The Cyclone Global Navigation Satellite System (CYGNSS) is a new NASA earth science mission scheduled to be launched in 2016 that focuses on tropical cyclones (TCs) and tropical convection. The mission’s two primary objectives are the measurement of ocean surface wind speed with sufficient temporal resolution to resolve short-time-scale processes such as the rapid intensification phase of TC development and the ability of the surface measurements to penetrate through the extremely high precipitation rates typically encountered in the TC inner core. The mission’s goal is to support significant improvements in our ability to forecast TC track, intensity, and storm surge through better observations and, ultimately, better understanding of inner-core processes. CYGNSS meets its temporal sampling objective by deploying a constellation of eight satellites. Its ability to see through heavy precipitation is enabled by its operation as a bistatic radar using low-frequency GPS signals. The mission will deploy an eight-spacecraft constellation in a low-inclination (35°) circular orbit to maximize coverage and sampling in the tropics. Each CYGNSS spacecraft carries a four-channel radar receiver that measures GPS navigation signals scattered by the ocean surface. The mission will measure inner-core surface winds with high temporal resolution and spatial coverage, under all precipitating conditions, and over the full dynamic range of TC wind speeds.

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An analysis of spaceborne Global Positioning System reflectometry (GPS‐R) data from the TechDemoSat‐1 (TDS‐1) satellite is carried out to image the ocean sea surface height (SSH). An SSH estimation algorithm is applied to GPS‐R delay waveforms over two regions in the South Atlantic and the North Pacific. Estimates made from TDS‐1 overpasses during a 6 month period are aggregated to produce SSH maps of the two regions. The maps generally agree with the global DTU10 mean sea surface height. The GPS‐R instrument is designed to make bistatic measurements of radar cross section for ocean wind observations, and its altimetric performance is not optimized. The differences observed between measured and DTU10 SSH can be attributed to limitations with the GPS‐R instrument and the lack of precision orbit determination by the TDS‐1 platform. These results represent the first observations of SSH by a spaceborne GPS‐R instrument.

A Minimum Variance (MV) wind speed estimator for Global Navigation Satellite System-Reflectometry (GNSS-R) is presented. The MV estimator is a composite of wind estimates obtained from five different observables derived from GNSS-R Delay-Doppler Maps (DDMs). Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions that are derived from NDBC buoy wind matchups with collocated overpass measurements made by the GNSS-R sensor on the United Kingdom-Disaster Monitoring Constellation (UK-DMC) satellite. The MV estimator exploits the partial decorrelation that is present between residual errors in the five individual wind retrievals. In particular, the RMS error in the MV estimator, at 1.65 m/s, is lower than that of each of the individual retrievals. Although they are derived from the same DDM, the partial decorrelation between their retrieval errors demonstrates that there is some unique information contained in them. The MV estimator is applied here to UK-DMC data, but it can be easily adapted to retrieve wind speed for forthcoming GNSS-R missions, including the UK's TechDemoSat-1 (TDS-1) and NASA's Cyclone Global Navigation Satellite System (CYGNSS).Index Terms-Delay-Doppler map, global navigation satellite systems (GNSS)-reflectometry, minimum variance (MV) estimator, ocean surface wind speed.

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A study of the retrieval of sea surface roughness using Global Navigation Satellite System‐Reflectometry (GNSS‐R) from satellite is presented. Delay‐Doppler Maps (DDMs) from the SSTL UK‐DMC satellite are analyzed to retrieve directional Mean Square Slopes (MSSs). Results are compared to theoretically‐derived MSSs and in situ measurements from co‐located buoys of the National Data Buoy Center (NDBC), showing good agreement in most cases. Here, the whole DDM, a more complete source of information, is exploited for the first time using satellite GNSS‐R data. These are potentially able to provide high spatial and temporal sampling, and therefore offer an improved way to observe wind and waves by means of a very modest instrument.

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