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NCEP/NOAA全球预报产品在江苏近岸及长江口外的精度评估
作者:袁祖晴1  高寒旭1  胡翌哲1  燕俊羽2  张国胜1  王晓春1  徐淑雯3  陈智强4  陈旻豪4 
单位:1. 南京信息工程大学海洋科学学院, 江苏 南京 210044;
2. 南京信息工程大学大气物理学院, 江苏 南京 210044;
3. 自然资源部南通海洋中心, 江苏 南通 226000;
4. 上海海洋中心气象台, 上海 200000
关键词:江苏近岸及长江口外 预报评估 风场 有效波高 长短期记忆网络 海上风电业 
分类号:P731.3
出版年·卷·期(页码):2024·41·第四期(32-42)
摘要:
利用2021年1—7月江苏近岸和长江口外13个测风站、3个波浪浮标的观测数据,与美国国家环境预报中心/美国国家海洋和大气管理局(NCEP/NOAA)全球预报系统的风场、波浪预报数据进行对比。结果表明:与上一代预报产品相比,目前全球业务预报产品在风场预报方面的准确度明显提高,江苏近岸地区的风速预报误差明显小于长江口外地区,24 h风速和风向预报的均方根误差分别为 2 m/s和 45°。对大风天、大浪天环境下全球预报产品精度的评估表明:大风天风速预报精度在长江口外降低,在江苏近岸无明显变化,风向预报精度在长江口外无明显变化,在江苏近岸提高;大浪天波高、浪向的预报精度分别降低、提高。在台风极端天气条件下,全球预报产品对长江口的风场仍有一定的预报能力,但预报的最大风速的出现时间滞后6~9 h,且无法预报出风速剧烈变化的情况。尝试基于长短期记忆网络方法利用NCEP/NOAA预报及观测改进业务预报,可以改善短期单站预报水平。
The observation data of 13 wind stations and 3 wave buoys around Jiangsu coastal region and Yangtze River estuary from January to July 2021 are used to compare with the wind and wave forecasts from the National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration (NCEP/NOAA) global forecasting system. The results show that compared with the previous generation version of the NCEP/NOAA global forecasting system, the current version improves significantly in wind forecasts. The wind speed forecast error outside the Yangtze River estuary is greater than that in the Jiangsu coastal region, where the root mean squared errors of 24 h wind speed and direction forecasts are 2 m/s and 45 degrees, respectively. Wind speed forecasting accuracy on windy days decreases outside the Yangtze River estuary, but does not change obviously in the Jiangsu coastal region. Wind direction forecasting accuracy does not change obviously outside the Yangtze River estuary, but increases in the Jiangsu coastal region. The accuracy of wave height and direction forecasts on high wave days decreases and increases, respectively. Under extreme weather conditions, the global forecasting system also demonstrates capability to forecast wind around the Yangtze River estuary, but the timing of maximum wind speed is delayed by 6~9 h, and the drastic wind speed change cannot be predicted. Combing the Long Short-Term Memory method and the NCEP/NOAA data can improve the short-term single-station forecast.
参考文献:
[1] 李英, 王淼. 我国海上风电发展面临的挑战与法律建议[J]. 大众用电, 2019(6): 6-7. LI Y, WANG M. Challenges and legal suggestions for development of offshore wind power in China[J]. Popular Utilization of Electricity, 2019(6): 6-7.
[2] 中国可再生能源学会风能专业委员会. 2021年中国海上风电装机统计[J]. 风能, 2022(8): 46-49. Wind Energy Professional Committee of China Renewable Energy Society. Statistics of Chinese offshore wind power installed capacity in 2021[J]. Wind Energy, 2022(8): 46-49.
[3] 夏云峰. 2021年全球新增风电装机93.6 GW[J]. 风能, 2022(6): 38-43. XIA Y F. 93.6 GW of new wind power installed capacity in 2021[J]. Wind Energy, 2022(6): 38-43.
[4] 邓心怡, 黎北梅, 王晓春, 等. 2019年8—10月长江口近岸海上风电场风浪预报[J]. 海洋预报, 2021, 38(4): 45-52. DENG X Y, LI B M, WANG X C, et al. Wind and ocean wave forecasts for the offshore wind farms near the Changjiang River estuary from August to October 2019[J]. Marine Forecasts, 2021, 38(4): 45-52.
[5] LIN S J. A "Vertically Lagrangian" finite-volume dynamical core for global models[J]. Monthly Weather Review, 2004, 132(10): 2293-2307.
[6] GUAN H, ZHU Y, SINSKY E, et al. The NCEP GEFS-v12 reforecasts to support subseasonal and hydrometeorological applications[R]. STI Climate Bulletin, 2020: 79-82.
[7] ATLAS R, HOFFMAN R N, ARDIZZONE J, et al. A crosscalibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications[J]. Bulletin of the American Meteorological Society, 2011, 92(2): 157-174.
[8] 唐飞, 陈凤娇, 诸葛小勇, 等. 利用卫星遥感资料分析台风“烟花" (202106)的影响过程[J]. 大气科学学报, 2021, 44(5): 703-716. TANG F, CHEN F J, ZHUGE X Y, et al. Analysis of influence process of Typhoon In-Fa (202106) based on satellite remote sensing data[J]. Transactions of Atmospheric Sciences, 2021, 44(5): 703-716.
[9] 李扬, 刘玉宝, 许小峰. 基于深度学习改进数值天气预报模式和预报的研究及挑战[J]. 气象科技进展, 2021, 11(3): 103-112. LI Y, LIU Y B, XU X F. Advances and challenges for improving numerical weather prediction models and forecasting using deep learning[J]. Advances in Meteorological Science and Technology, 2021, 11(3): 103-112.
[10] SCHULTZ M G, BETANCOURT C, GONG B, et al. Can deep learning beat numerical weather prediction? [J]. Philosophical Transactions of the Royal Society Series A: Mathematical, Physical and Engineering Sciences, 2021, 379(2194): 20200097.
[11] 王国松, 王喜冬, 侯敏, 等. 基于观测和再分析数据的LSTM深度神经网络沿海风速预报应用研究[J]. 海洋学报, 2020, 42(1): 67-77. WANG G S, WANG X D, HOU M, et al. Research on application of LSTM deep neural network on historical observation data and reanalysis data for sea surface wind speed forecasting[J]. Haiyang Xuebao, 2020, 42(1): 67-77.
[12] JASEENA K U, KOVOOR B C. Deep learning based multi-step short term wind speed forecasts with LSTM[C]//Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (DATA'19). Dubai: Association for Computing Machinery, 2019: 7.
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