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class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="38431410" href="https://www.academia.edu/Documents/in/Pacifism"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/HerveRoquet","location":"/HerveRoquet","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/HerveRoquet","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Pacifism"]}" data-trace="false" data-dom-id="Pill-react-component-3db79489-e56d-40fa-87df-44b1a240d883"></div> <div id="Pill-react-component-3db79489-e56d-40fa-87df-44b1a240d883"></div> </a><a 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</a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Herve Roquet</h3></div><div class="js-work-strip profile--work_container" data-work-id="77035229"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77035229/The_Copernicus_Marine_Environment_Monitoring_Service_Ocean_State_Report"><img alt="Research paper thumbnail of The Copernicus Marine Environment Monitoring Service Ocean State Report" class="work-thumbnail" src="https://attachments.academia-assets.com/84525335/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035229/The_Copernicus_Marine_Environment_Monitoring_Service_Ocean_State_Report">The Copernicus Marine Environment Monitoring Service Ocean State Report</a></div><div class="wp-workCard_item"><span>Journal of Operational Oceanography</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8dfc1d8b4fa101bd25363e41681990fb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" 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paper thumbnail of Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/84566407/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035226/Estimation_of_Sea_Surface_Temperature_from_the_Spinning_Enhanced_Visible_and_Infrared_Imager_improved_using_numerical_weather_prediction">Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared ra...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f982b3a1b0542998e36e9c308b3ebd22" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84566407,"asset_id":77035226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84566407/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035226"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035226]").text(description); $(".js-view-count[data-work-id=77035226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035226, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f982b3a1b0542998e36e9c308b3ebd22" } } $('.js-work-strip[data-work-id=77035226]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035226,"title":"Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. 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(1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... (1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used for comparison. ... (1994) (referred to as da Silva) and by Josey and Josey (referred to asJosey), and those made available by the GPCP through Huffman et al. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035224"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035224"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035224; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035224]").text(description); $(".js-view-count[data-work-id=77035224]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035224; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035224']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035224, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77035224]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035224,"title":"Evaluation of operational ECMWF surface freshwater fluxes over oceans during 1991–1997","translated_title":"","metadata":{"abstract":"... (1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used for comparison. ... (1994) (referred to as da Silva) and by Josey and Josey (referred to asJosey), and those made available by the GPCP through Huffman et al. ...","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":1999,"errors":{}},"publication_name":"Journal of Marine Systems"},"translated_abstract":"... (1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used for comparison. ... (1994) (referred to as da Silva) and by Josey and Josey (referred to asJosey), and those made available by the GPCP through Huffman et al. ...","internal_url":"https://www.academia.edu/77035224/Evaluation_of_operational_ECMWF_surface_freshwater_fluxes_over_oceans_during_1991_1997","translated_internal_url":"","created_at":"2022-04-20T00:29:32.275-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":38431410,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Evaluation_of_operational_ECMWF_surface_freshwater_fluxes_over_oceans_during_1991_1997","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"... (1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used for comparison. ... (1994) (referred to as da Silva) and by Josey and Josey (referred to asJosey), and those made available by the GPCP through Huffman et al. ...","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"U3JQR2h0djY1di9lSlYvQUZzWlhlWWdGb0JwRm9HdCtJbkJmbVB4S215TT0tLXFJNnRmVVI3bkRVMVFXcjhhN044Rmc9PQ==--627535c48d2f60821e611c8bf81e74f24d8b870f"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography"},{"id":3754,"name":"Climatology","url":"https://www.academia.edu/Documents/in/Climatology"},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series"},{"id":13268,"name":"Evaporation","url":"https://www.academia.edu/Documents/in/Evaporation"},{"id":77900,"name":"Marine","url":"https://www.academia.edu/Documents/in/Marine"},{"id":123553,"name":"Water balance","url":"https://www.academia.edu/Documents/in/Water_balance"},{"id":345361,"name":"Geographic distribution","url":"https://www.academia.edu/Documents/in/Geographic_distribution"},{"id":496224,"name":"Marine Systems","url":"https://www.academia.edu/Documents/in/Marine_Systems"},{"id":554355,"name":"Pacific ocean","url":"https://www.academia.edu/Documents/in/Pacific_ocean"},{"id":3236538,"name":"world ocean","url":"https://www.academia.edu/Documents/in/world_ocean"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77035223"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77035223/The_relationship_between_the_simulated_climatic_variability_modes_of_the_tropical_Atlantic"><img alt="Research paper thumbnail of The relationship between the simulated climatic variability modes of the tropical Atlantic" class="work-thumbnail" src="https://attachments.academia-assets.com/84543454/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035223/The_relationship_between_the_simulated_climatic_variability_modes_of_the_tropical_Atlantic">The relationship between the simulated climatic variability modes of the tropical Atlantic</a></div><div class="wp-workCard_item"><span>International Journal of Climatology</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Two main modes of climatic variability occur in the tropical Atlantic Ocean at inter-annual times...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Two main modes of climatic variability occur in the tropical Atlantic Ocean at inter-annual timescales: the equatorial mode, similar to the El Niño phenomenon in the Pacific Ocean, and the meridional mode, or dipole-like mode, with no Pacific counterpart. The Atlantic equatorial mode is characterized by the occurrence of alternating warm and cold episodes at the equator, on the eastern side of the basin. These events are associated with abnormal variations in the zonal equatorial slope of the thermocline. The meridional mode is characterized by an inter-hemispheric gradient in the sea-surface temperature (SST). The mean position of the Inter-tropical Convergence Zone (ITCZ) separates positive and negative SST signals. It was recently shown with observational indices that there is significant correlation between these two climatic modes of variability. This study goes one step further, by using a multi-year numerical simulation, where an oceanic general circulation model is forced by the 1979-1993 ECMWF reanalysis. Model computed indices representing the two main modes of variability compare well with observations. The two inter-annual modes of variability are shown to have the same physics as the annual variability does, which is related to the latitudinal displacement of the ITCZ. Furthermore, it is suggested that the ocean dynamics (as opposed to thermodynamic processes) is the principal cause of climate variability in the region.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5f6bef36e4214d675026f3891a5590b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84543454,"asset_id":77035223,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84543454/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035223"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035223"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035223; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035223]").text(description); $(".js-view-count[data-work-id=77035223]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035223; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035223']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035223, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5f6bef36e4214d675026f3891a5590b0" } } $('.js-work-strip[data-work-id=77035223]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035223,"title":"The relationship between the simulated climatic variability modes of the tropical Atlantic","translated_title":"","metadata":{"publisher":"Wiley-Blackwell","grobid_abstract":"Two main modes of climatic variability occur in the tropical Atlantic Ocean at inter-annual timescales: the equatorial mode, similar to the El Niño phenomenon in the Pacific Ocean, and the meridional mode, or dipole-like mode, with no Pacific counterpart. 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The two inter-annual modes of variability are shown to have the same physics as the annual variability does, which is related to the latitudinal displacement of the ITCZ. 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Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High ResolutionRadiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from -0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between -0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by <0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically. Citation: Merchant, C. J.,A.  R.  Harris,  H.  Roquet,  and  P.  Le  Borgne  (2009),  Retrieval characteristics  of  non-linear  sea  surface  temperature  from  the Advanced Very High Resolution Radiometer, Geophys. Res. Lett.,36, L17604, doi:10.1029/2009GL039843.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="743ba691ce63aec348298b0a151f569f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84674175,"asset_id":77035221,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84674175/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035221"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035221"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035221; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035221]").text(description); $(".js-view-count[data-work-id=77035221]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035221; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035221']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035221, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "743ba691ce63aec348298b0a151f569f" } } $('.js-work-strip[data-work-id=77035221]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035221,"title":"Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer","translated_title":"","metadata":{"doi":"10.1029/2009GL039843","issue":"L17604","volume":"36","abstract":"[1] Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra-red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should beclose to 1 K K-1. Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High ResolutionRadiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from -0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between -0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by \u003c0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically. Citation: Merchant, C. J.,A. R. Harris, H. Roquet, and P. Le Borgne (2009), Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer, Geophys. Res. Lett.,36, L17604, doi:10.1029/2009GL039843.","publisher":"Wiley-Blackwell","ai_title_tag":"Evaluating Non-Linear Sea Surface Temperature Retrievals from AVHRR","page_numbers":"1-5","publication_date":{"day":null,"month":null,"year":2009,"errors":{}},"publication_name":"Geophysical Research Letters"},"translated_abstract":"[1] Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra-red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should beclose to 1 K K-1. Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High ResolutionRadiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from -0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between -0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by \u003c0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically. Citation: Merchant, C. J.,A. R. Harris, H. Roquet, and P. Le Borgne (2009), Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer, Geophys. Res. Lett.,36, L17604, doi:10.1029/2009GL039843.","internal_url":"https://www.academia.edu/77035221/Retrieval_characteristics_of_non_linear_sea_surface_temperature_from_the_Advanced_Very_High_Resolution_Radiometer","translated_internal_url":"","created_at":"2022-04-20T00:29:32.039-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":38431410,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":84674175,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84674175/thumbnails/1.jpg","file_name":"2009GL039843.pdf","download_url":"https://www.academia.edu/attachments/84674175/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Retrieval_characteristics_of_non_linear.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84674175/2009GL039843-libre.pdf?1650631936=\u0026response-content-disposition=attachment%3B+filename%3DRetrieval_characteristics_of_non_linear.pdf\u0026Expires=1733999778\u0026Signature=NEBGLy7rIc8qVfoJtPRPmHGzvozuFURCxbf85aM8Oy-yrcHFOalXZKAkWdoHLdgMpntGbLaWsEsfseznAzRcRbkIhr8iR0Q~0bUP115P4Xtl2dW-irF4k7vI3KDNgx6rsO3s6rnmuJ~hX2YKUlKjJ7NSZIbUeUeuw-DXqLTHbpl0GG1J8cKSpsxtGQiNVgP7blWtNgJ6cE6L570E6makvVoDPs7~Tfmbu6jhn6xtIC6fs9zFVgxhlQKEZcZvKfKzHwRjFY-8CullyhGb1KCbyfZIm7b0SXkiIj6TdTfAuelOfcIjGwX2NT~bn4YZKBFExhCvGNU6FU-DsO18RtpUuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Retrieval_characteristics_of_non_linear_sea_surface_temperature_from_the_Advanced_Very_High_Resolution_Radiometer","translated_slug":"","page_count":5,"language":"en","content_type":"Work","summary":"[1] Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra-red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should beclose to 1 K K-1. Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High ResolutionRadiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from -0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between -0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by \u003c0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically. Citation: Merchant, C. J.,A. R. Harris, H. Roquet, and P. Le Borgne (2009), Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer, Geophys. Res. Lett.,36, L17604, doi:10.1029/2009GL039843.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"QVkyOGN5RmFET3pYQVZSRlpGbEJLdlJ2U3BmV0UvbG9ZbHVmYzYyL2doND0tLXFXZkVqUloyZkN3UTNrRW13NXJHbXc9PQ==--b5dee6624d943a9324fc079c244d418adf63023b"},"attachments":[{"id":84674175,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84674175/thumbnails/1.jpg","file_name":"2009GL039843.pdf","download_url":"https://www.academia.edu/attachments/84674175/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Retrieval_characteristics_of_non_linear.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84674175/2009GL039843-libre.pdf?1650631936=\u0026response-content-disposition=attachment%3B+filename%3DRetrieval_characteristics_of_non_linear.pdf\u0026Expires=1733999778\u0026Signature=NEBGLy7rIc8qVfoJtPRPmHGzvozuFURCxbf85aM8Oy-yrcHFOalXZKAkWdoHLdgMpntGbLaWsEsfseznAzRcRbkIhr8iR0Q~0bUP115P4Xtl2dW-irF4k7vI3KDNgx6rsO3s6rnmuJ~hX2YKUlKjJ7NSZIbUeUeuw-DXqLTHbpl0GG1J8cKSpsxtGQiNVgP7blWtNgJ6cE6L570E6makvVoDPs7~Tfmbu6jhn6xtIC6fs9zFVgxhlQKEZcZvKfKzHwRjFY-8CullyhGb1KCbyfZIm7b0SXkiIj6TdTfAuelOfcIjGwX2NT~bn4YZKBFExhCvGNU6FU-DsO18RtpUuw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":10981,"name":"Data Assimilation","url":"https://www.academia.edu/Documents/in/Data_Assimilation"},{"id":12142,"name":"Bias","url":"https://www.academia.edu/Documents/in/Bias"},{"id":20099,"name":"Sensitivity Analysis","url":"https://www.academia.edu/Documents/in/Sensitivity_Analysis"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":31945,"name":"Sea surface temperature","url":"https://www.academia.edu/Documents/in/Sea_surface_temperature"},{"id":40033,"name":"Numerical Weather Prediction","url":"https://www.academia.edu/Documents/in/Numerical_Weather_Prediction"},{"id":65140,"name":"Models","url":"https://www.academia.edu/Documents/in/Models"},{"id":80308,"name":"Extreme Value Theory","url":"https://www.academia.edu/Documents/in/Extreme_Value_Theory"},{"id":442314,"name":"Radiative Transfer","url":"https://www.academia.edu/Documents/in/Radiative_Transfer"},{"id":2204428,"name":"Infra red","url":"https://www.academia.edu/Documents/in/Infra_red"},{"id":2579578,"name":"surface temperature","url":"https://www.academia.edu/Documents/in/surface_temperature"},{"id":2953864,"name":"Advanced Very High Resolution Radiometer (AVHRR) ","url":"https://www.academia.edu/Documents/in/Advanced_Very_High_Resolution_Radiometer_AVHRR_"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77035164"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77035164/Limagerie_g%C3%A9ostationnaire_et_son_%C3%A9volution"><img alt="Research paper thumbnail of L'imagerie géostationnaire et son évolution" class="work-thumbnail" src="https://attachments.academia-assets.com/84543418/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035164/Limagerie_g%C3%A9ostationnaire_et_son_%C3%A9volution">L'imagerie géostationnaire et son évolution</a></div><div class="wp-workCard_item"><span>La Météorologie</span><span>, 2003</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Les systèmes embarqués Résumé Après un bref rappel sur le programme Météosat, l'évolution de l'im...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Les systèmes embarqués Résumé Après un bref rappel sur le programme Météosat, l'évolution de l'imagerie géostationnaire est illustrée par une description du potentiel de l'imagerie des satellites européens Météosat de seconde génération (MSG), cette mission étant représentative de l'évolution constatée au plan international. Les bénéfices attendus de ces nouvelles observations, tant pour la météorologie opérationnelle que pour les disciplines connexes, sont brièvement présentés. La nécessité d'un effort de développement soutenu est soulignée comme une condition de l'usage optimal des données produites par le système MSG. Les perspectives ultérieures d'évolution sont esquissées.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="812f4538459c0a5a595844ba6f98f80c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84543418,"asset_id":77035164,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84543418/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035164"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035164"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035164; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035164]").text(description); $(".js-view-count[data-work-id=77035164]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035164; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035164']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035164, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "812f4538459c0a5a595844ba6f98f80c" } } $('.js-work-strip[data-work-id=77035164]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035164,"title":"L'imagerie géostationnaire et son évolution","translated_title":"","metadata":{"publisher":"Meteo et Climat, Societe Francaise de la Meteorologie et du Climat","grobid_abstract":"Les systèmes embarqués Résumé Après un bref rappel sur le programme Météosat, l'évolution de l'imagerie géostationnaire est illustrée par une description du potentiel de l'imagerie des satellites européens Météosat de seconde génération (MSG), cette mission étant représentative de l'évolution constatée au plan international. Les bénéfices attendus de ces nouvelles observations, tant pour la météorologie opérationnelle que pour les disciplines connexes, sont brièvement présentés. La nécessité d'un effort de développement soutenu est soulignée comme une condition de l'usage optimal des données produites par le système MSG. 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Les bénéfices attendus de ces nouvelles observations, tant pour la météorologie opérationnelle que pour les disciplines connexes, sont brièvement présentés. La nécessité d'un effort de développement soutenu est soulignée comme une condition de l'usage optimal des données produites par le système MSG. Les perspectives ultérieures d'évolution sont esquissées.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"OGcrcVlZeTUwYmNvWUgxUFlGa1NCNDA1Q2lPNHN2dTcxdGdjQVlkNnE4OD0tLTVDdHM4TXhYTVJOb0M1U2JhNitNakE9PQ==--f2f3c707c3026fb1079081ddfcadf470951d3937"},"attachments":[{"id":84543418,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84543418/thumbnails/1.jpg","file_name":"meteo_2003_40_74.pdf","download_url":"https://www.academia.edu/attachments/84543418/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Limagerie_geostationnaire_et_son_evoluti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84543418/meteo_2003_40_74-libre.pdf?1650460562=\u0026response-content-disposition=attachment%3B+filename%3DLimagerie_geostationnaire_et_son_evoluti.pdf\u0026Expires=1733999778\u0026Signature=YnK4G6pmuBHdf918Sw8jNDHgKywqKZ7cPfVEipXUjvU0WFhkVkZ1J7~-pf4fGIDabC6b9pyNVrgucAzy6SOy1NSQ3HvifRX4ipmUJxm9LfgycjdljLKNMb~WNJHR1bVt0J3s-onCN3M8TmD~spTk7D24rQPAS~0G1LVdbNukDXkHj4U3oH264nwXgJgas31fY-WcitUU~2Z10yEOcEsAj8a~1oaNjUFEVefQt7-A7nbG1hJXICisrv8BhQGVhEbsR6FgxpwGpSGsN7S3tlzzt8d258Ns61Vvis8D0dDNnMjph1O3BKWSLCdLKQendqNBdnz5AtszlawaOhN9S9qHcA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography"},{"id":10882,"name":"Evolution","url":"https://www.academia.edu/Documents/in/Evolution"},{"id":1224023,"name":"Météorologie","url":"https://www.academia.edu/Documents/in/Meteorologie"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="54158789"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/54158789/R%C3%A9alisation_dune_climatologie_mondiale_de_la_temp%C3%A9rature_de_surface_de_la_mer_%C3%A0_%C3%A9chelle_fine"><img alt="Research paper thumbnail of Réalisation d'une climatologie mondiale de la température de surface de la mer à échelle fine" class="work-thumbnail" src="https://attachments.academia-assets.com/70658557/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/54158789/R%C3%A9alisation_dune_climatologie_mondiale_de_la_temp%C3%A9rature_de_surface_de_la_mer_%C3%A0_%C3%A9chelle_fine">Réalisation d'une climatologie mondiale de la température de surface de la mer à échelle fine</a></div><div class="wp-workCard_item"><span>La Météorologie</span><span>, 2001</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Météorologie spatiale Une nouvelle climatologie décadaire de la température de surface de la mer ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Météorologie spatiale Une nouvelle climatologie décadaire de la température de surface de la mer à échelle fine a été réalisée pour améliorer la détection des nuages sur la mer dans l'imagerie satellitaire. Elle a été construite à partir des données AVHRR de nuit provenant des satellites NOAA sur la période 1985-1995. Cette climatologie comporte des champs de température moyenne, de température minimale et de température maximale à environ 9 kilomètres de résolution. Des champs du nombre de cas utilisés pour les calculs statistiques, de présence de glace et d'écart type ont également été calculés pour déterminer la qualité obtenue en chaque point de grille. Une méthode d'interpolation optimale a été mise en oeuvre pour pallier le manque de données dans les zones de nébulosité persistante. La haute résolution permet de mettre en évidence des structures fines ou des phénomènes locaux, tels les upwellings côtiers. Le réalisme de cette nouvelle climatologie a été évalué par comparaison aux climatologies existantes et à des observations in situ. A global fine-scale sea-surface temperature climatology A new 10-day period fine-scale sea-surface temperature climatology has been built to improve the detection of clouds over the sea in satellite imagery. We used night time AVHRR data from NOAA satellites over the period 1985-1995. This climatology is composed of fields of mean, minimum and maximum temperatures, at a resolution of about 9 km. Fields of number of cases used in the