Can we improve parametric cyclonic wind fields using recent satellite remote sensing data?

Abstract

Parametric cyclonic wind fields are widely used worldwide for insurance risk underwriting, coastal planning, and storm surge forecasts. They support high-stakes financial, development and emergency decisions. Yet, there is still no consensus on a potentially "best" parametric approach, nor guidance to choose among the great variety of published models. The aim of this paper is to demonstrate that recent progress in estimating extreme surface wind speeds from satellite remote sensing now makes it possible to assess the performance of existing parametric models, and select a relevant one with greater objectivity. In particular, we show that the Cyclone Global Navigation Satellite System (CYGNSS) mission of NASA, along with the Advanced Scatterometer (ASCAT), are able to capture a substantial part of the tropical cyclone structure, and to aid in characterizing the strengths and weaknesses of a number of parametric models. Our results suggest that none of the traditional empirical approaches are the best option in all cases. Rather, the choice of a parametric model depends on several criteria, such as cyclone intensity and the availability of wind radii information. The benefit of using satellite remote sensing data to select a relevant parametric model for a specific case study is tested here by simulating hurricane Maria (2017). The significant wave heights computed by a wave-current hydrodynamic coupled model are found to be in good agreement with the predictions given by the remote sensing data. The results and approach presented in this study should shed new light on how to handle parametric cyclonic wind models, and help the scientific community conduct better wind, wave, and surge analyses for tropical cyclones.