摘要
本文基于深度学习U-Net网络构建了CMA-GFS数值模式风场订正模型,并以此订正模型订正后的风场为背景场(CMA-GFS_Unet),以HY-2B/2C/2D以及MetOp-B 4颗卫星的散射计海面风资料为观测资料,采用插补法快速完成准实时海面风场的构建。此智能算法可实现滞后3 h准实时生成空间分辨率为0.25°、时间分辨率为6 h的全球海面融合风场(Fusion_QRT)。分别使用CCMP融合风场数据和中国近海浮标10 m风矢量数据对CMA-GFS、CMA-GFS_Unet和Fusion_QRT 3组风场资料进行了评估,结果表明,CMA-GFS_Unet风场质量得到显著提升,Fusion_QRT风场风速质量得到进一步改善,但风向质量略有降低:相较于CCMP,3组风场的风速平均绝对误差(MAE)分别为1.13 m/s、0.89 m/s和0.84 m/s,CMA-GFS_Unet和Fusion_QRT相较于CMA-GFS分别提升了21.3%和25.7%;风向MAE分别为17.5°、15.5°和16°,分别提升了11.3%和8.6%;而相较于浮标,风速MAE分别为1.50 m/s、1.36 m/s和1.28 m/s,分别提升了9.3%和14.7%;风向MAE分别为23.3°、22.7°和24.0°,分别提升了3.0%和-3.9%。
In this paper,the correction model of CMA-GFS numerical model wind field is constructed based on the deep learning U-Net network,and the construction of the quasi-real-time sea surface wind field is rapidly accom-plished by interpolation method using the corrected wind field with the correction model as the background field(CMA-GFS_Unet),and using the scatterometer sea surface wind data from the four satellites,namely,HY-2B/2C/2D and MetOp-B as the observation data.This intelligent algorithm can realize the generation of global sea surface fusion wind field(Fusion_QRT)with a spatial resolution of 0.25°and a temporal resolution of 6 hours in quasi-real time with a lag of 3 hours.The CMA-GFS,CMA-GFS_Unet and Fusion_QRT wind fields are evaluated using the CCMP fusion wind field data and the 10 m wind vector data from the Chinese offshore buoys,respect-ively.The results show that the quality of the CMA-GFS_Unet wind field has been significantly improved,and the quality of the wind speed of the Fusion_QRT wind field has been further improved but the quality of the wind direc-tion has been slightly reduced.The mean absolute errors(MAEs)of wind speed are 1.13 m/s,0.89 m/s and 0.84 m/s for the three wind fields by using CCMP data as reference,and the CMA-GFS_Unet and Fusion_QRT wind fields have improved by 21.3%and 25.7%compared to the CMA-GFS,respectively;while the MAEs of wind direction are 17.5°,15.5°and 16°,and have improved by11.3%and 8.6%,respectively.The MAEs of wind speed are 1.50 m/s,1.36 m/s and 1.28 m/s for the three wind fields by using buoy data as reference,and have improved by 9.5%and 14.7%,respectively;while the MAEs of wind direction are 23.3°,22.7°and 24.0°,and have improved by 3.0%and-3.9%,respectively.
作者
刘晓燕
宋晓姜
郭安博宇
郝赛
彭炜
Liu Xiaoyan;Song Xiaojiang;Guo Anboyu;Hao Sai;Peng Wei(National Marine Enviroment Forecasting Center,Beijing 100081 China;Key Laboratory of Marine Hazards Forecasting,Ministry of Natural Resources,Beijing 100081,China)
出处
《海洋学报》
CAS
CSCD
北大核心
2024年第6期51-65,共15页
基金
国家重点研发计划(2023YFC3107901)
自然资源部空间海洋遥感与应用研究重点实验室开放基金(202102004)。