statistics, standard deviation and sea-ice presence were derived to characterise the quality obtained on each grid point. An optimal interpolation technique has been used to compensate for the lack of data in permanently cloudy areas. The small scale adopted resolves fine structures like coastal upwellings. The performance of this new climatology has been evaluated by comparison with existing climatologies and in situ observations. Le Centre de météorologie spatiale (CMS) de Météo-France est engagé depuis 1997 dans le projet « Ocean and Sea Ice Satellite Application Facility » (appelé ici SAF Océan), soutenu par l'organisation européenne Eumetsat. L'objectif du SAF Océan est de fournir aux utilisateurs, parmi lesquels les services météorologiques des pays membres d'Eumetsat, les champs à la surface de la mer du vent, des flux radiatifs et de la température de l'eau, calculés à partir des données des</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="da48c64902bb08ff4b132bad466d99ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":70658557,"asset_id":54158789,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/70658557/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="54158789"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="54158789"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 54158789; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=54158789]").text(description); $(".js-view-count[data-work-id=54158789]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 54158789; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='54158789']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 54158789, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "da48c64902bb08ff4b132bad466d99ce" } } $('.js-work-strip[data-work-id=54158789]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":54158789,"title":"Réalisation d'une climatologie mondiale de la température de surface de la mer à échelle fine","translated_title":"","metadata":{"publisher":"INIST-CNRS","grobid_abstract":"Météorologie spatiale Une nouvelle climatologie décadaire de la température de surface de la mer à échelle fine a été réalisée pour améliorer la détection des nuages sur la mer dans l'imagerie satellitaire. 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A global fine-scale sea-surface temperature climatology A new 10-day period fine-scale sea-surface temperature climatology has been built to improve the detection of clouds over the sea in satellite imagery. We used night time AVHRR data from NOAA satellites over the period 1985-1995. This climatology is composed of fields of mean, minimum and maximum temperatures, at a resolution of about 9 km. Fields of number of cases used in the statistics, standard deviation and sea-ice presence were derived to characterise the quality obtained on each grid point. An optimal interpolation technique has been used to compensate for the lack of data in permanently cloudy areas. The small scale adopted resolves fine structures like coastal upwellings. The performance of this new climatology has been evaluated by comparison with existing climatologies and in situ observations. 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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 insitu 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 QuikSCA T 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 QuikS-CA T. No significant dependencies on the QuikSCAT wind speed or on the oceanic region considered are evident. LEAD AUTHOR'S BIOGRAPHY Dr Abderrahim Bentamy is a research engineer at IFRE-MER (Brest) working on active and passive microwave detection, numerical wind. flux and stress field analysis, ocean circulation and process studies. He has worked in the scatterometer callval teams for ESA ERS. NASA NSCAT. and the NASA/NOAA SeaWinds scatterometers, and more recently was involved in ASCA T validation. He is involved in the MERSEA and MyOcean European Union projects that provide, among others. global wind, stress and flux fields. based on a blend of active and passive satellite data products. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883216"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883216/Neural_network_wind_retrieval_from_ERS_1_scatterometer_data"><img alt="Research paper thumbnail of Neural network wind retrieval from ERS-1 scatterometer data" class="work-thumbnail" src="https://attachments.academia-assets.com/41152630/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883216/Neural_network_wind_retrieval_from_ERS_1_scatterometer_data">Neural network wind retrieval from ERS-1 scatterometer data</a></div><div class="wp-workCard_item"><span>Neurocomputing</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a neural network methodology to retrieve wind vectors from ERS-1 scatteromete...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a neural network methodology to retrieve wind vectors from ERS-1 scatterometer data. First, a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated wind direction are given. At least 75% of the most probable wind directions are consistent with European Centre for Medium-Range Weather Forecasts winds (at _+20ø). Then the remaining ambiguities are resolved by an adapted PRESCAT method that uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the skill of the method. The good performance is mainly due to the use of a spatial context and to the probabilistic approach adopted to estimate the wind direction. Comparisons with other methods are also presented. The good performance of the neural network method suggests that a selfconsistent wind retrieval from ERS-1 scatterometer is possible.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6e46641d1ef8fc04eb8187c0b70dd080" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41152630,"asset_id":19883216,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41152630/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883216"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883216"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883216; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883216]").text(description); $(".js-view-count[data-work-id=19883216]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883216; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883216']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883216, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6e46641d1ef8fc04eb8187c0b70dd080" } } $('.js-work-strip[data-work-id=19883216]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883216,"title":"Neural network wind retrieval from ERS-1 scatterometer data","translated_title":"","metadata":{"grobid_abstract":"This paper presents a neural network methodology to retrieve wind vectors from ERS-1 scatterometer data. 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CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883215"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883215"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883215; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883215]").text(description); $(".js-view-count[data-work-id=19883215]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883215; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883215']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883215, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=19883215]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883215,"title":"OSI-SAF operational NPP/VIIRS sea surface temperature chain","translated_title":"","metadata":{"abstract":"ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) have been acquired at Centre de Météorologie Spatiale (CMS) in Lannion (Brittany) in direct readout mode since April 2012. CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Ocean Sensing and Monitoring V"},"translated_abstract":"ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) have been acquired at Centre de Météorologie Spatiale (CMS) in Lannion (Brittany) in direct readout mode since April 2012. CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.","internal_url":"https://www.academia.edu/19883215/OSI_SAF_operational_NPP_VIIRS_sea_surface_temperature_chain","translated_internal_url":"","created_at":"2015-12-29T02:57:42.819-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":38431410,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"OSI_SAF_operational_NPP_VIIRS_sea_surface_temperature_chain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) have been acquired at Centre de Météorologie Spatiale (CMS) in Lannion (Brittany) in direct readout mode since April 2012. CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"ZzRZYWZiVVJDeGFJbUVYWmg5NnRqQ3dsVFRDVXdEdXVZajJDcEZKTXFwST0tLUVNUk5OLzRYZGwxV0VGcTQwcnN6TlE9PQ==--e34b8d1b08203f0213ff4e2dfafec0d8174cb199"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883214"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883214/Six_years_of_OSI_SAF_METOP_A_AVHRR_sea_surface_temperature"><img alt="Research paper thumbnail of Six years of OSI-SAF METOP-A AVHRR sea surface temperature" class="work-thumbnail" src="https://attachments.academia-assets.com/41152512/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883214/Six_years_of_OSI_SAF_METOP_A_AVHRR_sea_surface_temperature">Six years of OSI-SAF METOP-A AVHRR sea surface temperature</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Ocean and Sea Ice Satellite Application Facility (OSI SAF) has been producing full resolution...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Ocean and Sea Ice Satellite Application Facility (OSI SAF) has been producing full resolution global Sea Surface Temperature (SST) from the METOP-A Advanced Very High Resolution Radiometer (AVHRR) since July 2007. The SST operational processing and the validation scheme have remained unchanged for more than 6 years. The global validation results against measurements are stable over time. Night-time METOP-A SSTs differ from drifting buoy SSTs by −0.05 K in average with a standard deviation of 0.44 K and the daytime values are respectively 0.09 K and 0.56 K. Seasonal statistics have been calculated on a global regular 5-degree grid for a 6-year period to review the main biases and their characteristics. There is evidence of regional and seasonal biases, indeed the multispectral regression algorithms are known (to a various degree depending upon specific implementation) to have limitations in handling the variety of atmospheric absorption conditions encountered over the global ocean. This problem has been solved for the OSI SAF geostationary SST chain by adopting a Numerical Weather Prediction (NWP) profile based correction method. The same approach has been tested on a prototype chain ingesting METOP-A data and gives encouraging results. It will be used in the new polar orbiter chain under development at OSI SAF, that will process METOP-B data. An application example of METOP-A SST time series is given by analyzing the inter-annual variability of Arctic Ocean SST in relation with the ice coverage variability in September. The METOP-A time series gives consistent results when compared to other observations or model outputs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f79c05b43e3f0ae90338f3714fe473d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41152512,"asset_id":19883214,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41152512/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883214"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883214"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883214; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883214]").text(description); $(".js-view-count[data-work-id=19883214]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883214; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883214']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883214, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f79c05b43e3f0ae90338f3714fe473d6" } } $('.js-work-strip[data-work-id=19883214]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883214,"title":"Six years of OSI-SAF METOP-A AVHRR sea surface temperature","translated_title":"","metadata":{"grobid_abstract":"The Ocean and Sea Ice Satellite Application Facility (OSI SAF) has been producing full resolution global Sea Surface Temperature (SST) from the METOP-A Advanced Very High Resolution Radiometer (AVHRR) since July 2007. 