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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 107–112, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-107-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 107–112, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-107-2020

  04 Nov 2020

04 Nov 2020

ANALYSIS OF AN EXTRATROPICAL CYCLONE IN THE SOUTHWEST ATLANTIC: WRF MODEL BOUNDARY CONDITIONS SENSITIVITY

L. R. Diaz, R. A. Mollmann Junior, G. B. Muchow, P. S. Käfer, N. S. Rocha, E. A. Kaiser, S. T. L. Costa, G. P. Hallal, R. C. M. Alves, and S. B. A. Rolim L. R. Diaz et al.
  • State Research Center for Remote Sensing and Meteorology (CEPSRM), Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil

Keywords: WRF, Sensitivity analysis, Extratropical cyclones, NCEP FNL, NCEP CFSv2

Abstract. Meteorological conditions characterize the southern Brazilian coast a cyclogenetic area. The current study seeks to analyse the sensitivity of the WRF model to initial and boundary meteorological conditions in the simulation of an extratropical cyclone that occurred on the southern Brazilian coast on October 28, 2018. For this purpose, the WRF model was set up for two experimental simulations using the NCEP FNL and the NCEP CFSv2 reanalysis data as initial/boundary conditions. The sensitivity analysis was carried out with the cyclone trajectory assessment and comparison with wind speed data from meteorological stations. The results show that the initial meteorological conditions significantly influence the simulation of the cyclone track. In a nutshell, the use of NCEP CFSv2 resulted in more accurate wind speed simulations when compared to the values observed in the stations. With correlation coefficient values around 0.7, and the lowest bias (−2.57 m/s) and RMSE (3.68 m/s). In contrast, using the NCEP FNL data, the lowest correlation coefficient and the highest bias and RMSE values were obtained: 0.58, −3.97 m/s and 4.91 m/s, respectively. However, both simulations tend to underestimate observational wind speed values. The superior performance of simulations using CFSv2 tends to be related to the finer horizontal resolution of this reanalysis data source.