Toward Quasi-Global Gap-Free Ocean Wind Speed Mapping Enabled by Rapid-Sampling GNSS Reflectometry Satellites
Abstract
Accurate and continuous global ocean wind data are crucial for understanding weather and climate patterns. However, gaps in observations often limit the availability of measured ocean wind maps. As an emerging remote sensing technique, spaceborne global navigation satellite system reflectometry (GNSS-R) platforms have been providing billions of ocean surface observations with a revisit period shorter than one day, leading to the unprecedented potential of improving temporal and spatial resolutions simultaneously. In this study, we propose an optimal interpolation (OI) scheme to generate quasi-global gap-free ocean wind maps using rapid-revisit GNSS-R observations. NOAA Cyclone GNSS (CYGNSS) version 1.2 (V1.2) wind products are resampled to a 6-h temporal resolution and a $0.25^{\circ } {\,}\times {\,}0.25^{\circ } $ spatial grid. A space–time OI based on isotropic Gaussian fields is then applied to assign weights to measurements and fill in each target grid. The averaged root-mean-squared errors (RMSEs) of OI reconstructed winds are 1.31, 1.32, and 1.76 m/s, compared to European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5, Advanced Microwave Scanning Radiometer-2 (AMSR2) all-weather, and Soil Moisture Active Passive (SMAP) final wind products, respectively. The reconstructed fields demonstrate stable spatiotemporal performances relative to these reference datasets. Case studies of Super Typhoon Surigae and Typhoon IN-FA further illustrate the method’s capability, showing improved detection of peak intensity on time compared to ECMWF ERA5 winds and consistent alignment of detected typhoon centers with best track data. Triple collocation is applied for computing the uncertainty of OI reconstructed winds (0.98 m/s on average), and the interpolation error of the proposed OI scheme is about 1.08 m/s according to the OI reconstructed wind fields with the input of track-masked ERA5 winds. This work provides a valuable approach for improving the continuity and reliability of GNSS-R ocean wind products, offering potential benefits for weather forecasting, climate studies, and extreme event monitoring.