# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 3 Ocean Applications
### Wind Speed  
Near-surface wind speed is one of the geophysical variables that can be estimated from GNSS-R observations made over the ocean. With an increase in wind speed, the ocean surface will appear rougher, and the reflected signal will scatter more diffusely. As the reflected signals interact with the ocean surface, the roughness of the surface is measured, from which we can infer near-surface wind speed. Signatures within the DDM are then used to infer various geophysical quantities like ocean surface wind speed [58]. Several algorithms have been developed across the various GNSS-R missions for the purpose of estimating wind speed from the data. As the CYGNSS mission was proposed and implemented as the first dedicated GNSS-R ocean surface wind speed NASA mission, much of the recent research on this remote sensing theory has been performed specifically for CYGNSS. For example, ref. [60] describes development of wind speed geophysical model functions for the CYGNSS mission. Ref. [61] relates two features of the DDM to wind speed: the normalized bistatic radar cross section and the slope of the leading edge of the radar return pulse scattered from the ocean surface. The performance of the wind speed estimate depends on quality control and calibration of the data [62]. The calibration of the data is especially important in high wind speed regimes, where the sensitivity of the scattered signal patterns to changes in wind speed is lower [63; 64]. For the CYGNSS method, dynamic calibration has improved the performance of the wind speed algorithms [62]. However, ref. [65] developed a method for wind speed estimation irrespective of the calibration status. The authors of ref. [65] found they were able to provide quality estimates of wind speed using uncalibrated data by incorporating a physical forward model with a background numerical weather prediction dataset, a process which calibrates the raw data for the retrieval process. This variational retrieval method is computationally intensive, but offers another promising retrieval method. The characteristics of GNSS-R-derived wind speed data--strengths, weaknesses, coverage--are dissimilar to the data obtained from other methods. An in-depth description of the current state of the art of remotely sensed winds is outlined by [59]. Unlike other traditional systems, the spatial and temporal properties of data coverage and sampling depend on the observation geometry of the GNSS satellites and receiver pairs. The pseudo-random nature of CYGNSS data collections is explored by [66]. Additionally, the sensitivity of GNSS-R to wind speed differs from that of other techniques for a variety of factors. For example, while both scatterometers and GNSS-R receivers effectively measure surface roughness, albeit at different frequencies, the forward (GNSS-R) vs. backward (scatterometry) means that the relationship of the GNSS-R signal with respect to wind speed behaves in an opposite to that of monostatic scatterometry. Scatterometers are designed to be sensitive to capillary waves, which are dependent on local wind speed; however, GNSS-R is sensitive to a larger range of surface waves. The sensitivity of GNSS-R observations to wind speed changes decreases with wind speed [63]. There are many strengths and weaknesses for each remote sensing technique; ultimately, by using all available data, a greater understanding of the Earth system can be achieved. While GNSS-R data are unique, many studies have documented that CYGNSS data can be assimilated alongside traditional sensor data to improve weather forecasting [67; 68; 69; 70]. Data assimilation experiments are ongoing for the CYGNSS mission as the understanding of the data quality and properties change with every new version of wind speed data. Each data assimilation experiment uses slightly different methods and metrics for evaluation, but most experiments showed that CYGNSS data have a neutral or positive impact on the weather forecasts of tropical cyclones.