# Can We Improve Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data?
###### Abstract
Additionally, there is little guidance to help choose a relevant gradient wind model, i.e., a parametric model that is expected to accurately represent the increase and decay of wind speed as a function of distance from the TC center. One example of this is the Holland [33] vortex. Although known to have significant limitations [34], this model is still widely used by the research and insurance communities worldwide. Other commonly used parametric wind models (for which there is room for improvement) include, for example, those by [PERSON] and [PERSON] [35] and [PERSON] and [PERSON] [36]. New models are frequently proposed [18; 37], but no consensus has been reached in favor of any one of them. The difficulty in choosing among the great variety of gradient wind profiles can be attributed to a number of factors, including the fact that:  
* Validations of parametric wind models are generally performed using a limited number of \"ground-truth\" data. In-situ observations of surface wind speeds are relatively sparse for TCs, as they spend most of their lifetime over the oceans, where the density of buoys able to record extreme winds is relatively small. Besides, the wind recorded by meteorological stations can be biased because of terrestrial effects, which makes it difficult to compare observations with parametric values in a consistent way. Although these issues are offset to some extent in the North Atlantic and East Pacific thanks to aircraft reconnaissance, it remains a major problem in all oceanic basins;
* new proposed parametric models are generally compared with a limited number of existing gradient wind profiles. Except the work of [PERSON] and [PERSON] [17], we are not aware of any study investigating parametric wind models over a wide range of parameters and methods. New proposed models are often compared to the [PERSON] [33] or [PERSON] and [PERSON] [35] approaches to assess their quality, and disregard more recent models, such as [PERSON] et al. [38] or [PERSON] and [PERSON] [36]. It is thus difficult to assert that these new models really perform better than all existing approaches;
* new proposed parametric models are generally tested without considering all the available information on TC wind structure. As noted above, very few studies take into account all the available information about wind structure, such as the 34-kt, 50-kt, and 64-kt wind radii for each quadrant. Most of the time, only the radius of maximum winds or the hurricane-force (i.e., 64-kt)wind radii are used, which can potentially result in errors far from the cyclone center. It is thus unclear whether the discrepancies between the model and the observed data are due to the model itself, or to the fact that the model is not sufficiently constrained by available information (such as the 34-kt, 50-kt, and 64-kt wind radii); and
* some of the parameters used in parametric wind models are not always clearly specified. For example, the surface wind reduction factor (SWRF ([PERSON], 1976)), i.e., the empirical ratio between the surface and the top of the boundary layer, is rarely indicated, although it generally plays a significant role in the estimated parametric wind speeds ([PERSON], 1976). The main objective of this paper is to demonstrate that the recent availability of data from CYGNSS ([PERSON], 1976), in addition to other products, such as ASCAT ([PERSON], 1976), now makes it possible to select a relevant parametric approach to represent cyclonic wind speeds. After a short description of data and wind models used in the present study (Section 2), we compare CYGNSS and ASCAT data with parametric winds for 16 hurricanes of the 2017 Atlantic and Eastern Pacific season (Section 3). The aim is to determine in which cases CYGNSS and/or ASCAT products are good proxies for surface wind speeds. Our preliminary findings are then tested to investigate the consistency of the results.