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The same approach has been tested on a prototype chain ingesting METOP-A data and gives encouraging results. It will be used in the new polar orbiter chain under development at OSI SAF, that will process METOP-B data. An application example of METOP-A SST time series is given by analyzing the inter-annual variability of Arctic Ocean SST in relation with the ice coverage variability in September. 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The SST operational processing and the validation scheme have remained unchanged for more than 6 years. The global validation results against measurements are stable over time. Night-time METOP-A SSTs differ from drifting buoy SSTs by −0.05 K in average with a standard deviation of 0.44 K and the daytime values are respectively 0.09 K and 0.56 K. Seasonal statistics have been calculated on a global regular 5-degree grid for a 6-year period to review the main biases and their characteristics. There is evidence of regional and seasonal biases, indeed the multispectral regression algorithms are known (to a various degree depending upon specific implementation) to have limitations in handling the variety of atmospheric absorption conditions encountered over the global ocean. This problem has been solved for the OSI SAF geostationary SST chain by adopting a Numerical Weather Prediction (NWP) profile based correction method. The same approach has been tested on a prototype chain ingesting METOP-A data and gives encouraging results. It will be used in the new polar orbiter chain under development at OSI SAF, that will process METOP-B data. An application example of METOP-A SST time series is given by analyzing the inter-annual variability of Arctic Ocean SST in relation with the ice coverage variability in September. The METOP-A time series gives consistent results when compared to other observations or model outputs.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"dlkyejlPdVgrR0t5ZTU0MTNaT1kxbGJwQW1wb2hyTkZSK0ZjVld5WGh6az0tLXZuemk2YVFkK1NPc1FJTk1hb3lPV3c9PQ==--bbeb04c9dcb09023e16a4035101fd20a7e899c67"},"attachments":[{"id":41152512,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41152512/thumbnails/1.jpg","file_name":"Six_years_of_OSI-SAF_METOP-A_AVHRR_sea_s20160114-24366-17sgyxq.pdf","download_url":"https://www.academia.edu/attachments/41152512/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Six_years_of_OSI_SAF_METOP_A_AVHRR_sea_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41152512/Six_years_of_OSI-SAF_METOP-A_AVHRR_sea_s20160114-24366-17sgyxq-libre.pdf?1452823979=\u0026response-content-disposition=attachment%3B+filename%3DSix_years_of_OSI_SAF_METOP_A_AVHRR_sea_s.pdf\u0026Expires=1733999779\u0026Signature=aA-tmH93DpCmyEGppmIRNtj1-09q4YnURAK0LwnFlYY01Nq03b1nztJmZ3TFNL6qRYPegUrgjyCv-7iFrcbP~EbMUAmpgF7eYVj1~Jo3~cv0NNG4YcE4W04xYqXedTw2OAAEPvFFMPlspS3tZyu2TbE1CQfs4WgzTWmy751ojTQ~I2OLZNyEfPy2SNwADlcAWub50Fvdq7MX7qdfd~kUV3ZINokYgQ2IbfVd7VFFblk3fqiEDYL9Shs-jwTWcTt0sVquziBpdv0NoGZOHegxxpHtBhhx8GN1am9Vuf-xKYQLWeYFSyABFl0FboY-NpxCeXOlJ0oTA11g-n1qQi8EcA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883213"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883213/Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea"><img alt="Research paper thumbnail of Assessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea" class="work-thumbnail" src="https://attachments.academia-assets.com/42112521/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883213/Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea">Assessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiomet...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (&amp;lt;5 days) and higher spatial smoothing (&amp;gt;10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="83e7ff869c79836db1c0c7de06c51620" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42112521,"asset_id":19883213,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42112521/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883213"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883213"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883213; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883213]").text(description); $(".js-view-count[data-work-id=19883213]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883213; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883213']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883213, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "83e7ff869c79836db1c0c7de06c51620" } } $('.js-work-strip[data-work-id=19883213]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883213,"title":"Assessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea","translated_title":"","metadata":{"abstract":"ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (\u0026amp;lt;5 days) and higher spatial smoothing (\u0026amp;gt;10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Remote Sensing of Environment"},"translated_abstract":"ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (\u0026amp;lt;5 days) and higher spatial smoothing (\u0026amp;gt;10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results.","internal_url":"https://www.academia.edu/19883213/Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea","translated_internal_url":"","created_at":"2015-12-29T02:57:42.342-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":38431410,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":42112521,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/42112521/thumbnails/1.jpg","file_name":"Assessing_the_impact_of_brightness_tempe20160204-19318-yp5my.pdf","download_url":"https://www.academia.edu/attachments/42112521/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessing_the_impact_of_brightness_tempe.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/42112521/Assessing_the_impact_of_brightness_tempe20160204-19318-yp5my-libre.pdf?1454653429=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_impact_of_brightness_tempe.pdf\u0026Expires=1733999779\u0026Signature=O0EueumgSI~7vtOlJfD6Ag7sCi5vz~riGlAVnUhCJv4JLEhfJUEmqHFdlIftAC3wmPa8yBttCj0TAudIBCIk5wwr~snbtee1LlP8mBU62KUVMika-1W7bFYxPnZgs2Njh8ztYzcgHKQo801j7t7dl0mo9q16vuM8hN9IrFl7Bc0QDW~zUUKDEWDP1xBp84n4cLKc4MtAPINvw~baum6L5hOoYFS-AtLDDf38HJSSqrW41a5M9s4VjHuF54WHE1M-U-0AHFjXUk0axp~3CIBNdgIhIKbS6V-0visTLEdY~-K8OOcWrH-L2J7Zw0AHGN3IjwAPvh1JIyfB-Pbgq5zNIw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea","translated_slug":"","page_count":20,"language":"en","content_type":"Work","summary":"ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (\u0026amp;lt;5 days) and higher spatial smoothing (\u0026amp;gt;10 deg) for nighttime. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883212"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883212/Night_time_detection_of_Saharan_dust_using_infrared_window_channels_Application_to_NPP_VIIRS"><img alt="Research paper thumbnail of Night time detection of Saharan dust using infrared window channels: Application to NPP/VIIRS" class="work-thumbnail" src="https://attachments.academia-assets.com/41152003/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883212/Night_time_detection_of_Saharan_dust_using_infrared_window_channels_Application_to_NPP_VIIRS">Night time detection of Saharan dust using infrared window channels: Application to NPP/VIIRS</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90eb3553eba9b748afa8e08d96bd6377" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41152003,"asset_id":19883212,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41152003/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883212"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883212"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883212; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883212]").text(description); $(".js-view-count[data-work-id=19883212]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883212; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883212']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883212, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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By developing a Saharan Dust Index (SDI) tailored for VIIRS, the study improves the capability to monitor SST corrections for dust-induced errors, especially during nighttime. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883211"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883211/Determination_of_ocean_surface_heat_fluxes_by_a_variational_method"><img alt="Research paper thumbnail of Determination of ocean surface heat fluxes by a variational method" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883211/Determination_of_ocean_surface_heat_fluxes_by_a_variational_method">Determination of ocean surface heat fluxes by a variational method</a></div><div class="wp-workCard_item"><span>Journal of Geophysical Research</span><span>, 1993</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new technique of determination of the ``nonsolar&amp;amp;amp;amp;amp;#39;&amp;amp;amp;amp;amp;#...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new technique of determination of the ``nonsolar&amp;amp;amp;amp;amp;#39;&amp;amp;amp;amp;amp;#39; heat flux (sum of the latent, sensible, and net infrared fluxes) at the ocean surface is proposed. It applies when oceanic advection remains weak and thus relies on a one-dimensional modeling approach. It is based on a variational data assimilation scheme using the adjoint equation formalism. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="4038708" id="papers"><div class="js-work-strip profile--work_container" data-work-id="77035229"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77035229/The_Copernicus_Marine_Environment_Monitoring_Service_Ocean_State_Report"><img alt="Research paper thumbnail of The Copernicus Marine Environment Monitoring Service Ocean State Report" class="work-thumbnail" src="https://attachments.academia-assets.com/84525335/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035229/The_Copernicus_Marine_Environment_Monitoring_Service_Ocean_State_Report">The Copernicus Marine Environment Monitoring Service Ocean State Report</a></div><div class="wp-workCard_item"><span>Journal of Operational Oceanography</span><span>, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8dfc1d8b4fa101bd25363e41681990fb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84525335,"asset_id":77035229,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84525335/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035229"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035229"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035229; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77035226"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77035226/Estimation_of_Sea_Surface_Temperature_from_the_Spinning_Enhanced_Visible_and_Infrared_Imager_improved_using_numerical_weather_prediction"><img alt="Research paper thumbnail of Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction" class="work-thumbnail" src="https://attachments.academia-assets.com/84566407/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035226/Estimation_of_Sea_Surface_Temperature_from_the_Spinning_Enhanced_Visible_and_Infrared_Imager_improved_using_numerical_weather_prediction">Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared ra...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f982b3a1b0542998e36e9c308b3ebd22" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84566407,"asset_id":77035226,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84566407/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035226"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035226"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035226; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035226]").text(description); $(".js-view-count[data-work-id=77035226]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035226; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035226']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035226, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f982b3a1b0542998e36e9c308b3ebd22" } } $('.js-work-strip[data-work-id=77035226]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035226,"title":"Estimation of Sea Surface Temperature from the Spinning Enhanced Visible and Infrared Imager, improved using numerical weather prediction","translated_title":"","metadata":{"publisher":"Elsevier BV","grobid_abstract":"Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. 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They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). 