构建准实时海面风场的一种智能算法

刘晓燕; 宋晓姜; 郭安博宇; 郝赛; 彭炜

doi:10.12284/hyxb2024051

构建准实时海面风场的一种智能算法

doi: 10.12284/hyxb2024051

刘晓燕^{1, 2,},
宋晓姜^{1, 2, ,},
郭安博宇^{1, 2},
郝赛^{1, 2},
彭炜^{1, 2}

1.
国家海洋环境预报中心，北京 100081
2.
自然资源部海洋灾害预报技术重点实验室，北京 100081

基金项目: 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)。

详细信息

作者简介:
刘晓燕（1988—），女，山东省日照市人，从事海面风场资料同化与融合研究。E-mail：liuxiaoyan-de@163.com

通讯作者:
宋晓姜（1981—），女，北京市人，正高级工程师，从事海洋气象预报研究。E-mail：xjsong@nmefc.cn

中图分类号: P714⁺.2
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出版历程
- 收稿日期: 2024-01-24
- 修回日期: 2024-05-10
- 网络出版日期: 2024-07-12
- 刊出日期: 2024-06-01

An intelligent algorithm for constructing quasi-real-time sea surface wind field

Liu Xiaoyan^{1, 2
,},
Song Xiaojiang^{1, 2
, ,},
Guo Anboyu^{1, 2},
Hao Sai^{1, 2},
Peng Wei^{1, 2}

1.
National Marine Enviroment Forecasting Center, Beijing 100081 China
2.
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Beijing 100081, China

摘要

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%。
- U-Net /
- CCMP /
- CMA-GFS /
- HY-2B/2C/2D /
- MetOp-B /
- 准实时 /
- 海面风场
Abstract: 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 accomplished 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, respectively.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 direction 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.
- U-Net /
- CCMP /
- CMA-GFS /
- HY-2B/2C/2D /
- MetOp-B /
- quasi-real-time /
- sea surface wind field

HTML全文

图 1 浮标站点位置分布

Fig. 1 Distribution of buoys location

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图 2 准实时海面风场构建流程

Fig. 2 Flow chart of quasi-real-time surface wind field construction

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图 3 U-Net网络结构示意

Fig. 3 Diagram of U-Net network structure

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图 4 CMA-GFS风场订正效果

Fig. 4 CMA-GFS wind field correction effect diagram

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图 5 CMA-GFS风场订正效果（西北太平洋区域）

Fig. 5 CMA-GFS wind field correction effect diagram (Northwest Pacific region)

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图 6 准实时海面风场构建过程示意

Fig. 6 Process of quasi-real-time surface wind field construction

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图 7 海面融合风场构建过程示意

Fig. 7 Process of surface fuisonwind field construction

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图 8 4组数据风速/风向误差时间序列

Fig. 8 Time series of wind speed/direction error for four sets of data

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图 9 5组数据与浮标观测的风速/风向散点图

Fig. 9 Scatter plots of wind speed/direction of five sets of data vs buoys

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图 10 5组数据与浮标观测风速在不同海域的泰勒图

Fig. 10 Tylar diagram of wind speed of five sets of data in different sea areas

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图 11 5组数据在不同浮标站点的风速/风向平均绝对误差情况

Fig. 11 MAE of wind speed/direction at different buoy sites for five sets of data

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表 1 卫星散射计数据平均覆盖率

Tab. 1 Average coverage of satellite scatterometer data

时间	00 UTC	06 UTC	12 UTC	18 UTC
覆盖率	33.05%	42.88%	28.38%	16.51%

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表 2 4组风场数据风速风向检验结果

Tab. 2 Results of wind speed and direction for four sets of wind field data

检验数据	风速				风向
检验数据	ME/(m·s⁻¹)	MAE/(m·s⁻¹)	RMSE/(m·s⁻¹)	CC	ME/(°)	MAE/(°)	RMSE/(°)
CMA-GFS	−0.06	1.13	1.53	0.925	0.5	17.5	31.2
CMA-GFS_Unet	−0.06	0.89	1.21	0.955	0.2	15.5	28.1
Fusion_QRT	−0.06	0.84	1.16	0.958	0.3	16.0	28.8
Fusion	−0.05	0.78	1.09	0.963	0.3	16.4	29.5

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表 3 5组数据风速风向检验结果

Tab. 3 Results of wind speed and direction for five sets of wind field data

海区	检验数据	风速				风向
海区	检验数据	ME/(m·s⁻¹)	MAE/(m·s⁻¹)	RMSE/(m·s⁻¹)	CC	ME/(°)	MAE/(°)	RMSE/(°)
渤海	CCMP	−0.51	1.19	1.55	0.92	−14.7	24.6	32.9
	CMA-GFS	0.71	1.53	1.99	0.85	−2.7	26	37.1
	CMA-GFS_Unet	−0.42	1.37	1.87	0.87	−4.3	25.6	36.3
	Fusion_QRT	−0.76	1.44	1.85	0.89	−9.6	28	39.1
	Fusion	−0.76	1.41	1.81	0.90	−12.2	28.7	39.1
黄海	CCMP	0.65	1.43	1.91	0.83	−2.1	19.5	25.9
	CMA-GFS	1.06	1.72	2.2	0.8	6.6	21.3	30.7
	CMA-GFS_Unet	0.64	1.55	2.05	0.79	3.2	21.1	29.5
	Fusion_QRT	0.5	1.4	1.86	0.81	−1.2	23.1	31.2
	Fusion	0.48	1.36	1.82	0.82	−2.4	23.5	31.6
东海	CCMP	0.04	1.01	1.31	0.89	4.3	23.6	37.7
	CMA-GFS	0.16	1.33	1.74	0.82	9.8	25.4	38.7
	CMA-GFS_Unet	−0.12	1.24	1.61	0.85	6.5	23.7	36.7
	Fusion_QRT	−0.24	1.13	1.5	0.87	2.6	24.3	37.3
	Fusion	−0.21	1.09	1.45	0.88	3.4	24.5	37.6
南海	CCMP	0.08	0.97	1.44	0.86	−7.1	19.7	28
	CMA-GFS	0.03	1.36	1.84	0.75	−0.5	22.8	34.3
	CMA-GFS_Unet	−0.14	1.2	1.6	0.82	−5.2	22.1	32.4
	Fusion_QRT	−0.26	1.15	1.54	0.85	−5.2	22.8	34.1
	Fusion	−0.20	1.12	1.50	0.86	−6.7	22.6	34.0

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