# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 2 Data and Methods  
Level-1 GNSS-R data from the CyGNSS v3.0 pre-release (Sandbox 210) dataset were used in this analysis. This experimental dataset was made available to the CyGNSS Science Team for testing and validation prior to public release. Minimal differences are expected between the pre-release and public release of version v3.0. The CyGNSS L1-to-L2 ocean inversion algorithms currently adopt a combination of two GNSS-R observables for the retrieval of ocean wind speed, both of which are assessed here. The first is the NBRCS, which is linked to the peak power of the reflected signals in the DDM; the second is the leading edge slope (LES), associated with the slope of the delay waveform of correlated forward-scattered power.  
CyGNSS L1 DDMs are calibrated to account for a number of system and instrument effects, including platform attitude, receiving antenna pattern, viewing geometry, satellite vehicle number (SVN, i.e., the identifier for individual GPS transmitters), and available direct GPS power. The signal calibration scheme adopted in CyGNSS v3.0 relies on the use of estimates of direct GPS power data collected from the zenith front-end in order to (a) compensate for differences in GPS transmitter antenna patterns and (b) compensate for changes in transmit power over time, which is crucially important when GPS flex modes are enabled [16]. Up until the previous release (CyGNSS v2.1), transmitter antenna calibration was based on information made available by the GPS satellite manufacturer; however, significant discrepancies are known to exist between pre-launch and actual GPS transmitter antenna patterns. In-flight estimates of direct power are only available starting from August 2018, when the automatic gain control mode of the zenith GPS receivers flow on the CyGNSS constellation was switched off [18]. Available CyGNSS L1 records from August 2018 until December 2019 are examined in this study. CyGNSS L1 data are posted at 1 Hz until approximately June 2018, when the posting rate doubles to 2 Hz (Figure 1).  
The scattering of GNSS signals over the ocean is known to be primarily controlled by the mean square slope of the ocean surface, which is driven (to first order) by the wind speed above the surface. By using reference ocean data to characterise ocean conditions, CyGNSS L1 observables will be assessed independently of ocean wind speed. Our analysis of the response of reflected GPS signals collected by the CyGNSS constellation is thus based on the use of verification data from ECMWF ERA-5 model output. ERA-5 is a global climate reanalysis product released by ECMWF and accessible freely from apps.ecmwf.int/data-catalogues/era5. For the assessment of CyGNSS L1 products, the ERA-5 HRES (high-resolution realisation) output is used. The HRES atmospheric data have a spatial resolution of 31 km and a temporal resolution of 1 h. ERA-5 outputs a multitude of ocean, atmosphere, and surface geophysical parameters. Previous validation work on ERA-5 wind speed products has shown stable temporal performance relative to observations, as well as favourable spatial performance [19; 20]. In order to isolate any possible residual effects associated with non-geophysical parameters, whilst maintaining a large volume of data, model data from ERA-5 were used to select CyGNSS records collected within a limited range of surface conditions centred around the global ocean average wind speed (~7 m/s); specifically, only data with associated ERA-5 wind speeds of between 6 and 8 m/s were selected for analysis. ERA-5 data were collocated with CyGNSS specular points using the nearest point in space and time, which corresponds to a maximum spatial offset to the specular point of half a grid cell (15.5 km), although it should be noted that the size of the glistening zone is approximately 25 km in average ocean conditions. The maximum temporal offset is 30 min.