International audienceOcean surface mixing and drift are influenced by the mixed layer depth, buoyancy fluxes and currents below the mixed layer. Drift and mixing are also functions of the surface Stokes drift Uss, volume Stokes transport TS, a wave breaking height scale Hswg, and the flux of energy from waves to ocean turbulence Φoc. Here we describe a global database of these parameters, estimated from a well-validated numerical wave model, that uses traditional forms of the wave generation and dissipation parameterizations, and covers the years 2003–2007. Compared to previous studies, the present work has the advantage of being consistent with the known physical processes that regulate the wave field and the air–sea fluxes, and also consistent with a very large number of in situ and satellite observations of wave parameters. Consequently, some of our estimates differ significantly from previous estimates. In particular, we find that the mean global integral of Φoc is 68 TW, and the yearly mean value of TS is typically 10–30% of the Ekman transport, except in well-defined regions where it can reach 60%. We also have refined our previous estimates of Uss by using a better treatment of the high frequency part of the wave spectrum. In the open ocean, Uss similar, equals 0.013U10, where U10 is the wind speed at 10 m height

Surface winds from two scatterometers, the Advanced scatterometer (ASCAT), available since 2007, and QuikSCAT, which was available through November 2009, show persistent differences during their period of overlap. This study examines a set of collocated observations during a 13‐month period November 2008 through November 2009, to evaluate the causes of these differences. A difference in the operating frequency of the scatterometers leads to differences that this study argues depend on rain rate, wind velocity, and SST. The impact of rainfall on the higher frequency QuikSCAT introduces biases of up to 1 m s−1 in the tropical convergence zones and along the western boundary currents even after rain flagging is applied. This difference from ASCAT is reduced by some 30% to 40% when data for which the multidimensional rain probabilities > 0.05 is also removed. An additional component of the difference in wind speed seems to be the result of biases in the geophysical transfer functions used in processing the two data sets and is parameterized here as a function of ASCAT wind speed and direction relative to the mid‐beam azimuth. After applying the above two corrections, QuikSCAT wind speed remains systematically lower (by 0.5 m s−1) than ASCAT over regions of cold SST < 5°C. This difference appears to be the result of temperature‐dependence in the viscous damping of surface waves which has a greater impact on shorter waves and thus preferentially impacts QuikSCAT. The difference in wind retrievals also increases in the storm track corridors as well as in the coastal regions where the diurnal cycle of winds is aliased by the time lag between satellites.

Abstract.The new scatterometer Advanced SCATterometer (ASCAT) onboard MetOp-A satellite provides surface wind speed and direction over global ocean with a spatial resolution of 25 km square over two swaths of 550 km widths. The accuracy of ASCAT wind retrievals is determined through various comparisons with moored buoys. The comparisons indicate that the remotely sensed wind speeds and directions agree well with buoy data. The root-mean-squared differences of the wind speed and direction are less than 1.72 m/s and 18 • , respectively. At global scale, ASCAT winds are compared with surface winds derived from QuikSCAT scatterometer. The results confirm the buoy analyses, especially for wind speed ranging between 3 m/s and 20 m/s. For higher wind conditions, ASCAT is biased low. The ASCAT underestimation with respect to QuikSCAT winds is wind speed dependent. The comparisons based on the collocated scatterometer data collected after 17 of October 2007 indicate that there are significant improvements compared to previous periods.

Abstract.Several scientific programs, including the Mediterranean Forecasting System Toward Environmental Predictions (MFSTEP project), request high space and time resolutions of surface wind speed and direction. The purpose of this paper is to focus on surface wind improvements over the global Mediterranean Sea, based on the blending near real time remotely sensed wind observations and ECMWF wind analysis. Ocean surface wind observations are retrieved from QuikSCAT scatterometer and from SSM/I radiometers available at near real time at Météo-France. Using synchronous satellite data, the number of remotely sensed data available for each analysis epoch (00:00 h; 06:00 h; 12:00 h; 18:00 h) is not uniformly distributed as a function of space and time. On average two satellite wind observations are available for each analysis time period. The analysis is performed by optimum interpolation (OI) based on the kriging approach. The needed covariance matrixes are estimated from the satellite wind speed, zonal and meridional component observations. The quality of the 6-hourly resulting blended wind fields on 0.25 • grid are investigated trough comparisons with the remotely sensed observations as well as with moored buoy wind averaged wind estimates. The blended wind data and remotely wind observations, occurring within 3 h and 0.25 • from the analysis estimates, compare well over the global basin as well as over the sub-basins. The correlation coefficients exceed 0.95 while the rms difference values are less than 0.30 m/s. Using measurements from moored buoys, the high-resolution wind fields are found to have similar accuracy as satellite wind retrievals. Blended wind estimates exhibit better comparisons with buoy moored in open sea than near shore.

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.