宁波舟山港南大风时空分布特征及EC细网格模式评估-海洋预报

宁波舟山港南大风时空分布特征及EC细网格模式评估

单位：1. 浙江省宁波市气象服务中心, 浙江宁波 315000;
2. 浙江省宁波市鄞州区气象台, 浙江宁波 315100

分类号：P445

出版年·卷·期（页码）：2024·41·第六期（32-43）

摘要：

利用气象观测站小时和分钟数据，结合港口调度中心和海事局提供的封航资料，挑选2013—2022年南大风过程，探讨港区南大风时空分布特征，归纳天气分型，计算阵风系数，最后评估EC细网格10 m风产品的预报性能。结果表明：10 年间南大风过程总计71次，天气形势主要有低压入海型（26 次）、高压后部型（26 次）和热带气旋型（19 次）。宁波舟山港南大风阵风系数为1.61，略大于浙江沿海冷空气大风系数1.5，而小于强对流大风系数1.8。南大风过程中偏南风和东南风较多，西南风较少。从时间分布看，7月、8月最易出现南大风，4月、5月次之，傍晚—上半夜最易出现南大风，而早晨—上午出现较少。从空间分布看，高频站点多出现在舟山片区，而宁波片区极少，可见宁波区域地理位置存在避风特性，有利于在南大风过程中开展生产作业。EC细网格对南大风过程的最大风速预报总体偏小，其中热带气旋型12 h预报的平均误差为-3.5 m/s，均方根误差为 4.9 m/s，风速预报误差最小，预报稳定性最好，预报水平总体优于低压入海型和高压后部型；分区预报中，宁波片区的预报效果略优于舟山片区；无论是分区域或是分型，EC细网格的预报水平从 72 h起明显下降。EC 细网格对东南风向的预报效果最优，且宁波片区的预报效果略优于舟山片区，对低压入海型和高压后部型的风向预报稳定性优于热带低压型。

Hourly and minute data from meteorological observations, sealing data provided by the port dispatch center and maritime bureau are used to discuss temporal-spatial distribution of south gale in Ningbo-Zhoushan port during 2013-2022, with emphasize on south gale processes, the associated weather situations, gust factors, as well as evaluation of 10 m wind forecasts from the EC fine model. The results show that there are 71 south gale processes in the 10 years under three weather situations, including depression move onto the sea (26 times), ground high back (26 times), and tropical cyclone (19 times). Gust factor of south gale in Ningbo-Zhoushan Port is 1.61, slightly higher than 1.5 of cold air mass and lower than 1.8 of abruptly severe convection. During south gale processes, the proportion of south - southeast wind direction is higher than that of southwest. Temporal distribution analysis reveals that south gale is most likely to occur in July-August followed by April-May, with high frequency in the evening while low frequency in the morning. As the wind speed grade increases, the total hour for each grade decreases gradually. Spatial distribution analysis reflects that stations with high frequency report of south gale locate in Zhoushan, the stations in Ningbo area are less affected by south gale which is suitable for production operation. Evaluation of the EC model reveals that the predicted wind speeds are lower than the observation, with the best performance under tropical cyclone (E_M and E_RMS for 12 h forecasts is -3.5 m/s and 4.9 m/s respectively), and the errors are relatively higher and the stability is relatively lower for the other two weather types. The EC forecasting performance in Ningbo is slightly better than that in Zhoushan, and the forecasting accuracy significant decreases from 72 h onward. The EC forecasts have the best performance for southeast wind direction. In addition, the wind direction forecasting stability in types of depression move onto the sea and ground high back are better than that of tropical cyclone.

