-0.125*		0.803
15 h	0.302	0.28	0.287	0.315	0.386	0.058*	0.249*	-0.832*	0.819
24 h	0.282	0.293	0.307	0.272	0.314	0.272	-0.157*	0*	0.785
36 h	0.256	0.233	0.302	0.287	0.367	0.179*	-0.298*	0.207*	0.762
48 h	0.244	0.27	0.274	0.271	0.316	0.299	-0.082*		0.749
60 h	0.228	0.239	0.26	0.286	0.316	-0.013*	-0.252*	0*	0.737
72 h	0.204	0.246	0.228	0.225	0.346	-0.028*	-0.564*	0.994*	0.711

图3 2017—2019年ECMWF细网格3~72 h预报时效逐3 h阵风预报的ME箱线图

Fig.3 The ME box plots of 3-hour gust forecasted by the ECMWF for 3-72 h validity period from 2017 to 2019

表4 2017—2019年ECMWF细网格3~24 h预报时效不同风级、风向阵风预报ME统计

Tab.4 The statistics of ME of gust with different wind levels and direction forecasted by the ECMWF for 3-24 h validity period from 2017 to 2019

预报风级	北风	东北风	东风	东南风	南风	西南风	西风	西北风
3级及以下	0.67	0.50	0.14	-0.02	0.18	0.50	0.69	0.72
4级	1.12	1.21	0.85	0.61	0.87	1.08	1.04	1.10
5级	1.20	1.47	1.55	1.14	1.47	1.48	1.22	1.27
6级	1.09	1.34	2.19	1.58	2.10	1.70	1.17	1.42
7级	0.71	0.37	3.11	2.62	2.41	1.68	1.38	2.12
8级	0.34	0.87	3.80	3.34	4.76	0.82	1.69	2.60
9级	0.55	5.24	5.20	3.88	6.39	7.92	3.60	4.39
10级	4.87		6.65	1.90	1.10			1.90
11级	6.30
不分级误差	0.89	0.89	0.87	0.70	1.01	1.09	0.99	1.15

图4 2017—2019年ECMWF细网格3~24 h预报时效阵风、平均风ME日变化（a）与叠加阵风ME及ECMWF 10 m平均风与实况最大风速平均值日变化（b）

Fig.4 The diurnal variations of ME of gust， average wind speed of ECMWF for 3-24 h validity period （a） and superimposed gust ME and ECMWF 10 m average wind speed as well as observed mean maximum wind speed （b） from 2017 to 2019

图5 2017—2019年ECMWF细网格3~24 h预报时效不同风级（a）与风向（b）阵风预报ME分布

Fig.5 The distribution of ME of gust with different wind speed （a） and wind direction （b） forecasted by the ECMWF for 3-24 h validity period from 2017 to 2019

参考文献 22

[1]	查书瑶, 陈潇潇, 沈雨辰, 等, 2020. 2017年3月江苏省一次伴有对流激发的冷空气大风分析[J]. 沙漠与绿洲气象, 14(2): 36-42.
[2]	方艳莹, 申华羽, 涂小萍, 等, 2019. ECMWF 细网格对浙江沿海 10 m风预报性能评估[J]. 中国农学通报, 35(13): 119-125. DOI
[3]	李玲萍, 胡丽莉, 刘维成, 等, 2017. 河西走廊东部夏季沙尘暴气象要素变化特征[J]. 干旱气象, 35(3): 427-432. DOI
[4]	李昱茜, 杨景泰, 隋玉秀, 等, 2022. 大连地区阵风系数的特征分析[J]. 气象与环境学报, 38(5): 98-105.
[5]	连喜虎, 刘桂艳, 高松, 等, 2020. EC细网10 m风场产品在渤、黄海海域的预报能力检验与评估[J]. 海洋预报, 37(6): 74-82.
[6]	申华羽, 方艳莹, 涂小萍, 等, 2020. ECMWF细网格10 m风预报在浙江沿海的评估与订正[J]. 气象, 46(11): 1 486-1 494.
[7]	隋玉秀, 杨景泰, 常慧琳, 等, 2022. 大连地区ECMWF细网格风向与实况的对比检验[J]. 海洋预报, 39(1): 21-31.
[8]	韦惠红, 吴翠红, 魏凡, 等, 2023. 湖北雷暴阵风锋特征及其对流触发作用分析[J]. 干旱气象, 41(1): 73-81. DOI
[9]	吴焕波, 陈强, 谷新波, 等, 2018. 一次强沙尘天气过程及其对PM10时空分布的影响[J]. 干旱气象, 36(1): 117-123. DOI
[10]	吴俞, 冯文, 李勋, 等, 2015. ECMWF细网格10 m风场产品在南海海域的预报检验[J]. 西南师范大学学报(自然科学版), 40(9): 204-212.
[11]	杨景泰, 隋玉秀, 肖剑, 等, 2021. 大连地区ECMWF细网格10 m风场与观测实况对比与检验[J]. 气象与环境学报, 37(1): 73- 81.
[12]	ASHLEY W S, BLACK A W, 2008. Fatalities associated with nonconvective high-wind events in the United States[J]. Journal of Applied Meteorology and Climatology, 47(2): 717-725.
[13]	BECHTOLD P, BIDLOT J R, 2009. Parametrization of convective gusts[J]. ECMWF Newsletter, 119: 15-18.
[14]	BELJAARS A C M, 1987. The influence of sampling and ﬁltering on measured wind gusts[J]. Journal of Atmospheric & Oceanic Technology, 4(4): 613-626.
[15]	ECMWF, 2018. IFS Documentation CY45R1-Part IV: Physical processes[M]. ECMWF: 50- 54. DOI: 10.21957/4whwo8jw0.
[16]	HARRIS A R, KAHL J D W, 2017. Gust factors: Meteorologically stratified climatology, data artifacts, and utility in forecasting peak gusts[J]. Journal of Applied Meteorology and Climatology, 56: 3 151-3 166.
[17]	KAHL J D W, 2020. Forecasting peak wind gusts using meteorologically stratified gust factors and MOS guidance[J]. Weather and Forecasting, 35(3): 1 129-1 143.
[18]	LETSON F, PRYOR S C, BARTHELMIE R J, et al, 2018. Observed gust wind speeds in the coterminous United States, and their relationship to local and regional drivers[J]. Journal of Wind Engineering & Industrial Aerodynamics, 173: 199-209. DOI: 10.1016/j.jweia.2017.12.008.
[19]	PERSSON A, GRAZZINI F, 2007. User guide to ECMWF forecast products4.0[M]. ECMWF:1-162.
[20]	PINSON P, HAGEDORN R, 2012. Verification of the ECMWF ensemble forecasts of wind speed against analyses and observations[J]. Meteorological Applications, 19: 484-500. DOI: 10.1002/met.283.
[21]	SHERIDAN P, 2011. Review of techniques and research for gust forecasting and parameterisation[R]// Forecasting Research Technical Report 570. DOI: http://dx.doi.org/.
[22]	SHERIDAN P, 2018. Current gust forecasting techniques, developments and challenges[J]. Advances in Science and Research, 15: 159-172.