# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 7 Summary and Conclusions
Although CYGNSS is able to see the core of the storm, there are significant gaps even in the storm-centric 6-hourly aggregated data. The dataset is built by aggregating the SDR V3.0 YSLF wind speeds (L2), which still suffer from significant biases. Our analyses showed that the CYGNSS storm-centric winds display inconsistent biases versus the radiometers and HWRF winds, sometimes being much lower, and other times much higher. These inconsistent results for different storms are a cause for concern and need to be addressed before such products can be used by the forecasting community. The storm-centric methodology is indeed very promising and can lead to a very useful dataset for analyses of tropical cyclones, provided that better quality-controlled L2 datasets are used for aggregation, e.g., a future and improved version of the CYGNSS CDR YSLF product or the NOAA CYGNSS. In the current V1.0 storm-centric dataset, spurious features often emerge in the aggregated storm-centric wind fields, possibly due to fast-moving or rapidly intensifying storms seen over a large temporal window. This undesired effect can be minimized by using a narrower time window, \(\pm 3\) h, provided that future versions of the L2 CYGNSS dataset have sufficient valid retrievals in that time-frame. Current versions do not provide adequate coverage in most TC cases due to strict quality control. Additional issues that need to be addressed in future datasets relate to the use of ancillary wind data both in the track-wise corrected products and for quality control. It is very important that the final CYGNSS product remains an independent estimate of the surface wind speed and is not forced to the ancillary winds. The influence of the ancillary winds on the final product will need to be explored in detail such that the CYGNSS wind datasets can be considered faithful to the original observations. Moreover, the latency of the high-wind-speed products will need to be reduced to few hours if data are to be used for operational storm analyses. The CYGNSS constellation is the first of this kind, and as such is to be considered to be a research mission rather than an operational mission. One of the main advantages of the CYGNSS constellation mission is its frequent revisit time over the tropics and its ability to observe the core of the TCs. CYGNSS' frequent revisit time allows for multiple passes per day over a single storm, compared to once/twice daily as is the case with sensors in polar orbits. This assessment, together with numerous ones on current and previous versions of the CYGNSS wind datasets, provide a roadmap for future improvements and possible similar future missions. Possibly one of the most important outcomes from this type of GNSS-reflectometry wind-observing mission is the potential to detect when a storm is experiencing a rapid intensification (RI), defined as a sudden increase in intensity of at least 30 kt (15 m/s) within 24 h [61]. Several TCs in the past few years experienced RI episodes. These include Atlantic Hurricanes Harvey, Irma, Maria, Florence, Dorian, and, most recently, Laura, Sally, Teddy, Gamma, Delta, Epsilon, Eta (all in 2020), and Ida (2021). The detection of RI is critical for forecasters and, so far, represents the biggest challenge for improving intensity forecasts. RI episodes are often detected by hurricane hunters' flights into the storms, mostly in the Atlantic, and only recently by satellite measurements, i.e., the TC-dedicated wind algorithms from radiometers and observations from the synthetic aperture radar (SAR [62]) onboard the RadarSat-2 and Sentinel satellites. However, due to the large gaps between radiometer swaths, and the limitations of twice-daily sampling, radiometers might miss the storm during the RI phases. The frequent CYGNSS observations in all ocean basins can be very valuable to follow the storm evolution in greater detail, especially if organized into 3-hourly storm-centric maps. Finally, we explored the assimilation of the CYGNSS winds into a satellite-based global wind analysis, CCMP.