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Remote Sensing
Volume 16
Issue 22
10.3390/rs16224341
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Article

Characterization of CYGNSS Ocean Surface Wind Speed Products

by
Christopher Ruf
1,*,
Mohammad Al-Khaldi
2,
Shakeel Asharaf
3,
Rajeswari Balasubramaniam
1,
Darren McKague
1,
Daniel Pascual
4,
Anthony Russel
1,
Dorina Twigg
1 and
April Warnock
5
1
Space Physics Research Laboratory, Department of Climate, Space Sciences and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
2
ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA
3
Jet Propulsion Laboratory, University of California, Los Angeles, CA 91330, USA
4
Deimos Space UK Ltd., Didcot OX11 0RL, UK
5
SRI International, Menlo Park, CA 94025, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(22), 4341; https://doi.org/10.3390/rs16224341
Submission received: 22 October 2024 / Revised: 17 November 2024 / Accepted: 19 November 2024 / Published: 20 November 2024
(This article belongs to the Section Ocean Remote Sensing)
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Abstract

Since its launch in 2016, a number of wind speed retrieval algorithms have been developed for the NASA CYGNSS satellite observations. We assess their accuracy and precision and characterize the dependence of their performance on environmental factors. The dependence of retrieval uncertainty on the wind speed itself is considered. The triple colocation method of validation is used to correct for the quality of the reference wind speed products with which CYGNSS is compared. The dependence of retrieval performance on sea state is also considered, with particular attention being paid to the long wave portion of the surface roughness spectrum that is less closely coupled to the instantaneous local wind speed than the capillary wave portion of the spectrum. The dependence is found to be significant, and the efficacy of the approaches taken to account for it is examined. The dependence of retrieval accuracy on wind speed persistence (the change in wind speed prior to a measurement) is also characterized and is found to be significant when winds have increased markedly in the ~2 h preceding an observation.
Keywords: ocean surface wind speed; GNSS-R; CYGNSS; bistatic radar ocean surface wind speed; GNSS-R; CYGNSS; bistatic radar

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MDPI and ACS Style

Ruf, C.; Al-Khaldi, M.; Asharaf, S.; Balasubramaniam, R.; McKague, D.; Pascual, D.; Russel, A.; Twigg, D.; Warnock, A. Characterization of CYGNSS Ocean Surface Wind Speed Products. Remote Sens. 2024, 16, 4341. https://doi.org/10.3390/rs16224341

AMA Style

Ruf C, Al-Khaldi M, Asharaf S, Balasubramaniam R, McKague D, Pascual D, Russel A, Twigg D, Warnock A. Characterization of CYGNSS Ocean Surface Wind Speed Products. Remote Sensing. 2024; 16(22):4341. https://doi.org/10.3390/rs16224341

Chicago/Turabian Style

Ruf, Christopher, Mohammad Al-Khaldi, Shakeel Asharaf, Rajeswari Balasubramaniam, Darren McKague, Daniel Pascual, Anthony Russel, Dorina Twigg, and April Warnock. 2024. "Characterization of CYGNSS Ocean Surface Wind Speed Products" Remote Sensing 16, no. 22: 4341. https://doi.org/10.3390/rs16224341

APA Style

Ruf, C., Al-Khaldi, M., Asharaf, S., Balasubramaniam, R., McKague, D., Pascual, D., Russel, A., Twigg, D., & Warnock, A. (2024). Characterization of CYGNSS Ocean Surface Wind Speed Products. Remote Sensing, 16(22), 4341. https://doi.org/10.3390/rs16224341

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