# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 3 Proposed Quality Control Method
### Population Definitions  
The CYGNSS Level 2 v3.0 data with MERRA-2 wind speed matchups from the year 2018 have a total of ~153 million samples. The sample space is divided into 2 regions. The low wind region consists of all samples with CYGNSS retrieved winds, \(U_{\text{CYG}}\), less than or equal to 10 m/s. The high wind region consists of samples with \(U_{\text{CYG}}\) greater than 10 m/s. In both regions MERRA-2 wind speed, \(U_{M}\) is required to be less than 25 m/s. This division of sample space is due to the behavior of the GMF \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Diagnostic Variable** & **Description** & **Type** & **Symbol/Abbreviation** \\ \hline prn\_code & GPS PRN code & Instrument attribute & PRN \\ antenna & CYGNSS science antenna & Instrument attribute & ant \\ nst\_att\_status & Star tracker attitude & Instrument attribute & nst \\ sc\_roll & Spacecraft roll & Instrument attribute & Roll \\ zenith\_ant\_gain & Zenith antenna Gain & Instrument attribute & \(Z_{gain}\) \\ zenith\_power & Zenith antenna power & Instrument attribute & \(Z_{power}\) \\ incidence\_angle & Incidence angle at SP & Geometry attribute & \(\theta\) \\ azimuth & Azimuth angle of SP & Geometry attribute & \(\varphi\) \\ range\_corr\_gain & Range Corrected Gain & Geometry attribute & RCG \\ sp\_rx\_gain & CYGNSS antenna Gain & Surface attribute & G \\  & at SP & Surface attribute & SNR \\ ddm\_snr & SNR at SP & Surface attribute & DDMA \\ ddm\_nbrcs & NBRCS at SP & Surface attribute & LES \\ \hline \hline \end{tabular} SP = Specular Point.  
\end{table} 
Table 1: List of diagnostic variables used. line relative to the 1:1 line. Below 10 m/s, the GMF line is very similar to the 1:1 line (see Figure 1) and above this wind speed the GMF line begins to underestimate. Therefore, it is appropriate to have two different training datasets, one for each region. In the low wind region, a good sample satisfies \(\left|U_{CYG}-\left\langle U_{CYG}\right\rangle\right|_{U_{M}}\)\(\leq\) 1 m/s. and an outlier satisfies \(\left|U_{CYG}-\left\langle U_{CYG}\right\rangle\right|_{U_{M}}\)\(>\) 4 m/s, where \(\left\langle U_{CYG}\right\rangle\)\(\left|U_{M}\right\rangle\) refers to the mean value of the wind speed retrieved by CYGNSS for a given value of the MERRA-2 wind speed. This relationship is described by the solid line in Figure 1. In the high wind region, a good sample is defined by \(\left(U_{CYG}-U_{M}\right)\)\(\leq\) 2 m/s and an outlier as a sample \(\left(U_{CYG}-U_{M}\right)\)\(>\) 3 m/s. The difference in training population definitions at low and high wind speeds is due to the inherent bias in the GMF which can be observed as the deviation of the retrieval mean (solid line) from the 1:1 agreement (dashed line) above 10 m/s in Figure 1. As the wind retrieval is based on the GMF, a bias in the GMF can lead to under/over-estimation of winds despite being a good measurement. To mitigate the effect of GMF-induced bias on the outlier detection capability of the filter, the filter is trained with respect to the GMF. However, as the filter is reliant on the Level 1 diagnostic variables which are independent of GMF, the samples lying near the 1:1 line are also good samples and therefore the modified definition of training data is used at high winds. The training datasets for the 2 different sample spaces are shown in Figure 2. Such conservative training definitions are used to improve the outlier detection capability of the algorithm. Further analysis of the definition of a good or an outlier sample is discussed later in this section. For training, we use \(\sim\)4 million samples for each wind speed region and \(\sim\)8 million samples for validation. The performance metrics used to evaluate the outlier detection capability of an algorithm are the Probability of Detection (PD) and False Alarm Rate (FAR). For these metrics, the definition of good and outlier samples are different from the training definitions. The validation definitions are based on the NASA mission requirements on wind retrieval error.