# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## 6 Conclusions  
Taking advantage of an extremely active 2017 hurricane season in the tropical Atlantic Ocean and the Eastern Pacific, we investigated the feasibility of using the recent satellite remote sensing CYGNSS and ASCAT data to identify the strengths and weaknesses of several parametric wind models used for storm surge hazard assessment and the prediction of cyclonic waves. Under the assumption that ASCAT and CYGNSS data can be considered as good proxies for surface wind speeds in the outer and inner regions, respectively (with an exception in the core of cyclones), we were able to confirm the findings of a number of previous studies (e.g., [PERSON] et al. (2018), [PERSON] and [PERSON] (2017), or [PERSON] et al. (2019)) regarding biases present in several parametric models. Using a wave-current coupled numerical model, we also showed that this assumption is reasonable enough to be used to identify suitable parametric models for a specific case study. We emphatically note that our aim here is not to encourage using or discarding a specific parametric model, nor to propose a new one. If we wanted to do that, we should have tested all published models. Such a comprehensive assessment is well beyond the scope of the current work. Besides, the models considered here have been tested with a unique combination of parameters (e.g., SWRF) and approaches to synthesize the wind field. It would be premature to draw definitive conclusions. In addition, it should be kept in mind that there are also errors in remote sensing data, so differences of 2 m/s-3 m/s in bias are probably not always significant. The main finding of this paper is the following: Satellite remote sensing is now mature enough to provide relevant information about the structure of wind fields in cyclones, and can be used to assess the performance of parametric cyclonic wind models. Further work is yet needed to retrieve the detailed structure of TCs close to the eyewall. We focused here mainly on CYGNSS and ASCAT, but there is little doubt that other types of data may also be valuable. Remote sensing has now become \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fort de France** & **St Lucie** & **42060** \\ \hline \multirow{3}{*}{H80 (R64)} & Bias & \(-0.74\) m & \(-0.40\) m & \(-1.18\) m \\  & RMS & \(0.81\) m & \(0.52\) m & \(1.29\) m \\  & NRMS & \(37\%\) & \(23\%\) & \(30\%\) \\ \hline \multirow{3}{*}{H80 (All)} & Bias & \(1.11\) m & \(0.83\) m & \(0.44\) m \\  & RMS & \(1.24\) m & \(1.03\) m & \(0.64\) m \\  & NRMS & \(57\%\) & \(45\%\) & \(15\%\) \\ \hline \multirow{3}{*}{E11 (R64)} & Bias & \(-0.71\) m & \(-0.38\) m & \(-1.14\) m \\  & RMS & \(0.77\) m & \(0.50\) m & \(1.23\) m \\  & NRMS & \(36\%\) & \(22\%\) & \(28\%\) \\ \hline \multirow{3}{*}{E11 (All)} & Bias & \(0.62\) m & \(0.38\) m & \(0.13\) m \\  & RMS & \(0.72\) m & \(0.65\) m & \(0.56\) m \\  & NRMS & \(33\%\) & \(29\%\) & \(13\%\) \\ \hline \multirow{3}{*}{E11H80} & Bias & \(-0.04\) m & \(0.02\) m & \(-0.64\) m \\  & RMS & \(0.41\) m & \(0.46\) m & \(0.75\) m \\ \cline{1-1}  & NRMS & \(19\%\) & \(20\%\) & \(17\%\) \\ \hline \multirow{3}{*}{M16b} & Bias & \(0.06\) m & \(-0.06\) m & \(-0.28\) m \\ \cline{1-1}  & RMS & \(0.25\) m & \(0.40\) m & \(0.40\) m \\ \cline{1-1}  & NRMS & \(11.5\%\) & \(17.4\%\) & \(9.1\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bias, root mean square error (RMS), and RMS normalized by the mean observed values (NRMS) obtained when comparing numerical simulations with in-situ significant wave heights. a powerful tool that should be used to validate or improve existing parametric approaches, in order to conduct better wind, wave, and surge analyses for TCs. It is noteworthy to conclude by mentioning that even with the models tested here for Maria (see Section 5), the NRMS remains significant (about 9%-20%).