# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 5 Discussion  
The CYGNSS retrieved winds are currently being used for various ocean science applications such as ocean circulation studies, regional and global analysis of ocean winds [19], tropical cyclone studies [20; 21; 22; 23], and assimilation into Numerical Weather Prediction (NWP) models. Data reliability plays an important role in aiding such scientific studies. The CYGNSS wind speed data products are of two kinds--the Fully Developed Seas (FDS) wind retrievals and Young Sea Limited Fetch (YSLF) retrievals. Of these two, the FDS winds form the major proportion of the measurements and are therefore used for many scientific applications, especially for assimilation into NWP models. The YSLF data product is for hurricane force winds measured over individual storms, therefore is a substantially smaller set of measurements. The QC mechanism developed in this work is for the CYGNSS FDS winds, in order to reduce errors (in particular, outliers) in the retrieval due to various engineering and measurement related errors. Figure 12: Mean difference and RMS difference statistic on CYGNSS retrieved winds before and after QC filter. Figure 13: Variance in CYGNSS retrieved winds before and after QC filter. The primary merit of the proposed ML filter is its ability to better account for interactions between the individual engineering, instrument and measurement conditions than can separate thresholded flags for each one. The current approach upon which we are improving uses individual flags and, despite these existing QC filters, there remains considerable scatter in the data--hinting that individual and independent thresholds is not an effective way of removing the outliers. The filter proposed here utilizes the capability of ML tools to learn inherent patterns from the training dataset and quickly come up with any convex boundaries separating the outliers from good data. One other advantage of such filters is that, because the system itself is aging with time, and as shown in this work--most of the outliers are due to calibration errors, the new ML-based QC thresholds can be reassessed periodically. In such situations, the ML filters come in handy as their parameters can be tuned easily to respond to any changes. Assimilating the CYGNSS near surface wind retrievals into NWP models for better forecasting is one of its important uses. In general, NWP models give a weight to meteorological satellite observations based on their error statistics. Thus, reducing errors in the retrieval will help assimilate CYGNSS winds better. Using this filter, the standard deviation of the retrieval is reduced from 2.6 m/s to 1.7 m/s over the wind speed range 0-25 m/s. At higher wind speed ranges, this filter is too aggressive and removes some valuable high wind measurements. This is due to the fact that high wind data density (> 20 m/s) is very sparse, hence insufficient for the Neural Network to be able to learn significant patterns from it. To address this situation, one possibility is to assimilate more of CYGNSS high wind data in future years, to better train the Neural Network in this region. However, it is also important to consider here that the CYGNSS FDS winds are reliable only up to 25 m/s as they have been developed using NOAA/GDAS ocean surface winds as their reference [24]. The direction of focus of future work will be to develop automated machine learning based QC that can effectively remove outliers at all wind speed ranges. Currently this filter is operable only between 5-18 m/s. The lower sample density at high and very low winds, prevent the QC filter from operating in these regions. One possible solution, as mentioned above, is to wait for more CYGNSS measurements in these wind speed regions before the QC is applied. Using ML based QC for YSLF winds can be complicated by the rapidly varying sea state inside hurricanes. In such cases, a physics based definition of an outlier might be needed.