FN Archimer Export Format PT J TI Surface Wave Developments under Tropical Cyclone Goni (2020): Multi-Satellite Observations and Parametric Model Comparisons BT AF Yurovskaya, Maria Kudryavtsev, Vladimir Mironov, Alexey Mouche, Alexis Collard, Fabrice Chapron, Bertrand AS 1:1,2;2:1,2;3:3;4:4;5:5;6:4; FF 1:;2:;3:;4:PDG-ODE-LOPS-SIAM;5:;6:PDG-ODE-LOPS-SIAM; C1 Marine Hydrophysical Institute, 299011 Sevastopol, Russia Satellite Oceanography Laboratory, Russian State Hydrometeorological University, 195196 St. Petersburg, Russia eOdyn, 29280 Plouzané, France Institut Français de Recherche pour l’Exploitation de la Mer, 29280 Plouzané, France OceanDataLab, 29280 Locmaria-Plouzané, France C2 MARINE HYDROPHYS INST, RUSSIA UNIV RUSSIAN STATE HYDROMETEOROL, RUSSIA EODYN, FRANCE IFREMER, FRANCE OCEANDATALAB, FRANCE SI BREST SE PDG-ODE-LOPS-SIAM UM LOPS IN WOS Ifremer UMR DOAJ copubli-france copubli-int-hors-europe IF 5 TC 11 UR https://archimer.ifremer.fr/doc/00766/87847/93425.pdf LA English DT Article DE ;tropical cyclones;CFOSAT;ocean wave observation and modeling;wave directional properties;altimeter AB Simple Summary Multi-satellite observations to jointly analyze extreme surface wind and wave properties can now be readily obtained to study tropical cyclone (TC) events. Developing over the Philippine Sea, TC Goni was one of the most powerful TCs in 2020. Constrained by Sentinel-1/RadarSat-2 SAR and CFOSAT SCAT satellite data, wind field in the intense TC region is reconstructed. Significant wave height measurements are further obtained from altimeters on-board the CFOSAT, Jason-3, and Sentinel-3A, B satellites. The directional wave spectrum information can be derived from the CFOSAT SWIM off-nadir radar measurements. Using a simplified 2D parametric model and derived self-similar analytical solutions, the measured surface wave amplification in the right-hand TC sector, relative to its propagation direction, is well captured and interpreted. For TC Goni, observed and predicted waves reach 8 m and wavelengths larger than 200 m, leaving the TC inner region in the forward and forward-left direction. In the far TC zone, swell attenuates and superposes with wind waves, not necessarily aligned, to give observed significant wave height values. Multi-satellite data together with simplified parametric model outputs open new perspectives to more precisely study and predict surface waves generated by moving and rapidly evolving TCs for different scientific and practical purposes. Abstract Over the Philippine Sea, the tropical cyclone (TC) Goni reaches category 5 on 29–31 October 2020. Multi-satellite observations, including CFOSAT SWIM/SCAT and Sentinel-1 SAR data, are jointly analyzed to assess the performances of a parametric model. Recently developed to provide a fast estimation of surface wave developments under rapidly evolving TCs, this full 2D parametric model (KYCM) and its simplified self-similar solutions (TC-wave geophysical model function (TCW GMF)) are thoroughly compared with satellite observations. TCW GMF provides immediate first-guess estimates, at any location in space and time, for the significant wave height, wavelength, and wave direction parameters. Moving cyclones trigger strong asymmetrical wave fields, associated to a resonance between wave group velocity and TC heading velocity. For TC Goni, this effect is well evidenced and captured, leading to extreme waves reaching up to 8 m, further outrunning as swell systems with wavelengths about 200–250 m in the TC heading direction, slightly shifted leftwards. Considering wind field constrained with very highly resolved Sentinel-1 SAR measurements and medium resolution CFOSAT SCAT data, quantitative agreements between satellite measurements and KYCM/TCW GMF results are obtained. Far from the TC inner core (∼10 radii of maximum wind speed), the superposition of outrunning swell systems and local wind waves estimates leads to Hs values very close to altimeter measurements. This case study demonstrates the promising capabilities to combine multi-satellite observations, with analytical self-similar solutions to advance improved understandings of surface wave generation under extreme wind conditions PY 2022 PD MAY SO Remote Sensing SN 2072-4292 PU MDPI AG VL 14 IS 9 UT 000794524200001 DI 10.3390/rs14092032 ID 87847 ER EF