a290bb8a-f9e9-4011-b081-cebacec746bf Bentamy Abderrahim IFREMER 02 98 22 44 12 02 98 22 45 33 Centre Bretagne - ZI de la Pointe du Diable - CS 10070 - 29280 Plouzané Brest France Abderrahim.Bentamy@ifremer.fr pointOfContact 2025-12-14T11:19:50.379171Z ISO 19115-3 - SEXTANT 1.0 WGS 84 (EPSG:4326) EPSG 8.6 Ocean Heat Fluxes from Remotely Sensed Observations 2019-07-04 publication FR-330-715-368-00032-IFR-HEAT_FLUXES DOI:10.12770/a290bb8a-f9e9-4011-b081-cebacec746bf Daily air-sea heat fluxes dataset on the last 27 years (1992-2018). Global coverage with 0.25° resolution. Data is mainly coming from aggregated calibrated scatterometer datasets and numerical models. Main geophysical parameters are: sensible heat flux, latent heat flux, wind speed, SST, air temperature. Latest version : 4.1 released in June 2019. Ifremer - Laboratoire d’Océanographie Physique et Spatial (LOPS) Bentamy Abderrahim IFREMER 02 98 22 44 12 02 98 22 45 33 Centre Bretagne - ZI de la Pointe du Diable - CS 10070 - 29280 Plouzané Brest France Abderrahim.Bentamy@ifremer.fr author Grouazel Antoine IFREMER Antoine.Grouazel@ifremer.fr pointOfContact Piolle Jean-Francois IFREMER 02 98 22 46 91 02 98 22 45 33 Centre Bretagne - ZI de la Pointe du Diable - CS 10070 - 29280 Plouzané Brest France Jean.Francois.Piolle@ifremer.fr pointOfContact IFREMER sismer@ifremer.fr publisher https://sextant.ifremer.fr/geonetwork/srv/api/records/a290bb8a-f9e9-4011-b081-cebacec746bf/attachments/illustration.png Oceanographic geographical features GEMET - INSPIRE themes, version 1.0 2018-07-27T14:39:48 publication 2018-07-27 publication geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme Thèmes Sextant 2025-12-09 publication geonetwork.thesaurus.local.theme.sextant-theme Sea temperature ODATIS aggregation parameters and Essential Variable names 2025-07-29 publication geonetwork.thesaurus.external.theme.NVS.OD1 Simulation data Type de jeux de donnée ODATIS 2025-12-09 publication geonetwork.thesaurus.local.theme.type_jeux_donnee Données sous Licence ouverte / Open licence : http://www.etalab.gouv.fr/pages/licence-ouverte-open-licence-5899923.html grid utf8 oceans -180.00 180.00 -90.00 90.00 1992-01-01 /home/ref-heat-fluxes NETWORK:LINK https://doi.org/10.12770/a290bb8a-f9e9-4011-b081-cebacec746bf DOI DOI of the dataset DOI of the dataset WWW:DOWNLOAD-1.0-http--download Upgrading and Estimation of Long Time Series of Heat Fluxes from Remotely Sensed Observations https://sextant.ifremer.fr/geonetwork/srv/api/records/a290bb8a-f9e9-4011-b081-cebacec746bf/attachments/IfremerOceanHeatFluxes_V4.1-1.pdf WWW:LINK Report on Heat Fluxes V4.1 Upgrading and Estimation of Long Time Series of Heat Fluxes from Remotely Sensed Observations. 1992 - 2018 https://data-dataref.ifremer.fr/heat-fluxes/ WWW:DOWNLOAD download link This new version of Ifremer daily heat fluxes uses the updated empirical model relating 10m specific air humidity (Qa10) and radiometer brightness temperature (TB) measurements. The determination of the empirical Qa10 model is described in (Bentamy et al, 2013). For this project, a new calibration of Qa10 model is performed in conjunction with the use of the recently re-processed fundamental climate data record (FCDR) of brightness temperatures from the Colorado State University (CSU_FCDR) (Wesley et al, 2013), and the latest reprocessed International Comprehensive Ocean–Atmosphere Data Set (ICOADS Version 3; Freeman et al. 2017). The latter is a compilation of in situ surface marine observations (ship, moored and drift buoys). ICOADS Qa10 and FCDR_CSU TB are collocated in space and time. The resulting collocated data allow the determination and improvement of the satellite Qa10 model. Figure 1 shows examples of monthly mean difference between Qa10 from OHF standard product (Bentamy et al 2017a) and from the updated estimates. The former are calculated from daily estimates occurring in January (Figure 1a) and July (Figure 1b) 2003. The main departure between the two estimates are clearly depicted in Gulf Stream, Kurishio, and Aghulas regions, where the OHF standard data are overestimated compared to the new data. Such overestimation leads to the underestimation of OHF standard LHF (Bentamy et al, 2017a).