# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
## 5 Comparison with In-Situ Oceanic Data  
Hurricane Maria was the deadliest storm of the 2017 Atlantic season. Recorded as a category 5 event, it caused catastrophic damage in Dominica and Puerto Rico, as well as widespread flooding and crop destructions in Guadeloupe. We test here the ability of the results presented in Figures 3 and 4 to identify parametric models able to satisfactorily represent the temporal evolution of the wind pattern that took place during Maria. To do so, we compared the significant wave heights observed at buoys in the Lesser Antilles with those computed using a wave-current coupled model forced by a sub-set of the various parametric winds considered in the previous sections. The model is based on the code, SCHISM-WWM [50]. The computational domain is represented in Figure 5. Resolution spans from 10 km far from the region of interest (where the bathymetry is derived from GEBCO), up to about 100 m in Guadeloupe and Martinique, where we have the best bathymetric data (ship-based soundings from the SHOM, the French Naval Hydrographic and Oceanographic Department). The model is forced by:  
* Astronomic tidal potential over the whole domain (12 constituents);
* 26 tidal harmonic constituents at the open boundaries, provided by the global FES2012 model [51];
* parametric pressure fields [33]; and
* parametric winds blended with CFSR (Climate Forecast System Reanalysis [52]) wind data. The parametric winds are prescribed for smaller radii than R34, whereas CFSR data are imposed for \(r>1.5\) R34. In between, a smooth transition is ensured using a weighing coefficient, which varies linearly with radius \(r\). We considered here six parametric models:
* E11 and H80, constrained using the 64-kt wind radii only (E11(R64) and H80(R64) in Figure 6);
* E11 and H80, constrained using all the wind radii information (E11(All) and H80(All) in Figure 6);* E11H80, for which we choose to blend the wind speeds inferred from E11 for the inner core region with those given by H80 for the outer region (see the black contours in Figure 3); and
* M16b, a blend between wind speeds obtained from M16 constrained by R64 only and with all wind radii information (see black contours in Figure 4). E11H80 and M16b were chosen to test whether the results presented in Figures 3 and 4 could be of benefit to build a relevant parametric model for the cyclone considered, using a combination of models that is expected to reduce the biases. We strongly emphasize the fact that the new models tested here are provided only as examples. They should in no way be considered as the \"best\" possible option. Better results can certainly be obtained with other wind gradient profiles. As an example, J92 also generally displays low biases, and could be a good candidate in a number of cases. The reader is referred to [PERSON] et al. (2015) and [PERSON] et al. (2016) for greater details about the model and the numerical strategy. Here, we compare the significant wave heights (Hs) computed by the model with Hs recorded by three buoys located in the Lesser Antilles (Figure 5): Fort de France (FdF) and Sainte Lucie, owned by Meteo France, as well as 42060, maintained by the National Data Buoy Center (NDBC). The latter went adrift during the peak of Maria, hence the decrease in Hs was unfortunately not captured. Results (Table3, Figure 6) show that:  
* H80 and E11 constrained only by the 64-kt wind radius (R64) give the worst results, with Hs generally significantly underestimated, and NRMS ranging between 20% and 50% (Table 3). This is consistent with the results of Figures 3 and 4, which show large negative biases for H80 and E11 in the case of category 4-5 cyclones and wider radii than R64;
* constraining all the 34-kt, 50-kt, and 64-kt wind radii (All) results in better performance for E11, with reduced bias and NRMS (15% to 22% approximately), although significant wave heights are now overestimated.