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(1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... (1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used for comparison. ... (1994) (referred to as da Silva) and by Josey and Josey (referred to asJosey), and those made available by the GPCP through Huffman et al. ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035224"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035224"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035224; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035224]").text(description); $(".js-view-count[data-work-id=77035224]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035224; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035224']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035224, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=77035224]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035224,"title":"Evaluation of operational ECMWF surface freshwater fluxes over oceans during 1991–1997","translated_title":"","metadata":{"abstract":"... (1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used for comparison. ... (1994) (referred to as da Silva) and by Josey and Josey (referred to asJosey), and those made available by the GPCP through Huffman et al. ...","publisher":"Elsevier BV","publication_date":{"day":null,"month":null,"year":1999,"errors":{}},"publication_name":"Journal of Marine Systems"},"translated_abstract":"... (1994) for the 19451989 period and Josey and Josey for the 19801993 period, and are used for comparison. ... 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(1994) (referred to as da Silva) and by Josey and Josey (referred to asJosey), and those made available by the GPCP through Huffman et al. ...","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"U3JQR2h0djY1di9lSlYvQUZzWlhlWWdGb0JwRm9HdCtJbkJmbVB4S215TT0tLXFJNnRmVVI3bkRVMVFXcjhhN044Rmc9PQ==--627535c48d2f60821e611c8bf81e74f24d8b870f"},"attachments":[],"research_interests":[{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":415,"name":"Oceanography","url":"https://www.academia.edu/Documents/in/Oceanography"},{"id":3754,"name":"Climatology","url":"https://www.academia.edu/Documents/in/Climatology"},{"id":4456,"name":"Time Series","url":"https://www.academia.edu/Documents/in/Time_Series"},{"id":13268,"name":"Evaporation","url":"https://www.academia.edu/Documents/in/Evaporation"},{"id":77900,"name":"Marine","url":"https://www.academia.edu/Documents/in/Marine"},{"id":123553,"name":"Water balance","url":"https://www.academia.edu/Documents/in/Water_balance"},{"id":345361,"name":"Geographic distribution","url":"https://www.academia.edu/Documents/in/Geographic_distribution"},{"id":496224,"name":"Marine Systems","url":"https://www.academia.edu/Documents/in/Marine_Systems"},{"id":554355,"name":"Pacific ocean","url":"https://www.academia.edu/Documents/in/Pacific_ocean"},{"id":3236538,"name":"world ocean","url":"https://www.academia.edu/Documents/in/world_ocean"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77035223"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77035223/The_relationship_between_the_simulated_climatic_variability_modes_of_the_tropical_Atlantic"><img alt="Research paper thumbnail of The relationship between the simulated climatic variability modes of the tropical Atlantic" class="work-thumbnail" src="https://attachments.academia-assets.com/84543454/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035223/The_relationship_between_the_simulated_climatic_variability_modes_of_the_tropical_Atlantic">The relationship between the simulated climatic variability modes of the tropical Atlantic</a></div><div class="wp-workCard_item"><span>International Journal of Climatology</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Two main modes of climatic variability occur in the tropical Atlantic Ocean at inter-annual times...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Two main modes of climatic variability occur in the tropical Atlantic Ocean at inter-annual timescales: the equatorial mode, similar to the El Niño phenomenon in the Pacific Ocean, and the meridional mode, or dipole-like mode, with no Pacific counterpart. The Atlantic equatorial mode is characterized by the occurrence of alternating warm and cold episodes at the equator, on the eastern side of the basin. These events are associated with abnormal variations in the zonal equatorial slope of the thermocline. The meridional mode is characterized by an inter-hemispheric gradient in the sea-surface temperature (SST). The mean position of the Inter-tropical Convergence Zone (ITCZ) separates positive and negative SST signals. It was recently shown with observational indices that there is significant correlation between these two climatic modes of variability. This study goes one step further, by using a multi-year numerical simulation, where an oceanic general circulation model is forced by the 1979-1993 ECMWF reanalysis. Model computed indices representing the two main modes of variability compare well with observations. The two inter-annual modes of variability are shown to have the same physics as the annual variability does, which is related to the latitudinal displacement of the ITCZ. Furthermore, it is suggested that the ocean dynamics (as opposed to thermodynamic processes) is the principal cause of climate variability in the region.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5f6bef36e4214d675026f3891a5590b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84543454,"asset_id":77035223,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84543454/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035223"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035223"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035223; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035223]").text(description); $(".js-view-count[data-work-id=77035223]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035223; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035223']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035223, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "5f6bef36e4214d675026f3891a5590b0" } } $('.js-work-strip[data-work-id=77035223]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035223,"title":"The relationship between the simulated climatic variability modes of the tropical Atlantic","translated_title":"","metadata":{"publisher":"Wiley-Blackwell","grobid_abstract":"Two main modes of climatic variability occur in the tropical Atlantic Ocean at inter-annual timescales: the equatorial mode, similar to the El Niño phenomenon in the Pacific Ocean, and the meridional mode, or dipole-like mode, with no Pacific counterpart. 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The Atlantic equatorial mode is characterized by the occurrence of alternating warm and cold episodes at the equator, on the eastern side of the basin. These events are associated with abnormal variations in the zonal equatorial slope of the thermocline. The meridional mode is characterized by an inter-hemispheric gradient in the sea-surface temperature (SST). The mean position of the Inter-tropical Convergence Zone (ITCZ) separates positive and negative SST signals. It was recently shown with observational indices that there is significant correlation between these two climatic modes of variability. This study goes one step further, by using a multi-year numerical simulation, where an oceanic general circulation model is forced by the 1979-1993 ECMWF reanalysis. Model computed indices representing the two main modes of variability compare well with observations. The two inter-annual modes of variability are shown to have the same physics as the annual variability does, which is related to the latitudinal displacement of the ITCZ. Furthermore, it is suggested that the ocean dynamics (as opposed to thermodynamic processes) is the principal cause of climate variability in the region.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"L3FrQk9SWDhGRndCNXREUmpDSmkra0dQVGw4eHVKeVRvaWlHanRUSS90MD0tLXFHK1RTT0ZIQ2VyaW4wV2dYTFhaMnc9PQ==--3538e33257a5e135b315f113f97a622b4e7d38a5"},"attachments":[{"id":84543454,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84543454/thumbnails/1.jpg","file_name":"010022375.pdf","download_url":"https://www.academia.edu/attachments/84543454/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"The_relationship_between_the_simulated_c.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84543454/010022375-libre.pdf?1650460563=\u0026response-content-disposition=attachment%3B+filename%3DThe_relationship_between_the_simulated_c.pdf\u0026Expires=1733999778\u0026Signature=fpGAITn9atR7Nt4VIyB-iH1v7fXMaxSSnZL~1eI6hwgxnq9msTIWHfntufsDsG2wgKrl0HwTb18vgGunS3i18y1T3gIXHo~C5qSh0xwv1T4N4o4LCuAMxrIbND-l0vKoPxv17N8kUzr4GLwWRwbt3ZGd1lSP285PVkAZqQuiKRoOb7qian56TvTaQiXc02Cmgcz7iie3mVbTjniEA0etB45w3ASfJ~28ZR9C3wThKVVCjTMuug32sjGPKxZXS0sBV9wZ9uuE8uRAasCeb0rR2PilLwHhZuRdbUMVlsL3NXdFF8z7yvK8ilrPZKmeQsVmk-PRQVyC0easTeQmZ3B4Bw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":55,"name":"Environmental Engineering","url":"https://www.academia.edu/Documents/in/Environmental_Engineering"},{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering"},{"id":402,"name":"Environmental Science","url":"https://www.academia.edu/Documents/in/Environmental_Science"},{"id":522,"name":"Thermodynamics","url":"https://www.academia.edu/Documents/in/Thermodynamics"},{"id":3754,"name":"Climatology","url":"https://www.academia.edu/Documents/in/Climatology"},{"id":4753,"name":"Climate variability","url":"https://www.academia.edu/Documents/in/Climate_variability"},{"id":31945,"name":"Sea surface temperature","url":"https://www.academia.edu/Documents/in/Sea_surface_temperature"},{"id":60285,"name":"Atmospheric sciences","url":"https://www.academia.edu/Documents/in/Atmospheric_sciences"},{"id":60658,"name":"Numerical Simulation","url":"https://www.academia.edu/Documents/in/Numerical_Simulation"},{"id":133177,"name":"Temperature","url":"https://www.academia.edu/Documents/in/Temperature"},{"id":554355,"name":"Pacific ocean","url":"https://www.academia.edu/Documents/in/Pacific_ocean"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="77035221"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/77035221/Retrieval_characteristics_of_non_linear_sea_surface_temperature_from_the_Advanced_Very_High_Resolution_Radiometer"><img alt="Research paper thumbnail of Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer" class="work-thumbnail" src="https://attachments.academia-assets.com/84674175/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/77035221/Retrieval_characteristics_of_non_linear_sea_surface_temperature_from_the_Advanced_Very_High_Resolution_Radiometer">Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer</a></div><div class="wp-workCard_item"><span>Geophysical Research Letters</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">[1] Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">[1]  Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra-red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should beclose to 1 K K-1. Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High ResolutionRadiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from -0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between -0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by <0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically. Citation: Merchant, C. J.,A.  R.  Harris,  H.  Roquet,  and  P.  Le  Borgne  (2009),  Retrieval characteristics  of  non-linear  sea  surface  temperature  from  the Advanced Very High Resolution Radiometer, Geophys. Res. Lett.,36, L17604, doi:10.1029/2009GL039843.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="743ba691ce63aec348298b0a151f569f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84674175,"asset_id":77035221,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84674175/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="77035221"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="77035221"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 77035221; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=77035221]").text(description); $(".js-view-count[data-work-id=77035221]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 77035221; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='77035221']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 77035221, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "743ba691ce63aec348298b0a151f569f" } } $('.js-work-strip[data-work-id=77035221]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":77035221,"title":"Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer","translated_title":"","metadata":{"doi":"10.1029/2009GL039843","issue":"L17604","volume":"36","abstract":"[1] Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra-red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should beclose to 1 K K-1. Their application is illustrated for non-linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High ResolutionRadiometer (AVHRR) on Metop-A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from -0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between -0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by \u003c0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically. Citation: Merchant, C. J.,A. R. Harris, H. Roquet, and P. Le Borgne (2009), Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer, Geophys. Res. 