# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## 3 Comparison of CYGNSS and ASCAT Data  
To get a preliminary idea of the usefulness of CYGNSS and ASCAT data as a proxy for surface wind speed, we compute the biases between these data and the mean (i.e., averaged over all empirical models) parametric winds for different cyclone categories and distances to the center (Figure 2). Computations are performed only when more than 30 data points are available for a given intensity/distance class. In practice, the comparison is possible in almost all cases, as hundreds to thousands of space-borne observations are available for each class. Parametric models are constrained by all the information provided by the NHC in the advisories to ensure that they give the best approximation possible of real winds. In classes for which the biases are large, remote sensing data are not consistent with the mean parametric winds. We choose not to investigate these cases further in the following sections, even if there is no evidence that the discrepancy is due to remote sensing errors rather than parametric models. On the contrary, small biases (in absolute terms) suggest that remote sensing data and parametric winds are consistent, so that they both give satisfactory results a priori. In the following, we will make the assumption that these CYGNSS/ASCAT data are indeed good proxies, and will also check whether or not this leads to consistent results. Figure 1: Flowchart summarizing the key steps of this study. Regarding ASCAT data, Figure 2 shows that the bias is low (less than about 2 m/s-3 m/s in absolute value) for wider radii than R34, but becomes increasingly negative with larger wind speeds, up to almost \(-\)20 m/s for category 5 cyclones and smaller radii than R64. These results suggest that ASCAT data are a good proxy for wind speeds lower than 34-kt, but that they underestimate extreme winds. This conclusion is consistent with previous published papers [48] based on methodologies and datasets different from ours. Figure 2: Bias between the remote sensing data and the parametric winds averaged over all empirical models. Negative (resp. positive) values indicate that remote sensing data display lower (resp. higher) wind speeds compared to the mean parametric winds. Different categories of distance to the cyclone center (r) and cyclone intensities are considered. TS stands for tropical storms, H1, H2, H3, H4, and H5 to the Saffir-Simpson hurricane category (Cat 1, 2, 3, 4, and 5, respectively). R34, R50, and R64 are the radii for the 34-kt, 50-kt, and 64-kt winds. _vs_, _nbrc_, and _les_ are three different CYGNSS products (see Section 2). The stars indicate which product has been selected as proxy for wind speeds in Sections 4 and 5. The red box/cross shows the intensity/category class for which CYGNSS/ASCAT data are not expected to give adequate information about wind speeds. The \"wind speed\" (_ws_) product is found to give systematically more negative biases than ASCAT, and thus probably often underestimates the velocities (Figure 2). However, the absolute value of bias remains relatively low for wider radii than R34, which suggests that this product might still be a good proxy for moderate and (potentially even more) low wind speeds. As this product was obtained for fully developed seas and not for cyclones, these results were also expected. The LES and NBRCS products generally display lower biases for smaller radii than 50-kt, and are thus expected to outperform the other wind speed estimations close to the center of cyclones. Again, this finding is not surprising since these products have been derived using young seas/limited-fetch GMFs that are expected to be more suitable in strong cyclonic wind regions. It is not clear here whether one of these two products yields better results compared to the other, since they show similar biases of a few meters per second. It must be noted, however, that none of them is expected to perform well for smaller radii than R64 and category 1 storms, since the bias reaches very large values (10 m/s to 15 m/s).