Recent Improvements in Wind Speed Retrieval for the CYGNSS Mission

The NASA spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission is a constellation of 8 microsatellites focused on tropical cyclone (TC) inner core process studies. CYGNSS will be launched in October 2016, and will use GPS-Reflectometry (GPS-R) to measure ocean surface wind speed in all precipitating conditions, including those experienced in the TC eyewall, and with sufficient frequency to resolve genesis and rapid intensification. The main advantage of CYGNSS and of GPS-R in general is to combine a dense space-time sampling capability with the ability of L-band signals to penetrate well through rain, using simple, low-cost/low-power modified GPS receivers. Here we present a mission overview and status, focusing in particular on some recent developments in the wind speed retrieval algorithm. We illustrate a modified and improved version of the current baseline Level 2 (L2) wind speed retrieval algorithm designed for CYGNSS. The baseline approach makes use of two different observables computed from delay-Doppler Maps (DDMs) of radar cross section, called Delay-Doppler Map Average (DDMA), and Leading Edge Slope (LES). The first is the averaged radar cross section over a delay-Doppler window around the DDM peak, while the second is the leading edge of the Integrated Delay Waveform (IDW), obtained by integrating the DDM along the Doppler dimension. The DDMA and LES observables are calculated over a limited range of time delays and Doppler frequencies to comply with baseline spatial resolution requirements for the retrieved winds, which in the case of CYGNSS is 25 km. In the current approach, the relationship between the observable value and the surface winds is described by an empirical Geophysical Model Function (GMF) that is characterized by a very high slope in the high wind regime, for both DDMA and LES observables, causing large errors in the retrieval at high winds. A simple mathematical modification of these observables is proposed, which stems from considerations related to the scattering mechanisms, and which linearizes the relationship between ocean surface roughness and the observables. This significantly reduces the non-linearity present in the GMF that relates the observables to the wind speed, and reduces the root-mean square error between true and retrieved winds, particularly in the high wind regime. An analysis of the algorithm performance and of the quality and error in the retrieved winds is presented, using GPS-R synthetic data simulated through the End-to-End Simulator (E2ES) developed for CYGNSS. The analysis is then applied to real data from the TechDemoSat-1 (TDS-1) GPS-R experiment, highlighting a reduction in the sensitivity to Significant Wave Height (SWH) which has been observed recently in GPS-R data from TDS-1.