Characterization of CYGNSS Ocean Surface Wind Speed Products
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.
Authors
- April Warnock (ORCID: https://orcid.org/0000-0001-8146-2765)
- Darren McKague (ORCID: https://orcid.org/0000-0003-0297-0388)
- Dorina Twigg
- Mohammad M. Al-Khaldi (ORCID: https://orcid.org/0000-0003-2478-5909)
- Anthony Russel (ORCID: https://orcid.org/0000-0003-1128-9527)
- Rajeswari Balasubramaniam (ORCID: https://orcid.org/0000-0002-9937-1201)
- Shakeel Asharaf (ORCID: https://orcid.org/0000-0003-3981-7360)
- Christopher S. Ruf (ORCID: https://orcid.org/0000-0002-5937-4483)
- Daniel Pascual (ORCID: https://orcid.org/0000-0001-8517-2415)
Institutions
- SRI International (US)
- Jet Propulsion Laboratory (US)
- University of California, Los Angeles (US)
- University of Michigan (US)
- Menlo School (US)
- The Ohio State University (US)
Publication Details
- Journal
- Remote Sensing
- Published
- 2024-11-20
- DOI
- https://doi.org/10.3390/rs16224341
- Citations
- 10
- Primary Topic
- Soil Moisture and Remote Sensing
- Type
- article
- Field-Weighted Citation Impact
- 2.06