# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 5 Wind Assessment in Tropical Cyclones
### L3 CYGNSS Storm-Centric Winds  
As described in Section 2.1, a gridded storm-centric wind product has been developed, directed at users interested in TC evolution and forecasting [26]. These 6-hourly storm \"snapshots\" are composed by aggregating CYGNSS V3.0 YSLF observations within a \(\pm\)6-h time window. These aggregations help to facilitate the determination of the storm radii critical for forecasting [25, 53] as they minimize the presence of gaps between tracks. Figure 13 qualitatively illustrates the storm-centric wind fields for Hurricane Florence for the 6-hourly samples with at least 1000 valid 0.1\({}^{\circ}\) grid points. Figure 14 displays a comparison of the storm-centric CYGNSS wind field in the best-quality frame for Hurricane Florence (11 September, 1800 UTC, Figure 13d) with the HWRF model and a close pass with AMSR2 (1730 UTC). CYGNSS storm-centric winds are displayed at the original resolution (0.1\({}^{\circ}\), panel a) and resampled at 0.25\({}^{\circ}\) (d) compared  
Figure 11: Scatterplot of NOAA V1.1 winds in tropical cyclones compared to 1 h colocations with SMAP (**a**), AMSR2 TC-winds (**b**), and WindSat TC-winds (**c**). The statistics were built from a database that includes 86 scenes of wind fields in selected intense TCs in all ocean basins from 2017 to 2020. Figure 12: Similar to Figure 11, but for the YSLF high-wind products in SDR V3.0 (**a**) and CDR V1.0 (**b**) compared to AMSR2 TC-winds colocated within 1 h for the TC scenes in our database. Notice that the SDR V3.0 dataset starts in 2018 (see Table 1), therefore it does not include the 2017 storms. However, the scatterplot is still sufficiently populated for the statistics to be valid. to the HWRF model winds resampled at 0.1\({}^{\circ}\) (b) and 0.25\({}^{\circ}\) (c), respectively; AMSR2 TC-winds are on a 0.25\({}^{\circ}\) grid (e). Resampling to a common lower resolution allows us a more meaningful comparison among these datasets. While the statistics in the scatterplots (f-g) seem to indicate that the 0.25\({}^{\circ}\) resampled CYGNSS retrievals have just a modest bias (\(-\)0.88 m/s) and standard deviation (4.4 m/s) compared to the resampled HWRF, observing the two-dimensional wind fields suggests a less than ideal comparison, with the CYGNSS wind field having a very irregular shape, and spurious regions of high winds in the NE quadrant. Moreover, this example shows that the CYGNSS storm-centric wind fields do not actually resolve features below the 0.25\({}^{\circ}\) resolution, as expected from the large temporal and spatial aggregation window, making the 0.1\({}^{\circ}\) gridding superfluous. In terms of surface wind field and statistics, the AMSR2 TC-winds provide a much more realistic description of the hurricane features when compared to HWRF, with high correlation (0.93) and a small bias and standard deviation (0.09 m/s and 2.97 m/s, respectively), albeit with a positive bias of few m/s for winds above 40 m/s. For a comparison with SMAP, in Figure 15, we focus on the same storm but a different time sample (12 September 1200 UTC), which better colocates with SMAP (11 UTC). Similar conclusions are drawn from this comparison, with SMAP matching the HWRF data even better for wind speeds up to 50 m/s. Both Figures 14 and 15 illustrate how using the current version (V1.0) of the storm-centric CYGNSS winds for evaluation of intensity and radii of the storms can lead to very erroneous results, on average. Ideally, to avoid spurious features, it is advisable to redevelop the storm-centric dataset using a bias-corrected L2 CYGNSS dataset, such as the CDR or the NOAA, rather than the SDR. Comparisons such as those presented in Figures 14 and 15 and in [22] give confidence in the radiometer TC-winds for intense storms and in rain. A point-by-point comparison of CYGNSS storm-centric data colocated with AMSR2 for Hurricane Michael on 10 October 2018 at 0600 UTC is displayed in Figure 16a-c, with results consistent with those seen for Hurricane Florence.