A near-real-time blended sea surface wind data product from multiple satellites

Zou Juhong; Lin Wenming; Lu Sirui; Wang Zhixiong; Lin Mingsen

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Zou Juhong，Lin Wenming，Lu Sirui, et al. A near-real-time blended sea surface wind data product from multiple satellites[J]. Haiyang Xuebao，2025, 47(x)：1–10

Citation:

Zou Juhong，Lin Wenming，Lu Sirui, et al. A near-real-time blended sea surface wind data product from multiple satellites[J]. Haiyang Xuebao，2025, 47(x)：1–10

Citation:

Zou Juhong，Lin Wenming，Lu Sirui, et al. A near-real-time blended sea surface wind data product from multiple satellites[J]. Haiyang Xuebao，2025, 47(x)：1–10

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A near-real-time blended sea surface wind data product from multiple satellites

1.
National Satellite Ocean Application Service, Beijing 100081, China
2.
Key Laboratory of Space Ocean Remote Sensing and Application, Beijing 100081, China
3.
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
4.
Qingdao 266100, China

Received Date: 2024-07-26
Rev Recd Date: 2025-01-14

Available Online: 2025-02-12

Abstract

Abstract

A near-real-time version of the blended sea surface wind (BSSW) data product from multiple satellites, as well as the data processing method, and data accuracy analysis is introduced in this paper. The BSSW used sea surface winds provided by the virtual satellite constellation composed of HY-2 series satellites, Metop series satellites and DMSP series satellites as input. Error analysis, cross-calibration and 2D-Var processing is applied to blend these winds derived from different platform. With these methods, a near-real-time blended sea surface product with 6 hours interval and a spatial resolution of 25 kilometers is produced and released operationally by National Satellite Ocean Satellite Application Service. Comparing to buoy data, the RMSE is below 1.6 m/s for wind speed and below 19° for wind direction. While comparing to ERA5 data, the RMSE is below 1.2 m/s for wind speed and below 11° for wind direction. The validation results show that the BSSW is consistent with the buoy winds and ERA5 winds, indicating that BSSW can be of great importance to ocean and atmospheric numerical forecast model, marine disaster prevention and reduction, as well as scientific research on ocean.
- sea surface wind,
- sea surface wind Fusion,
- scatterometer,
- microwave radiometer,
- 2D-Var

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References(17)

References

[1]	GCOS. Implementation plan for the global observing system for climate in support of the UNFCCC[R]. GCOS-138, Geneva: GCOS, 2010.
[2]	Marseille G, Stoffelen A. Toward scatterometer winds assimilation in the mesoscale HARMONIE model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(5): 2383−2393. doi: 10.1109/JSTARS.2016.2640339
[3]	Valkonen T, Schyberg H, Figa-Saldaña J. Assimilating Advanced Scatterometer winds in a high-resolution limited area model over Northern Europe[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(5): 2394−2405. doi: 10.1109/JSTARS.2016.2602889
[4]	Atlas R, Hoffman R N, Bloom S C, et al. A multiyear global surface wind velocity dataset using SSM/I wind observations[J]. Bulletin of the American Meteorological Society, 1996, 77(5): 869−882. doi: 10.1175/1520-0477(1996)077<0869:AMGSWV>2.0.CO;2
[5]	Atlas R, Hoffman R N, Ardizzone J, et al. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications[J]. Bulletin of the American Meteorological Society, 2011, 9(2): 157−174.
[6]	Yu Lisan, Jin Xiangze. Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)[J]. Journal of Geophysical Research: Oceans, 2014, 119(8): 5244−5269. doi: 10.1002/2013JC009648
[7]	Zhang Huaimin, Reynolds R W, Bates J J. Blended and gridded high resolution global sea surface wind speed and climatology from multiple satellites: 1987-present[C]//Proceedings of the 14th Conference on Satellite Meteorology and Oceanography. Atlanta: American Meteorological Society, 2006.
[8]	NCEP/NOAA. Global data assimilation system[R/OL]. NESDIS, NOAA. http://www.ncep.noaa.gov/. (查阅网上资料,未找到本条文献信息且网址打不开,请确认)
[9]	Wentz F J, Scott J, Hoffman R. et al. Remote sensing systems cross-calibrated multi-platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 3.1[R]. Santa Rosa, CA: Remote Sensing Systems, 2022. (查阅网上资料, 未找到对应的作者信息, 请确认)
[10]	Mears C A, Scott J, Wentz F J, et al. A near-real-time version of the cross-calibrated multiplatform (CCMP) ocean surface wind velocity data set[J]. Journal of Geophysical Research: Oceans, 2019, 124(10): 6997−7010. doi: 10.1029/2019JC015367
[11]	高健. 多源测风资料融合技术研究[D]. 杭州: 杭州师范大学, 2015. Gao Jian. Research on multi-source wind data fusion technology[D]. Hangzhou: Hangzhou Normal University, 2015.
[12]	许遐祯, 高健, 张康宇, 等. 基于多源数据的中国近海风场融合方法研究[J]. 杭州师范大学学报(自然科学版), 2016, 15(3): 325−330. Xu Xiazhen, Gao Jian, Zhang Kangyu, et al. Fusion method of China's offshore wind field based on multi-source data[J]. Journal of Hangzhou Normal University (Natural Science Edition), 2016, 15(3): 325−330.
[13]	项杰, 王慧鹏, 王春明, 等. 南海海面风场变分融合的初步研究[J]. 热带气象学报, 2015, 31(2): 153−160. Xiang Jie, Wang Huipeng, Wang Chunming, et al. Preliminary study on variational blending of surface vector winds in South China sea[J]. Journal of Tropical Meteorology, 2015, 31(2): 153−160.
[14]	Liu W T, Katsaros K B, Businger J A. Bulk parameterization of air-sea exchanges of heat and water vapor including the molecular constraints at the interface[J]. Journal of the Atmospheric Sciences, 1979, 36(9): 1722−1735. doi: 10.1175/1520-0469(1979)036<1722:BPOASE>2.0.CO;2
[15]	吕思睿, 林文明, 邹巨洪, 等. 多源卫星遥感海面风速误差分析和交叉标定[J]. 海洋学报, 2023, 45(5): 118−128. Lü Sirui, Lin Wenming, Zou Juhong, et al. Error quantification and cross calibration of sea surface wind speeds from multiple remote sensing satellites[J]. Haiyang Xuebao, 2023, 45(5): 118−128.
[16]	Vogelzang J, King G P, Stoffelen A. Spatial variances of wind fields and their relation to second-order structure functions and spectra[J]. Journal of Geophysical Research: Oceans, 2015, 120(2): 1048−1064. doi: 10.1002/2014JC010239
[17]	Hoffman R N, Leidner S M, Henderson J M, et al. A two-dimensional variational analysis method for NSCAT ambiguity removal: methodology, sensitivity, and tuning[J]. Journal of Atmospheric and Oceanic Technology, 2003, 20(5): 585−605. doi: 10.1175/1520-0426(2003)20<585:ATDVAM>2.0.CO;2