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Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between -0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by \u003c0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically. Citation: Merchant, C. J.,A. R. Harris, H. Roquet, and P. Le Borgne (2009), Retrieval characteristics of non-linear sea surface temperature from the Advanced Very High Resolution Radiometer, Geophys. Res. 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Les bénéfices attendus de ces nouvelles observations, tant pour la météorologie opérationnelle que pour les disciplines connexes, sont brièvement présentés. La nécessité d'un effort de développement soutenu est soulignée comme une condition de l'usage optimal des données produites par le système MSG. 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Elle a été construite à partir des données AVHRR de nuit provenant des satellites NOAA sur la période 1985-1995. Cette climatologie comporte des champs de température moyenne, de température minimale et de température maximale à environ 9 kilomètres de résolution. Des champs du nombre de cas utilisés pour les calculs statistiques, de présence de glace et d'écart type ont également été calculés pour déterminer la qualité obtenue en chaque point de grille. Une méthode d'interpolation optimale a été mise en oeuvre pour pallier le manque de données dans les zones de nébulosité persistante. La haute résolution permet de mettre en évidence des structures fines ou des phénomènes locaux, tels les upwellings côtiers. Le réalisme de cette nouvelle climatologie a été évalué par comparaison aux climatologies existantes et à des observations in situ. A global fine-scale sea-surface temperature climatology A new 10-day period fine-scale sea-surface temperature climatology has been built to improve the detection of clouds over the sea in satellite imagery. We used night time AVHRR data from NOAA satellites over the period 1985-1995. This climatology is composed of fields of mean, minimum and maximum temperatures, at a resolution of about 9 km. Fields of number of cases used in the statistics, standard deviation and sea-ice presence were derived to characterise the quality obtained on each grid point. An optimal interpolation technique has been used to compensate for the lack of data in permanently cloudy areas. The small scale adopted resolves fine structures like coastal upwellings. The performance of this new climatology has been evaluated by comparison with existing climatologies and in situ observations. Le Centre de météorologie spatiale (CMS) de Météo-France est engagé depuis 1997 dans le projet « Ocean and Sea Ice Satellite Application Facility » (appelé ici SAF Océan), soutenu par l'organisation européenne Eumetsat. L'objectif du SAF Océan est de fournir aux utilisateurs, parmi lesquels les services météorologiques des pays membres d'Eumetsat, les champs à la surface de la mer du vent, des flux radiatifs et de la température de l'eau, calculés à partir des données des</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="da48c64902bb08ff4b132bad466d99ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":70658557,"asset_id":54158789,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/70658557/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="54158789"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="54158789"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 54158789; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=54158789]").text(description); $(".js-view-count[data-work-id=54158789]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 54158789; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='54158789']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 54158789, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "da48c64902bb08ff4b132bad466d99ce" } } $('.js-work-strip[data-work-id=54158789]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":54158789,"title":"Réalisation d'une climatologie mondiale de la température de surface de la mer à échelle fine","translated_title":"","metadata":{"publisher":"INIST-CNRS","grobid_abstract":"Météorologie spatiale Une nouvelle climatologie décadaire de la température de surface de la mer à échelle fine a été réalisée pour améliorer la détection des nuages sur la mer dans l'imagerie satellitaire. 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A global fine-scale sea-surface temperature climatology A new 10-day period fine-scale sea-surface temperature climatology has been built to improve the detection of clouds over the sea in satellite imagery. We used night time AVHRR data from NOAA satellites over the period 1985-1995. This climatology is composed of fields of mean, minimum and maximum temperatures, at a resolution of about 9 km. Fields of number of cases used in the statistics, standard deviation and sea-ice presence were derived to characterise the quality obtained on each grid point. An optimal interpolation technique has been used to compensate for the lack of data in permanently cloudy areas. The small scale adopted resolves fine structures like coastal upwellings. The performance of this new climatology has been evaluated by comparison with existing climatologies and in situ observations. 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A global fine-scale sea-surface temperature climatology A new 10-day period fine-scale sea-surface temperature climatology has been built to improve the detection of clouds over the sea in satellite imagery. We used night time AVHRR data from NOAA satellites over the period 1985-1995. This climatology is composed of fields of mean, minimum and maximum temperatures, at a resolution of about 9 km. Fields of number of cases used in the statistics, standard deviation and sea-ice presence were derived to characterise the quality obtained on each grid point. An optimal interpolation technique has been used to compensate for the lack of data in permanently cloudy areas. The small scale adopted resolves fine structures like coastal upwellings. The performance of this new climatology has been evaluated by comparison with existing climatologies and in situ observations. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="54158632"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/54158632/Evaluation_of_high_resolution_surface_wind_products_at_global_and_regional_scales"><img alt="Research paper thumbnail of Evaluation of high-resolution surface wind products at global and regional scales" class="work-thumbnail" src="https://attachments.academia-assets.com/70658394/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/54158632/Evaluation_of_high_resolution_surface_wind_products_at_global_and_regional_scales">Evaluation of high-resolution surface wind products at global and regional scales</a></div><div class="wp-workCard_item"><span>Journal of Operational Oceanography</span><span>, Jul 31, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">High resolution surface wind fields covering the global ocean, estimated from remotely sensed win...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">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 insitu 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 QuikSCA T 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 QuikS-CA T. No significant dependencies on the QuikSCAT wind speed or on the oceanic region considered are evident. LEAD AUTHOR'S BIOGRAPHY Dr Abderrahim Bentamy is a research engineer at IFRE-MER (Brest) working on active and passive microwave detection, numerical wind. flux and stress field analysis, ocean circulation and process studies. He has worked in the scatterometer callval teams for ESA ERS. NASA NSCAT. and the NASA/NOAA SeaWinds scatterometers, and more recently was involved in ASCA T validation. He is involved in the MERSEA and MyOcean European Union projects that provide, among others. global wind, stress and flux fields. based on a blend of active and passive satellite data products. Vnlt ~rnP) Nn))()()9</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="01ad88527bab952e1db7c2fb1477fab9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":70658394,"asset_id":54158632,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/70658394/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="54158632"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="54158632"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 54158632; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=54158632]").text(description); $(".js-view-count[data-work-id=54158632]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 54158632; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='54158632']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 54158632, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "01ad88527bab952e1db7c2fb1477fab9" } } $('.js-work-strip[data-work-id=54158632]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":54158632,"title":"Evaluation of high-resolution surface wind products at global and regional scales","translated_title":"","metadata":{"grobid_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 insitu 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 QuikSCA T 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 QuikS-CA T. No significant dependencies on the QuikSCAT wind speed or on the oceanic region considered are evident. LEAD AUTHOR'S BIOGRAPHY Dr Abderrahim Bentamy is a research engineer at IFRE-MER (Brest) working on active and passive microwave detection, numerical wind. flux and stress field analysis, ocean circulation and process studies. He has worked in the scatterometer callval teams for ESA ERS. NASA NSCAT. and the NASA/NOAA SeaWinds scatterometers, and more recently was involved in ASCA T validation. He is involved in the MERSEA and MyOcean European Union projects that provide, among others. global wind, stress and flux fields. based on a blend of active and passive satellite data products. 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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 insitu 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 QuikSCA T 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 QuikS-CA T. No significant dependencies on the QuikSCAT wind speed or on the oceanic region considered are evident. LEAD AUTHOR'S BIOGRAPHY Dr Abderrahim Bentamy is a research engineer at IFRE-MER (Brest) working on active and passive microwave detection, numerical wind. flux and stress field analysis, ocean circulation and process studies. He has worked in the scatterometer callval teams for ESA ERS. NASA NSCAT. and the NASA/NOAA SeaWinds scatterometers, and more recently was involved in ASCA T validation. He is involved in the MERSEA and MyOcean European Union projects that provide, among others. global wind, stress and flux fields. based on a blend of active and passive satellite data products. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883216"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883216/Neural_network_wind_retrieval_from_ERS_1_scatterometer_data"><img alt="Research paper thumbnail of Neural network wind retrieval from ERS-1 scatterometer data" class="work-thumbnail" src="https://attachments.academia-assets.com/41152630/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883216/Neural_network_wind_retrieval_from_ERS_1_scatterometer_data">Neural network wind retrieval from ERS-1 scatterometer data</a></div><div class="wp-workCard_item"><span>Neurocomputing</span><span>, 2000</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a neural network methodology to retrieve wind vectors from ERS-1 scatteromete...