# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 7 Summary and Conclusions  
The CYGNSS satellite constellation was initially designed with the objective of providing critical and frequent wind measurements in TCs, a task that, due to unforeseen calibration issues, has been proven to be extremely challenging [8; 9; 10]. Measurements in the low-to-moderate wind regimes have displayed better skills than anticipated and have begun to be used in a variety of applications such as assimilation experiments [54; 55; 56; 57], deriving products such as surface fluxes [58], and air-sea interaction studies [59; 60]. With the original objective in mind, here, we focused on CYGNSS tropical cyclone wind measurements as we believe them to be the most innovative part of the planned mission and the one in most need of improvements before they can be used for operational purposes. We assessed the capability of CYGNSS observations in TCs from all wind datasets currently available to the public using recently developed satellite TC-wind products. While the various CYGNSS datasets have been independently validated by individual groups, the work presented here focused on a unified methodology that allows an intercomparison at high winds. The assessment was performed using satellite microwave radiometer wind products specifically developed for TC conditions. In addition, TC scenes from the HWRF model were used as additional validation resource. From these CYGNSS wind datasets, we chose the best candidate and implemented it into a wind assimilation model. In this paper, we first provided an overview of the CYGNSS L2 wind products in low-to-moderate wind regimes; we performed detailed analyses by colocating CYGNSS observations in tropical cyclones with measurements from radiometers; and evaluated a new storm-centric CYGNSS product. We found that the CYGNSS NOAA V1.1 track-wise adjusted wind product [13] outperforms the other datasets and shows promising skill in all wind regimes, including TCs. At high winds (above 20 m/s), this dataset shows remarkable consistency with the SMAP radiometer and displays no significant overall bias. The quality control of the most recent NOAA winds (V1.1) has been relaxed compared to the beta version V1.0 and now provides very good data coverage in the core of the storms. Overall, the NOAA V1.1 shows promising skill in the determination of storm radii and intensity for gale (>17 m/s)- and storm-force (>25 m/s) winds, but, at this time, it underestimates hurricane-force (>33 m/s) storm intensity and size. This remaining issue is being addressed by the developers at NOAA, and a new version with improved winds above hurricane force is expected to be released in early 2022. The high-wind-speed YSLF products in the SDR V3.0 are very noisy and display severe biases in storms: about -7 m/s, with a standard deviation of 7 m/s. Smaller biases were found for the CDR V1.0 YSLF products in storms (-4.8 m/s), but with very poor data coverage in TCs and very large uncertainty (9.6 m/s). At this time, both of these products are not of a sufficient quality or reliability level required for any objective/quantitative use in tropical cyclone studies. Some of these biases are being addressed in new products that are currently under development. In particular, the SDR V3.1, under development at the SOC, will use SWH to characterize the sea state in the wind retrievals for FDS and YSLF [11], similar to the NOAA wind product, and will implement some additional bias corrections for some the GPS transmitters (CYGNSS Science Team meeting 27 July 2021, presentations by [PERSON]; and [PERSON]). We also assessed the storm-centric gridded CYGNSS dataset [26], currently available for most intense storms in the North Atlantic and Eastern Pacific over 2018-2020. Organizing the CYGNSS data in a storm-centric framework helps facilitate storm analyses of features often used for forecasting, including estimates of the storm size and intensity at 6-hourly intervals and storm evolution [25; 53]. While promising, the storm-centric V1.0 dataset has some limitations at this time.