FN Archimer Export Format
PT J
TI C-Band SAR Winds for Tropical Cyclone Monitoring and Forecast in the South-West Indian Ocean
BT 
AF Duong, Quoc-Phi
   Langlade, Sébastien
   Payan, Christophe
   Husson, Romain
   Mouche, Alexis
   Malardel, Sylvie
AS 1:1;2:2;3:3;4:4;5:5;6:1;
FF 1:;2:;3:;4:;5:PDG-ODE-LOPS-SIAM;6:;
C1 Laboratoire de l’Atmosphère et des Cyclones (UMR8105 LACy), Université de La Réunion, CNRS, Météo-France, 97400 Saint-Denis, France
   Regional Specialized Meteorological Center for Tropical Cyclones La Réunion, Météo-France, 97400 Saint-Denis, France
   Centre National de Recherche Météorologique (UMR3589 CNRM), Université de Toulouse, CNRS, Météo-France, 31057 Toulouse, France
   Collecte Localisation Satellites (CLS), 29280 Brest, France
   Laboratoire d’Océanographie Physique et Spatiale, Ifremer, Université de Brest, CNRS, IRD, IUEM, 29280 Brest, France
C2 UNIV LA REUNION, FRANCE
   METEO FRANCE, FRANCE
   CNRM (METEO FRANCE), FRANCE
   CLS, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer UMR
   DOAJ
   copubli-france
   copubli-univ-france
IF 3.11
TC 6
UR https://archimer.ifremer.fr/doc/00692/80373/83490.pdf
LA English
DT Article
DE ;SAR;3D-Var;data assimilation;tropical cyclone;sentinel;IDAI;GELENA
AB Tropical cyclone (TC) monitoring and forecast in the South West Indian Ocean (SWIO) basin remain challenging, notably because of the lack of direct observations. During the 2018–2019 cyclone season, S-1 Sentinel SAR images were acquired, as part of the ReNovRisk-Cyclone research program, giving access to unprecedented detailed TC wind structure description without wind speed limitation. This paper assesses the quality of these data and the impact of their assimilation for TC forecasts. SAR observations are compared with analyses from a convection-permitting, limited area model AROME OI 3D-Var and with wind products used for operational TC monitoring. Their bias depends on the angle of incidence of the radar and the observation error is larger for extreme wind speed. The impact of SAR assimilation in AROME OI 3D-Var is assessed through two case studies. In the TC GELENA case, it leads to a better TC positioning and an improved representation of inner and outer vortex structures. The TC intensity reduction in the analysis propagates through subsequent analyses and it has an impact on forecasts for around 12 h. In the TC IDAI case, the 3D-Var does not manage to reproduce TC intensity captured by SAR. In both cases, the modification of the initial conditions has little influence on the intensification rate of the model forecasts. Sensitivity tests show that these results are robust to different observation errors and thinning. 
PY 2021
PD MAY
SO Atmosphere
SN 2073-4433
PU MDPI AG
VL 12
IS 5
UT 000653443700001
DI 10.3390/atmos12050576
ID 80373
ER

EF