Éditeur(s) : The Oceanography Society
Résumé : Data assembly and processing centers are essential elements of the operational oceanography infrastructure. They provide data and products needed by modeling and data assimilation systems; they also provide products directly usable for applications. This paper discusses the role and functions of the data centers for operational oceanography. It describes some of the main data assembly centers (Argo and in situ data, altimetry, sea surface temperature) developed during the Global Ocean Data Assimilation Experiment. An overview of other data centers (wind and fluxes, ocean color, sea ice) is also given. Much progress has been achieved over the past 10 years to validate, intercalibrate, and merge altimeter data from multiple satellites. Accuracy and timeliness of products have been improved, and new products have been developed. The same is true for sea surface temperature data through the Global High-Resolution Sea Surface Temperature Pilot Project. A breakthrough in processing, quality control, and assembly for in situ data has also been achieved through the development of the real-time and delayed-mode Argo data system. In situ and remote-sensing data are now systematically and jointly used to calibrate, validate, and monitor over the long term the quality and consistency of the global ocean observing system. Main results are illustrated. There is also a review of the development and use of products that merge in situ and remote-sensing data. Future issues and main prospects are discussed in the conclusion.
Oceanography (1042-8275) (The Oceanography Society), 2009-09 , Vol. 22 , N. 3 , P. 56-69

Droits : 2009 The Oceanography Society
http://archimer.ifremer.fr/doc/2009/publication-6879.pdf
http://archimer.ifremer.fr/doc/00000/6879/

Éditeur(s) : Taylor & Francis Ltd
Résumé : A new method is developed to estimate daily turbulent airsea fluxes over the global ocean on a 0.25 degrees grid. The required surface wind speed (w(10)) and specific air humidity (q(10)) at 10m height are both estimated from remotely sensed measurements. w(10) is obtained from the SeaWinds scatterometer on board the QuikSCAT satellite. A new empirical model relating brightness temperatures (T-b) from the Special Sensor Microwave Imager (SSM/I) and q(10) is developed. It is an extension of the author's previous q(10) model. In addition to T-b, the empirical model includes sea surface temperature (SST) and airsea temperature difference data. The calibration of the new empirical q(10) model utilizes q(10) from the latest version of the National Oceanography Centre airsea interaction gridded data set (NOCS2.0). Compared with mooring data, the new satellite q(10) exhibits better statistical results than previous estimates. For instance, the bias, the root mean square (RMS), and the correlation coefficient values estimated from comparisons between satellite and moorings in the northeast Atlantic and the Mediterranean Sea are 0.04gkg(1), 0.87gkg(1), and 0.95, respectively. The new satellite q(10) is used in combination with the newly reprocessed QuikSCAT V3, the latest version of SST analyses provided by the National Climatic Data Center (NCDC), and 10m air temperature estimated from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-Interim), to determine three daily gridded turbulent quantities at 0.25 degrees spatial resolution: surface wind stress, latent heat flux (LHF), and sensible heat flux (SHF). Validation of the resulting fields is performed through a comprehensive comparison with daily, in situ values of LHF and SHF from buoys. In the northeast Atlantic basin, the satellite-derived daily LHF has bias, RMS, and correlation of 5Wm(2), 27Wm(2), and 0.89, respectively. For SHF, the statistical parameters are 2Wm(2), 10Wm(2), and 0.94, respectively. At global scale, the new satellite LHF and SHF are compared to NOCS2.0 daily estimates. Both daily fluxes exhibit similar spatial and seasonal variability. The main departures are found at latitudes south of 40 degrees S, where satellite latent and sensible heat fluxes are generally larger.
International Journal Of Remote Sensing (0143-1161) (Taylor & Francis Ltd), 2013-07 , Vol. 34 , N. 14 , P. 5243-5261

Droits : 2013 Taylor & Francis
http://archimer.ifremer.fr/doc/00137/24825/23575.pdf
DOI:10.1080/01431161.2013.787502
http://archimer.ifremer.fr/doc/00137/24825/