参考文献：

[1] 涂小萍, 姚日升, 廉亮, 等. 宁波沿海灾害性大风特征[M]. 北京: 气象出版社, 2018. TU X P, YAO R S, LIAN L, et al. Characteristics of catastrophic gale in the coast of Ningbo[M]. Beijing: China Meteorological Press, 2018.
[2] 卢美, 朱业. 浙江沿海大风的天气气候特征[J]. 杭州师范大学学报(自然科学版), 2011, 10(5): 474-480. LU M, ZHU Y. Weather and climate characteristics of the coastal gale in Zhejiang[J]. Journal of Hangzhou Normal University (Natural Science Edition), 2011, 10(5): 474-480.
[3] 姚霞, 王杰, 史学凡. 1993—2020年北仑大风特征及天气分型的分析[J]. 农业灾害研究, 2021, 11(9): 40-43. YAO X, WANG J, SHI X F. Analysis of gale characteristics and weather classification in Beilun from 1993 to 2020[J]. Journal of Agricultural Catastropholgy, 2021, 11(9): 40-43.
[4] 曹宗元, 王坚侃, 何晴, 等. 浙江沿海大风气候特征及影响系统分析[J]. 海洋预报, 2023, 40(2): 89-97. CAO Z Y, WANG J K, HE Q, et al. Analysis of the climatic characteristics and influence system of strong wind along the coast of Zhejiang[J]. Marine Forecasts, 2023, 40(2): 89-97.
[5] 项素清. 浙北沿海南大风过程诊断分析[J]. 海洋预报, 2004, 21(1): 75-80. XIANG S Q. Diagnostic analysis of the south gale in the north coast of Zhejiang Province[J]. Marine Forecasts, 2004, 21(1): 75-80.
[6] 董加斌, 胡波. 浙江沿海大风的天气气候概况[J]. 台湾海峡, 2007, 26(4): 476-483. DONG J B, HU B. Synoptic and climatic survey of the gale in the coast of Zhejiang Province[J]. Journal of Oceanography in Taiwan Strait, 2007, 26(4): 476-483.
[7] 许爱华, 张瑛, 刘献耀. 江西“暖区”强对流天气的热力和动力条件诊断分析[J]. 气象, 2001, 27(5): 30-34. XU A H, ZHANG Y, LIU X Y. Diagnosis of thermal and dynamic conditions of warm area severe convection in Jiangxi Province[J]. Meteorological Monthly, 2001, 27(5): 30-34.
[8] 于波, 荆浩, 孙继松, 等. 北京夏季一次罕见偏南大风天气的成因分析[J]. 高原气象, 2017, 36(6): 1674-1681. YU B, JING H, SUN J S, et al. The analysis on an infrequent southerly gust of Beijing in summer[J]. Plateau Meteorology, 2017, 36(6): 1674-1681.
[9] 唐毓羚. 四川白鹤滩一次罕见秋冬10级偏南大风过程分析[J]. 成都信息工程大学学报, 2023, 38(1): 70-74. TANG Y L. Diagnosis and analysis of a rare 10-level southerly windy weather at Sichuan Baihetan Hydropower Station[J]. Journal of Chengdu University of Information Technology, 2023, 38(1): 70-74.
[10] 申华羽, 方艳莹, 涂小萍, 等. ECMWF细网格10 m风预报在浙江沿海的评估与订正[J]. 气象, 2020, 46(11): 1485-1494. SHEN H Y, FANG Y Y, TU X P, et al. Verification of ECMWF 10 m wind forecast for coastal Zhejiang Province[J]. Meteorological Monthly, 2020, 46(11): 1485-1494.
[11] 连喜虎, 刘桂艳, 高松, 等. EC细网格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, 2020, 37(6): 74-82. LIAN X H, LIU G Y, GAO S, et al. Evaluation of the ECMWF forecast skills for the 10 m wind field in the Bohai Sea and the Yellow Sea[J]. Marine Forecasts, 2020, 37(6): 74-82.
[12] 曾瑾瑜, 刘爱鸣, 高珊, 等. 福建省沿海冬半年东北大风的数值预报释用方法研究[J]. 海洋预报, 2015, 32(5): 61-68. ZENG J Y, LIU A M, GAO S, et al. Study on interpretation of numerical prediction method on northeast winds along the coast of Fujian Province in winter half year[J]. Marine Forecasts, 2015, 32(5): 61-68.
[13] 隋玉秀, 杨景泰, 常慧琳, 等. 大连地区ECMWF细网格风向与实况的对比检验[J]. 海洋预报, 2022, 39(1): 21-31. SUI Y X, YANG J T, CHANG H L, et al. Comparison and validation of ECMWF fine-grid wind direction and observation data in Dalian area[J]. Marine Forecasts, 2022, 39(1): 21-31.
[14] 於敏佳, 刘菡, 李晓丽. 舟山风力智能网格精细化订正释用技术[J]. 海洋预报, 2021, 38(6): 48-55. YU M J, LIU H, LI X L. Refined intelligent grid correction and interpretation technology for the wind field in Zhoushan area[J]. Marine Forecasts, 2021, 38(6): 48-55.
[15] 孙全德, 焦瑞莉, 夏江江, 等. 基于机器学习的数值天气预报风速订正研究[J]. 气象, 2019, 45(3): 426-436. SUN Q D, JIAO R L, XIA J J, et al. Adjusting wind speed prediction of numerical weather forecast model based on machine learning methods[J]. Meteorological Monthly, 2019, 45(3): 426-436.
[16] 刘敏, 漆梁波. 嵊泗站大风的统计特征及其与平均风的关系[J]. 大气科学研究与应用, 2010(2): 83-92. LIU M, QI L B. Statistic characteristics of gale in Shengsi station and the relationship between gale and average wind force[J]. Atmospheric Science Research and Application, 2010(2): 83-92.
[17] 魏凤英. 现代气候统计诊断与预测技术[M]. 3版. 北京: 气象出版社, 2022. WEI F Y. Modern climate statistical diagnosis and prediction technology[M]. 3rd ed. Beijing: China Meteorological Press, 2022.
[18] World Meteorological Organization. Guidelines for converting between various wind averaging periods in tropical cyclone conditions[R]. Geneva: WMO, 2010.
[19] 杨忠恩, 陈淑琴, 黄辉. 舟山群岛冬半年灾害性大风的成因与预报[J]. 应用气象学报, 2007, 18(1): 80-85. YANG Z E, CHEN S Q, HUANG H. The causes of catastrophic gales in Zhoushan Islands with their forecasting[J]. Journal of Applied Meteorological Science, 2007, 18(1): 80-85.
[20] 周福, 蒋璐璐, 涂小萍, 等. 浙江省几种灾害性大风近地面阵风系数特征[J]. 应用气象学报, 2017, 28(1): 119-128. ZHOU F, JIANG L L, TU X P, et al. Near-surface gust factor characteristics in several disastrous winds over Zhejiang Province [J]. Journal of Applied Meteorological Science, 2017, 28(1): 119-128.
[21] 侯淑梅, 张少林, 盛春岩, 等. T639数值预报产品对黄渤海沿海大风预报效果检验[J]. 海洋预报, 2014, 31(6): 48-56. HOU S M, ZHANG S L, SHENG C Y, et al. Evaluation of T639 numerical forecast products during the strong gales in the Yellow Sea and Bohai Sea[J]. Marine Forecasts, 2014, 31(6): 48-56.

服务与反馈：

【文章下载】【发表评论】【查看评论】【加入收藏】