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a neural network methodology to retrieve wind vectors from ERS-1 scatterometer data. First, a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated wind direction are given. At least 75% of the most probable wind directions are consistent with European Centre for Medium-Range Weather Forecasts winds (at _+20ø). Then the remaining ambiguities are resolved by an adapted PRESCAT method that uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the skill of the method. The good performance is mainly due to the use of a spatial context and to the probabilistic approach adopted to estimate the wind direction. Comparisons with other methods are also presented. The good performance of the neural network method suggests that a selfconsistent wind retrieval from ERS-1 scatterometer is possible.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6e46641d1ef8fc04eb8187c0b70dd080" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41152630,"asset_id":19883216,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41152630/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883216"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883216"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883216; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883216]").text(description); $(".js-view-count[data-work-id=19883216]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883216; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883216']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883216, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6e46641d1ef8fc04eb8187c0b70dd080" } } $('.js-work-strip[data-work-id=19883216]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883216,"title":"Neural network wind retrieval from ERS-1 scatterometer data","translated_title":"","metadata":{"grobid_abstract":"This paper presents a neural network methodology to retrieve wind vectors from ERS-1 scatterometer data. First, a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated wind direction are given. At least 75% of the most probable wind directions are consistent with European Centre for Medium-Range Weather Forecasts winds (at _+20ø). Then the remaining ambiguities are resolved by an adapted PRESCAT method that uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the skill of the method. The good performance is mainly due to the use of a spatial context and to the probabilistic approach adopted to estimate the wind direction. Comparisons with other methods are also presented. 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Richaume","title":"Neural network wind retrieval from ERS-1 scatterometer data"},{"id":12965307,"work_id":19883216,"tagging_user_id":38431410,"tagged_user_id":18008905,"co_author_invite_id":null,"email":"c***n@gmail.com","display_order":7340032,"name":"Carlos Mejia","title":"Neural network wind retrieval from ERS-1 scatterometer data"},{"id":12965321,"work_id":19883216,"tagging_user_id":38431410,"tagged_user_id":52339113,"co_author_invite_id":1093387,"email":"m***n@ens.fr","display_order":7864320,"name":"Marc Crépon","title":"Neural network wind retrieval from ERS-1 scatterometer data"}],"downloadable_attachments":[{"id":41152630,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41152630/thumbnails/1.jpg","file_name":"Neural_Network_Wind_Retrieval_from_ERS-120160114-24366-1jpmcf6.pdf","download_url":"https://www.academia.edu/attachments/41152630/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Neural_network_wind_retrieval_from_ERS_1.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41152630/Neural_Network_Wind_Retrieval_from_ERS-120160114-24366-1jpmcf6-libre.pdf?1452824310=\u0026response-content-disposition=attachment%3B+filename%3DNeural_network_wind_retrieval_from_ERS_1.pdf\u0026Expires=1733999779\u0026Signature=M5yRQDjrKzDEEcckUmeh-D2sJZGk6q~36OOWK2MqIHvrLh5NIwpPdPk9NRpqgVvPV8rWHc1EmsynHQR7buNzTFcpN7pECUD3gYWOAeIFf0Q7N-JjeRfO3~vQhKufigpGAxQ5qxCbhVaHicJhrua6wqmKr5Qfg9behLU83s-i2CwnLoF1s82QC7JRVc-EfYGy0vAZ~6Auxt-TjX9sRVlDDP5Xdlcn3nw408-r-jAqGiyWJpYqD1DCnpgQNs4qjvrXG85B2hh2BwGQPamxPryzZCArz7Bk-sVkuBnnpCkOVlBnMUDoHK3vc7tbzAgc1tCyK4nVWselmpBbSxfSiFIXEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Neural_network_wind_retrieval_from_ERS_1_scatterometer_data","translated_slug":"","page_count":16,"language":"en","content_type":"Work","summary":"This paper presents a neural network methodology to retrieve wind vectors from ERS-1 scatterometer data. First, a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated wind direction are given. At least 75% of the most probable wind directions are consistent with European Centre for Medium-Range Weather Forecasts winds (at _+20ø). Then the remaining ambiguities are resolved by an adapted PRESCAT method that uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the skill of the method. The good performance is mainly due to the use of a spatial context and to the probabilistic approach adopted to estimate the wind direction. Comparisons with other methods are also presented. The good performance of the neural network method suggests that a selfconsistent wind retrieval from ERS-1 scatterometer is possible.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"cjZHSVBoUFVSSStlcWN1dXBrbkQxSEhrL0JGaE92NHVWZzR3VlI2WVpGdz0tLTF5Q016K0pyYmQybGc2SmhjN1BmeWc9PQ==--901f885fe1e5a2fdf151b54182becb80c529c250"},"attachments":[{"id":41152630,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41152630/thumbnails/1.jpg","file_name":"Neural_Network_Wind_Retrieval_from_ERS-120160114-24366-1jpmcf6.pdf","download_url":"https://www.academia.edu/attachments/41152630/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Neural_network_wind_retrieval_from_ERS_1.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41152630/Neural_Network_Wind_Retrieval_from_ERS-120160114-24366-1jpmcf6-libre.pdf?1452824310=\u0026response-content-disposition=attachment%3B+filename%3DNeural_network_wind_retrieval_from_ERS_1.pdf\u0026Expires=1733999779\u0026Signature=M5yRQDjrKzDEEcckUmeh-D2sJZGk6q~36OOWK2MqIHvrLh5NIwpPdPk9NRpqgVvPV8rWHc1EmsynHQR7buNzTFcpN7pECUD3gYWOAeIFf0Q7N-JjeRfO3~vQhKufigpGAxQ5qxCbhVaHicJhrua6wqmKr5Qfg9behLU83s-i2CwnLoF1s82QC7JRVc-EfYGy0vAZ~6Auxt-TjX9sRVlDDP5Xdlcn3nw408-r-jAqGiyWJpYqD1DCnpgQNs4qjvrXG85B2hh2BwGQPamxPryzZCArz7Bk-sVkuBnnpCkOVlBnMUDoHK3vc7tbzAgc1tCyK4nVWselmpBbSxfSiFIXEw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing"},{"id":26066,"name":"Neural Network","url":"https://www.academia.edu/Documents/in/Neural_Network"},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary"},{"id":306573,"name":"Neurocomputing","url":"https://www.academia.edu/Documents/in/Neurocomputing"},{"id":314103,"name":"Inverse Modeling","url":"https://www.academia.edu/Documents/in/Inverse_Modeling"},{"id":411513,"name":"Geophysical","url":"https://www.academia.edu/Documents/in/Geophysical"},{"id":703835,"name":"Statistical Test","url":"https://www.academia.edu/Documents/in/Statistical_Test"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883215"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883215/OSI_SAF_operational_NPP_VIIRS_sea_surface_temperature_chain"><img alt="Research paper thumbnail of OSI-SAF operational NPP/VIIRS sea surface temperature chain" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883215/OSI_SAF_operational_NPP_VIIRS_sea_surface_temperature_chain">OSI-SAF operational NPP/VIIRS sea surface temperature chain</a></div><div class="wp-workCard_item"><span>Ocean Sensing and Monitoring V</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Pol...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) have been acquired at Centre de Météorologie Spatiale (CMS) in Lannion (Brittany) in direct readout mode since April 2012. CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883215"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883215"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883215; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883215]").text(description); $(".js-view-count[data-work-id=19883215]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883215; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883215']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883215, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=19883215]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883215,"title":"OSI-SAF operational NPP/VIIRS sea surface temperature chain","translated_title":"","metadata":{"abstract":"ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) have been acquired at Centre de Météorologie Spatiale (CMS) in Lannion (Brittany) in direct readout mode since April 2012. CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.","publication_date":{"day":null,"month":null,"year":2013,"errors":{}},"publication_name":"Ocean Sensing and Monitoring V"},"translated_abstract":"ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) have been acquired at Centre de Météorologie Spatiale (CMS) in Lannion (Brittany) in direct readout mode since April 2012. CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.","internal_url":"https://www.academia.edu/19883215/OSI_SAF_operational_NPP_VIIRS_sea_surface_temperature_chain","translated_internal_url":"","created_at":"2015-12-29T02:57:42.819-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":38431410,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"OSI_SAF_operational_NPP_VIIRS_sea_surface_temperature_chain","translated_slug":"","page_count":null,"language":"en","content_type":"Work","summary":"ABSTRACT Data of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) have been acquired at Centre de Météorologie Spatiale (CMS) in Lannion (Brittany) in direct readout mode since April 2012. CMS is committed to produce sea surface temperature (SST) products from VIIRS data twice a day over an area covering North-East Atlantic and the Mediterranean Sea in the framework of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF). A cloud mask has been developed and cloud mask control techniques have been implemented. SST algorithms have been defined, as well as quality level attribution rules. Since mid October 2012 a VIIRS SST chain, similar to that used for processing METOP AVHRR has been run in a preoperational mode. The corresponding bias and standard deviation against drifting buoy measurements (mid October 2012 to mid March 2013) are -0.05 and 0.37 K for nighttime and -0.13 and 0.46 K for daytime, respectively. VIIRS derived SST production is expected operational by mid 2013. The OSI-SAF VIIRS derived SST products are compliant with the Group for High Resolution SST (GHRSST) GDS V2.0 format.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"ZzRZYWZiVVJDeGFJbUVYWmg5NnRqQ3dsVFRDVXdEdXVZajJDcEZKTXFwST0tLUVNUk5OLzRYZGwxV0VGcTQwcnN6TlE9PQ==--e34b8d1b08203f0213ff4e2dfafec0d8174cb199"},"attachments":[],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883214"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883214/Six_years_of_OSI_SAF_METOP_A_AVHRR_sea_surface_temperature"><img alt="Research paper thumbnail of Six years of OSI-SAF METOP-A AVHRR sea surface temperature" class="work-thumbnail" src="https://attachments.academia-assets.com/41152512/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883214/Six_years_of_OSI_SAF_METOP_A_AVHRR_sea_surface_temperature">Six years of OSI-SAF METOP-A AVHRR sea surface temperature</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The Ocean and Sea Ice Satellite Application Facility (OSI SAF) has been producing full resolution...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The Ocean and Sea Ice Satellite Application Facility (OSI SAF) has been producing full resolution global Sea Surface Temperature (SST) from the METOP-A Advanced Very High Resolution Radiometer (AVHRR) since July 2007. The SST operational processing and the validation scheme have remained unchanged for more than 6 years. The global validation results against measurements are stable over time. Night-time METOP-A SSTs differ from drifting buoy SSTs by −0.05 K in average with a standard deviation of 0.44 K and the daytime values are respectively 0.09 K and 0.56 K. Seasonal statistics have been calculated on a global regular 5-degree grid for a 6-year period to review the main biases and their characteristics. There is evidence of regional and seasonal biases, indeed the multispectral regression algorithms are known (to a various degree depending upon specific implementation) to have limitations in handling the variety of atmospheric absorption conditions encountered over the global ocean. This problem has been solved for the OSI SAF geostationary SST chain by adopting a Numerical Weather Prediction (NWP) profile based correction method. The same approach has been tested on a prototype chain ingesting METOP-A data and gives encouraging results. It will be used in the new polar orbiter chain under development at OSI SAF, that will process METOP-B data. An application example of METOP-A SST time series is given by analyzing the inter-annual variability of Arctic Ocean SST in relation with the ice coverage variability in September. The METOP-A time series gives consistent results when compared to other observations or model outputs.