Comparison of CYGNSS and Jason-3 Wind Speed Measurements via Gaussian Processes. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Bekerman, William; Guinness, Joseph
• Is Part Of: Data science in science. Volume 2:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Remote sensing -- satellite -- measurement bias; Big data -- Periodicals; Big data -- Data processing -- Periodicals; Data mining -- Periodicals; 006.312
• DOI: 10.1080/26941899.2023.2194349
• Languages: English
• ISSNs: 2694-1899

Abstract

Abstract: Wind is a critical component of the Earth system and has unmistakable impacts on everyday life. The CYGNSS satellite mission improves observational coverage of ocean winds via a fleet of eight micro-satellites that use reflected GNSS signals to infer surface wind speed. We present analyses characterizing variability in wind speed measurements among the eight CYGNSS satellites and between antennas, using a Gaussian process model that leverages comparisons between CYGNSS and Jason-3 during a one-year period from September 2019 to September 2020. The CYGNSS sensors exhibit a range of biases, mostly between −1.0 m/s and +0.2 m/s with respect to Jason-3, indicating that some CYGNSS sensors are biased with respect to one another and with respect to Jason-3. The biases between the starboard and port antennas within a CYGNSS satellite are smaller. Our results are consistent with, yet sharper than, a more traditional paired comparison analysis. We also explore the possibility that the bias depends on wind speed, finding some evidence that CYGNSS satellites have positive biases with respect to Jason-3 at low wind speeds. However, we argue that there are subtle issues associated with estimating wind speed-dependent biases, so additional careful statistical modeling and analysis is warranted.