: Received: 12 March 2018 / Approved: 13 March 2018 / Online: 13 March 2018 (04:25:36 CET)
: Received: 21 April 2018 / Approved: 23 April 2018 / Online: 23 April 2018 (12:01:15 CEST)
How to cite:
Krien, Y.; Arnaud, G.; Cécé, R.; Khan, J.; Bel Madani, A.; Bernard, D.; Islam, A.; Durand, F.; Testut, L.; Palany, P.; Zahibo, N. Improving Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data. Preprints2018, 2018030095 (doi: 10.20944/preprints201803.0095.v1).
Krien, Y.; Arnaud, G.; Cécé, R.; Khan, J.; Bel Madani, A.; Bernard, D.; Islam, A.; Durand, F.; Testut, L.; Palany, P.; Zahibo, N. Improving Parametric Cyclonic Wind Fields Using Recent Satellite Remote Sensing Data. Preprints 2018, 2018030095 (doi: 10.20944/preprints201803.0095.v1).
Parametric cyclonic wind fields are widely used worldwide for insurance risk underwriting, coastal planning, or storm surge forecasts. They support high-stakes financial, development, and emergency decisions. Yet, there is still no consensus on the best parametric approach, or relevant guidance to choose among the great variety of published models. The aim of this paper is first and foremost to demonstrate that recent progresses on estimating extreme surface wind speeds from satellite remote sensing now makes it possible to select the best option with greater objectivity. In particular, we show that the Cyclone Global Navigation Satellite System (CYGNSS) mission of NASA is able to capture a substantial part of the tropical cyclones structure, and allows identifying systematic biases in a number of parametric models. Our results also suggest that none of the traditional empirical approaches can be considered as the best option in all cases. Rather, the choice of a parametric model depends on several criteria such as cyclone intensity and/or availability of wind radii information. The benefit of our approach is demonstrated by comparing traditional models with an improved vortex for hurricane Maria in the Caribbean. The wave heights computed by a wave-current hydrodynamic coupled model are found to be much better reproduced, with a significant reduction of the model biases. The results and approach presented in this study should shed new light on how to handle parametric cyclonic wind models. This will help the scientific community to conduct better wind, waves and surge analysis for tropical cyclones.
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