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Validation of Wave Spectral Partitions from SWIM instrument on-board CFOSAT against In-situ Data
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  • Haoyu Jiang,
  • Alexey S Mironov,
  • Lin Ren,
  • Alexander V Babanin,
  • Lin Mu
Haoyu Jiang
China University of Geosciences

Corresponding Author:lancelotjhy@163.com

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Alexey S Mironov
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Lin Ren
Second Institute of Oceanography, Ministry of Natural Resources
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Alexander V Babanin
Department of Infrastructure Engineering, University of Melbourne
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Lin Mu
College of Oceanography, China University of Geosciences
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The Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China France Oceanography Satellite (CFOSAT) can retrieve directional wave spectra with a wavelength range of 70~500 m. This study aims to validate the partitioned integrated wave parameters (PIWPs) from SWIM, including partitioned significant wave height (PSWH), peak wave period (PPWP), and peak wave direction (PPWD), against those from National Data Buoy Center (NDBC) buoys. With quasi-simultaneous spectra from two NDBC buoys 13 km away from each other near Hawaii, the methods of comparing PIWPs from two sets of spectra were discussed first. After cross-assigning partitions according to the spectral distance, it is found that wrong cross-assignments lead to many outliers strongly impacting the estimate of error metrics. Three methods, namely comparing only the best-matched partition, changing the threshold of spectral distance during cross-assignment, and maximum likelihood estimation of root-mean-square error (RMSE) of PIWPs, were used to reduce the impact of potential wrong cross-assignments. Using these methods, the SWIM PIWPs were validated against NDBC buoys. The results show that SWIM performs well at finding the spectral peaks of different partitions with the RMSE of PPWPs and PPWDs of 0.9 s and 20{degree sign}, respectively, which can be a useful complement for other wave observations. However, the accuracy of PSWH from SWIM is not that good at this stage, probably because the high noise level in the spectra impacts the result of the partitioning algorithm. Further improvement is needed to obtain better PSWH information.
2022Published in IEEE Transactions on Geoscience and Remote Sensing volume 60 on pages 1-13. 10.1109/TGRS.2021.3110952