Evaluation of high-resolution surface wind products at global and regional scales

Abstract

High resolution surface wind fields covering the global ocean, estimated from remotely sensed wind data and ECMWF wind analyses, have been available since 2005 with a spatial resolution of 0.25° in longitude and latitude, and a temporal resolution of 6h. Their quality is investigated through various comparisons with surface wind vectors from 190 buoys moored in various oceanic basins, from research vessels and from QuikSCAT scatterometer data taken during 2005-2006. The NCEP/NCAR and NCDC blended wind products are also considered. The comparisons performed during January-December 2005 show that speeds and directions compare well to in-situ observations, including from moored buoys and ships, as well as to the remotely sensed data. The root-mean-squared differences of the wind speed and direction for the new blended wind data are lower than 2m/s and 30°, respectively. These values are similar to those estimated in the comparisons of hourly buoy measurements and QuikSCAT near real time retrievals. At global scale, it is found that the new products compare well with the wind speed and wind vector components observed by QuikSCAT. No significant dependencies on the QuikSCAT wind speed or on the oceanic region considered are evident.

The impact of a thermodynamic sea-ice module in the COSMO numerical weather prediction model on simulations for the Laptev Sea, Siberian Arctic

“…The biases are below 0.4 ms (1 (Table 1) for all four AWS, the correlation coefficients range from 0.73 to 0.86, and the root mean square (RMS) errors from 0.8 ms (1 to 1.4 ms (1 . The RMS values for six-hourly GME data are remarkably low compared with general RMS errors for daily and monthly reanalysis products, which vary between 1 and 2 ms (1 (Bromwich & Wang 2005;Kolstad 2008;Bentamy et al 2009). The comparison reveals that the synoptic weather situation is well captured by the GME analyses for the Laptev Sea during this period.…”

Prior Selection for QuikSCAT Ultra-High Resolution Wind and Rain Retrieval

“…The NCEP winds used in this study are treated as a truth data set but are in reality only an approximation to the true wind field. Although the NCEP winds do not model small-scale variations in the wind field, they do well on a global scale [15], [16]. TRMM PR rain measurements are very reliable and are well-suited as a comparison data set for rain validation.…”

Detecting and Characterizing Ekman Currents in the Southern Ocean

“…To examine the wind forcing within the Kerguelen region we used gridded wind fields (Bentamy et al 2009) produced by the Institut Francais de Recherche pourl'Exploitation de la Mer (IFREMER), Department of Oceanography, and the Centre ERS d'Archivage et de Traitement (CERSAT). This dataset was produced by merging QuickSCAT scatterometer winds with SSM/I radiometer wind speeds and ECMWF reanalysis wind fields using an objective method (Bentamy et al 2009).…”

“…The observed Ekman current profiles were rotated into a wind-relative reference frame using wind estimates from the IFREMER-blended reanalysis-scatterometer wind fields (Bentamy et al 2009) and mapped onto a regular 2-m depth grid before averaging to produce time-mean hodographs of velocity. This suppresses variability arising from variations in wind heading and allows these results to be compared with prior studies (Lenn and Chereskin 2009;Price et al 1987;Schudlich and Price 1998).…”

“…This dataset was produced by merging QuickSCAT scatterometer winds with SSM/I radiometer wind speeds and ECMWF reanalysis wind fields using an objective method (Bentamy et al 2009). The resulting 10-m wind fields offer 0.258 spatial resolution and 6-h temporal resolution.…”