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Ocean surface wind remote sensing using reflectometry measurements from S-band (2.3 GHz) digital communication satellite transmissions is demonstrated in tropical cyclones. Twelve days of airborne data were collected during the 2014 Atlantic hurricane season. An empirical model function, relating the sensed ocean surface mean square slope to wind speed, was first developed by fitting mean square slope estimates to comparison wind speed data from the Stepped Frequency Microwave Radiometer on the same aircraft and wind direction data from the Hurricane Weather Research and Forecasting model. This model function was then applied for wind speed retrievals using an independent set of reflectometry measurements and validated against four data sources: Stepped Frequency Microwave Radiometer, the Hurricane Weather Research and Forecasting model, dropsondes, and flight-level winds. Good agreement between these retrievals and comparison data was found, with a root-mean square error between 4.9 and 6.6 m/s and a bias between -1.4 and -0.2 m/s for wind speeds up to 50 m/s.

Plain Language Summary We demonstrate a method for measuring the ocean surface wind speed in hurricanes, using reflected signals from communication satellite transmissions. Compared to other radar remote sensing techniques, this "signals of opportunity" approach does not require a dedicated transmitter and, therefore, is much smaller and uses lower power. This makes it advantageous for use on small satellites or uncrewed aerial vehicles. We present results of aircraft experiments during the 2014 Atlantic hurricane season to demonstrate the feasibility of this method and quantify its accuracy. We compared the wind speed estimated from the reflected signals with four independent data sources to validate its accuracy. The results showed a good agreement, with an error between 4.9 and 6.6 m/s.