# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 2 Data Description  
The Level 2 CYGNSS winds are minimum variance estimated winds from two observables namely, NBRCS and LES for low-moderate wind speed ranges (0-25 m/s). The CYGNSS retrieved winds are matched to near coincident independent estimates of the ocean surface wind speed referenced to a 10 m height (\(u_{10}\)) from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) [12]. MERRA-2 is a reanalysis product provided by NASA's Global Modelling and Assimilation Office (GMAO). The reference matchup MERRA-2 gridded data product has a spatial resolution of 0.5 deg \(\times\) 0.625 (lat, lon) and an hourly instantaneous assimilation [13].  
Figure 1 shows the density scatter plot of CYGNSS retrieved winds with respect to the MERRA-2 winds. In the figure, the dashed line represents the 1:1 line and the solid line represents the mean retrieved wind speed line, which essentially is the GMF. This plot is generated by dividing the 2-D space into 500 bins or regions. And the matchup winds are assimilated into the nearest bin and finally the log to the base 10 of the number density is taken for better visualization of the density differences. There are several important observations from this plot. Firstly, most of the observations fall along the 1:1 line at lower wind speeds, indicating good retrieval quality. However, a cluster of very high CYGNSS retrieved winds (15-35 m/s) is noticeable at low MERRA-2 winds (5-10 m/s). The improved filtering method developed here targets the removal of these outliers. Secondly, the GMF line and the 1:1 line are very similar up to a MERRA-2 and CYGNSS wind speed of \(\sim\)10 m/s. Above this range, the GMF line begins to deviate away from the 1:1 line. This inherent bias in the GMF complicates the identification of outliers by the filter algorithm. The purpose of a quality filter is to remove outliers only and not correct for biases in the retrieval. This is another consideration to be accounted for while designing the filter. Finally, the density of samples at high MERRA-2 wind speeds (\(>\)20 m/s) is very small relative to the lower wind speed ranges. Therefore trade-off studies must be performed for filter design to balance between efficiency of outlier removal and retaining as many high wind samples as possible. All the above objectives will be addressed in the course of developing the filter.  
Figure 1: Log density plot of CYGNSS Level 2 retrieved winds matched to MERRA-2 reference winds for the wind speed range 0–25 m/s. The dashed line represents 1:1 agreement between the two winds. The solid line is the average CYGNSS retrieved wind at each MERRA-2 wind speed. A clustering of outliers can be seen near MERRA-2 wind speeds of 0-10 m/s and CYGNSS wind speeds \(>\)15 m/s (shown with a red box). One primary objective of the new filter is removal of this cluster.  
Over and above the existing quality flags, 13 diagnostic variables are used to distinguish outlier samples from good samples. These diagnostic variables are listed in Table 1. The choice of diagnostic variables is based on previous calibration experience with GNSS-R data and error analyses [7; 8; 9]. The diagnostic variables can be categorized into 3 major types--instrument related attributes, measurement geometry related attributes and surface related attributes. In Section 4 these diagnostic variables will be assessed for their individual significance in enabling the filter to distinguish between outliers and good samples.