Performance of surface winds from atmospheric reanalyses in the Southwestern South Atlantic Ocean

Abstract

AbstractSea surface wind (10‐m high) is one of the most important variables for oceanic applications. The zonal and meridional surface wind components from seven global atmospheric reanalyses [National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research Reanalysis 1, NCEP/Department of Energy Reanalysis 2, NCEP/climate forecast system reanalysis (CFSR), European Centre for Medium‐Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis (JRA‐55), the Modern‐Era Retrospective Analysis for Research and Application V2, and the ERA5 Reanalysis] were compared with surface winds retrieved from QuikScat/SeaWinds and Ascat scatterometers, and with surface ground winds measured in eight coastal meteorological stations, at the Southwestern South Atlantic Ocean, between 35° and 55°S and 55° and 70°W, approximately. Bias, root mean squared error and the Pearson linear correlation coefficient for the zonal and meridional wind components were estimated in this study. The effects of the atmospheric stability, the surface currents and the discontinuity between land and sea roughness on the satellite data are discussed in this paper. It was concluded that Modern‐Era Retrospective Analysis for Research and Application, Version 2 reanalysis is the product that better fitted with satellite data, and ERA5 follows it in performance. CFSR, ERA‐Interim and JRA‐55 reanalyses also presented very good performances. Surface winds from NR1 and NR2 reanalysis showed the largest differences with satellite data. ERA5 reanalysis was the database that better compared with the coastal observations measured at the meteorological stations. Surface winds measured at meteorological stations are, in general, worse represented by the reanalyses than satellite data. This last could be due to the discontinuity between land and sea roughness, and because the topography is not completely represented by the atmospheric global models.