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f79c05b43e3f0ae90338f3714fe473d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41152512,"asset_id":19883214,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41152512/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883214"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883214"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883214; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883214]").text(description); $(".js-view-count[data-work-id=19883214]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883214; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883214']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883214, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f79c05b43e3f0ae90338f3714fe473d6" } } $('.js-work-strip[data-work-id=19883214]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883214,"title":"Six years of OSI-SAF METOP-A AVHRR sea surface temperature","translated_title":"","metadata":{"grobid_abstract":"The Ocean and Sea Ice Satellite Application Facility (OSI SAF) has been producing full resolution global Sea Surface Temperature (SST) from the METOP-A Advanced Very High Resolution Radiometer (AVHRR) since July 2007. The SST operational processing and the validation scheme have remained unchanged for more than 6 years. The global validation results against measurements are stable over time. Night-time METOP-A SSTs differ from drifting buoy SSTs by −0.05 K in average with a standard deviation of 0.44 K and the daytime values are respectively 0.09 K and 0.56 K. Seasonal statistics have been calculated on a global regular 5-degree grid for a 6-year period to review the main biases and their characteristics. There is evidence of regional and seasonal biases, indeed the multispectral regression algorithms are known (to a various degree depending upon specific implementation) to have limitations in handling the variety of atmospheric absorption conditions encountered over the global ocean. This problem has been solved for the OSI SAF geostationary SST chain by adopting a Numerical Weather Prediction (NWP) profile based correction method. The same approach has been tested on a prototype chain ingesting METOP-A data and gives encouraging results. It will be used in the new polar orbiter chain under development at OSI SAF, that will process METOP-B data. An application example of METOP-A SST time series is given by analyzing the inter-annual variability of Arctic Ocean SST in relation with the ice coverage variability in September. 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The SST operational processing and the validation scheme have remained unchanged for more than 6 years. The global validation results against measurements are stable over time. Night-time METOP-A SSTs differ from drifting buoy SSTs by −0.05 K in average with a standard deviation of 0.44 K and the daytime values are respectively 0.09 K and 0.56 K. Seasonal statistics have been calculated on a global regular 5-degree grid for a 6-year period to review the main biases and their characteristics. There is evidence of regional and seasonal biases, indeed the multispectral regression algorithms are known (to a various degree depending upon specific implementation) to have limitations in handling the variety of atmospheric absorption conditions encountered over the global ocean. This problem has been solved for the OSI SAF geostationary SST chain by adopting a Numerical Weather Prediction (NWP) profile based correction method. The same approach has been tested on a prototype chain ingesting METOP-A data and gives encouraging results. It will be used in the new polar orbiter chain under development at OSI SAF, that will process METOP-B data. An application example of METOP-A SST time series is given by analyzing the inter-annual variability of Arctic Ocean SST in relation with the ice coverage variability in September. The METOP-A time series gives consistent results when compared to other observations or model outputs.","owner":{"id":38431410,"first_name":"Herve","middle_initials":null,"last_name":"Roquet","page_name":"HerveRoquet","domain_name":"independent","created_at":"2015-11-16T01:58:41.006-08:00","display_name":"Herve Roquet","url":"https://independent.academia.edu/HerveRoquet","email":"dlkyejlPdVgrR0t5ZTU0MTNaT1kxbGJwQW1wb2hyTkZSK0ZjVld5WGh6az0tLXZuemk2YVFkK1NPc1FJTk1hb3lPV3c9PQ==--bbeb04c9dcb09023e16a4035101fd20a7e899c67"},"attachments":[{"id":41152512,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/41152512/thumbnails/1.jpg","file_name":"Six_years_of_OSI-SAF_METOP-A_AVHRR_sea_s20160114-24366-17sgyxq.pdf","download_url":"https://www.academia.edu/attachments/41152512/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Six_years_of_OSI_SAF_METOP_A_AVHRR_sea_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/41152512/Six_years_of_OSI-SAF_METOP-A_AVHRR_sea_s20160114-24366-17sgyxq-libre.pdf?1452823979=\u0026response-content-disposition=attachment%3B+filename%3DSix_years_of_OSI_SAF_METOP_A_AVHRR_sea_s.pdf\u0026Expires=1733999779\u0026Signature=aA-tmH93DpCmyEGppmIRNtj1-09q4YnURAK0LwnFlYY01Nq03b1nztJmZ3TFNL6qRYPegUrgjyCv-7iFrcbP~EbMUAmpgF7eYVj1~Jo3~cv0NNG4YcE4W04xYqXedTw2OAAEPvFFMPlspS3tZyu2TbE1CQfs4WgzTWmy751ojTQ~I2OLZNyEfPy2SNwADlcAWub50Fvdq7MX7qdfd~kUV3ZINokYgQ2IbfVd7VFFblk3fqiEDYL9Shs-jwTWcTt0sVquziBpdv0NoGZOHegxxpHtBhhx8GN1am9Vuf-xKYQLWeYFSyABFl0FboY-NpxCeXOlJ0oTA11g-n1qQi8EcA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":162010,"name":"Geomatic Engineering","url":"https://www.academia.edu/Documents/in/Geomatic_Engineering"}],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883213"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883213/Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea"><img alt="Research paper thumbnail of Assessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea" class="work-thumbnail" src="https://attachments.academia-assets.com/42112521/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883213/Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea">Assessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiomet...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (&amp;lt;5 days) and higher spatial smoothing (&amp;gt;10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="83e7ff869c79836db1c0c7de06c51620" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":42112521,"asset_id":19883213,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/42112521/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883213"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883213"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883213; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=19883213]").text(description); $(".js-view-count[data-work-id=19883213]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 19883213; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='19883213']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 19883213, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "83e7ff869c79836db1c0c7de06c51620" } } $('.js-work-strip[data-work-id=19883213]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":19883213,"title":"Assessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea","translated_title":"","metadata":{"abstract":"ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (\u0026amp;lt;5 days) and higher spatial smoothing (\u0026amp;gt;10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results.","publication_date":{"day":null,"month":null,"year":2014,"errors":{}},"publication_name":"Remote Sensing of Environment"},"translated_abstract":"ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (\u0026amp;lt;5 days) and higher spatial smoothing (\u0026amp;gt;10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results.","internal_url":"https://www.academia.edu/19883213/Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea","translated_internal_url":"","created_at":"2015-12-29T02:57:42.342-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":38431410,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":42112521,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/42112521/thumbnails/1.jpg","file_name":"Assessing_the_impact_of_brightness_tempe20160204-19318-yp5my.pdf","download_url":"https://www.academia.edu/attachments/42112521/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Assessing_the_impact_of_brightness_tempe.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/42112521/Assessing_the_impact_of_brightness_tempe20160204-19318-yp5my-libre.pdf?1454653429=\u0026response-content-disposition=attachment%3B+filename%3DAssessing_the_impact_of_brightness_tempe.pdf\u0026Expires=1733999779\u0026Signature=O0EueumgSI~7vtOlJfD6Ag7sCi5vz~riGlAVnUhCJv4JLEhfJUEmqHFdlIftAC3wmPa8yBttCj0TAudIBCIk5wwr~snbtee1LlP8mBU62KUVMika-1W7bFYxPnZgs2Njh8ztYzcgHKQo801j7t7dl0mo9q16vuM8hN9IrFl7Bc0QDW~zUUKDEWDP1xBp84n4cLKc4MtAPINvw~baum6L5hOoYFS-AtLDDf38HJSSqrW41a5M9s4VjHuF54WHE1M-U-0AHFjXUk0axp~3CIBNdgIhIKbS6V-0visTLEdY~-K8OOcWrH-L2J7Zw0AHGN3IjwAPvh1JIyfB-Pbgq5zNIw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Assessing_the_impact_of_brightness_temperature_simulation_adjustment_conditions_in_correcting_Metop_A_SST_over_the_Mediterranean_Sea","translated_slug":"","page_count":20,"language":"en","content_type":"Work","summary":"ABSTRACT Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution - 15 levels - NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution - 91 levels - and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (\u0026amp;lt;5 days) and higher spatial smoothing (\u0026amp;gt;10 deg) for nighttime. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883212"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883212/Night_time_detection_of_Saharan_dust_using_infrared_window_channels_Application_to_NPP_VIIRS"><img alt="Research paper thumbnail of Night time detection of Saharan dust using infrared window channels: Application to NPP/VIIRS" class="work-thumbnail" src="https://attachments.academia-assets.com/41152003/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883212/Night_time_detection_of_Saharan_dust_using_infrared_window_channels_Application_to_NPP_VIIRS">Night time detection of Saharan dust using infrared window channels: Application to NPP/VIIRS</a></div><div class="wp-workCard_item"><span>Remote Sensing of Environment</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="90eb3553eba9b748afa8e08d96bd6377" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":41152003,"asset_id":19883212,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/41152003/download_file?st=MTczNDAwOTE1OCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="19883212"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="19883212"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 19883212; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="19883211"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/19883211/Determination_of_ocean_surface_heat_fluxes_by_a_variational_method"><img alt="Research paper thumbnail of Determination of ocean surface heat fluxes by a variational method" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/19883211/Determination_of_ocean_surface_heat_fluxes_by_a_variational_method">Determination of ocean surface heat fluxes by a variational method</a></div><div class="wp-workCard_item"><span>Journal of Geophysical Research</span><span>, 1993</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A new technique of determination of the ``nonsolar&amp;amp;amp;amp;amp;#39;&amp;amp;amp;amp;amp;#...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">A new technique of determination of the ``nonsolar&amp;amp;amp;amp;amp;#39;&amp;amp;amp;amp;amp;#39; heat flux (sum of the latent, sensible, and net infrared fluxes) at the ocean surface is proposed. It applies when oceanic advection remains weak and thus relies on a one-dimensional modeling approach. It is based on a variational data assimilation scheme using the adjoint equation formalism. 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