{"id":3139,"date":"2024-02-15T09:30:00","date_gmt":"2024-02-15T08:30:00","guid":{"rendered":"https:\/\/frogcast.com\/?p=3139"},"modified":"2026-01-27T11:40:48","modified_gmt":"2026-01-27T10:40:48","slug":"optimiser-precision-previsions-meteo-avec-modeles-multiples","status":"publish","type":"post","link":"https:\/\/frogcast.com\/fr\/blog\/technology\/optimizing-weather-forecasts-using-models-blending\/","title":{"rendered":"Optimiser la pr\u00e9cision des pr\u00e9visions m\u00e9t\u00e9o en combinant plusieurs mod\u00e8les : analyse comparative des performances de FROGCAST"},"content":{"rendered":"<p>Il est bien connu que la nature chaotique de l'atmosph\u00e8re rend la pr\u00e9vision de son \u00e9volution particuli\u00e8rement d\u00e9licate. L'utilisation de sources d'information multiples permet de relever ce d\u00e9fi en <strong><a href=\"https:\/\/frogcast.com\/fr\/blog\/meteorologie\/how-are-weather-forecasts-generated\/\" target=\"_blank\" rel=\"noreferrer noopener\">quantifiant et en r\u00e9duisant l'incertitude<\/a><\/strong>. <strong>Mais alors, quelles sont r\u00e9ellement les performances de FROGCAST ?<\/strong><\/p>\n\n\n\n<p>Cet article \u00e9value les pr\u00e9visions de FROGCAST face \u00e0 7 mod\u00e8les individuels de Pr\u00e9vision Num\u00e9rique du Temps (PNT) pour 6 variables atmosph\u00e9riques sur une p\u00e9riode de 3 mois.<\/p>\n\n\n\n<p class=\"has-white-color has-text-color has-background has-link-color wp-elements-32d09651b54d3c65062c8c38bb3cbb4b\" style=\"background-color:#1f807d\">Pour en savoir plus sur la g\u00e9n\u00e9ration de nos pr\u00e9visions, <strong><a href=\"https:\/\/frogcast.com\/fr\/blog\/meteorologie\/how-are-weather-forecasts-generated\/\">consultez cette page<\/a><\/strong>. Vous pouvez \u00e9galement explorer tous les d\u00e9tails techniques et les fonctionnalit\u00e9s de nos pr\u00e9dictions <strong><a href=\"https:\/\/frogcast.com\/fr\/nos-previsions-meteorologiques\/\">ici<\/a><\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/a-comparative-analysis-of-frogcast-s-performances-slider1-1024x576.webp\" alt=\"Un analyste consulte plusieurs \u00e9crans num\u00e9riques affichant des donn\u00e9es complexes, illustrant l&#039;analyse comparative des mod\u00e8les m\u00e9t\u00e9orologiques.\" class=\"wp-image-3051\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/a-comparative-analysis-of-frogcast-s-performances-slider1-1024x576.webp 1024w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/a-comparative-analysis-of-frogcast-s-performances-slider1-300x169.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/a-comparative-analysis-of-frogcast-s-performances-slider1-768x432.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/a-comparative-analysis-of-frogcast-s-performances-slider1-1536x864.webp 1536w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/a-comparative-analysis-of-frogcast-s-performances-slider1-18x10.webp 18w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/a-comparative-analysis-of-frogcast-s-performances-slider1.webp 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Nous analysons comment la combinaison de plusieurs mod\u00e8les de pr\u00e9vision num\u00e9rique du temps (PNT) surpasse les sources individuelles sur une p\u00e9riode d'\u00e9valuation de 3 mois.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Donn\u00e9es et dispositif d'\u00e9valuation<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">P\u00e9riode et zone d'\u00e9valuation<\/h3>\n\n\n\n<p>Pour obtenir des r\u00e9sultats pertinents et robustes, nous avons \u00e9valu\u00e9 FROGCAST du 1er octobre au 31 d\u00e9cembre 2023. La zone couvre la majeure partie de l'Europe (Scandinavie exclue) et une partie du Maghreb. Elle s'\u00e9tend de 30\u00b0N \u00e0 60\u00b0N de latitude et de 15\u00b0O \u00e0 30\u00b0E de longitude.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Variables m\u00e9t\u00e9orologiques s\u00e9lectionn\u00e9es<\/h3>\n\n\n\n<p>Nous avons s\u00e9lectionn\u00e9 6 variables m\u00e9t\u00e9orologiques classiques pour obtenir une vision claire et large des performances de FROGCAST :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Temp\u00e9rature \u00e0 2 m\u00e8tres (\u00b0C)<\/li>\n\n\n\n<li>Vitesse du vent \u00e0 10 m\u00e8tres (m\/s)<\/li>\n\n\n\n<li>Taux de pr\u00e9cipitation par heure (mm\/h)<\/li>\n\n\n\n<li>Humidit\u00e9 relative \u00e0 2 m\u00e8tres (%)<\/li>\n\n\n\n<li>Pression au niveau de la mer (hPa)<\/li>\n\n\n\n<li>Irradiation horizontale globale (W\/m\u00b2)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">M\u00e9trique d'\u00e9valuation<\/h3>\n\n\n\n<p>Nous avons utilis\u00e9 l'erreur absolue moyenne (MAE) calcul\u00e9e \u00e0 l'aide des s\u00e9ries temporelles horaires pour mesurer la pr\u00e9cision des pr\u00e9visions.<\/p>\n\n\n\n<div class=\"wp-block-math\"><math display=\"block\"><semantics><mrow><mi>M<\/mi><mi>A<\/mi><mi>E<\/mi><mo>=<\/mo><mfrac><mn>1<\/mn><mi>n<\/mi><\/mfrac><mrow><munderover><mo movablelimits=\"false\">\u2211<\/mo><mrow><mi>i<\/mi><mo>=<\/mo><mn>1<\/mn><\/mrow><mi>n<\/mi><\/munderover><\/mrow><mrow><mo fence=\"true\" form=\"prefix\">|<\/mo><mi>F<\/mi><mi>o<\/mi><mi>r<\/mi><mi>e<\/mi><mi>c<\/mi><mi>a<\/mi><mi>s<\/mi><msubsup><mi>t<\/mi><mi>i<\/mi><mrow><mn>1<\/mn><mi>h<\/mi><\/mrow><\/msubsup><mo>\u2212<\/mo><mi>O<\/mi><mi>b<\/mi><mi>s<\/mi><mi>e<\/mi><mi>r<\/mi><mi>v<\/mi><mi>a<\/mi><mi>t<\/mi><mi>i<\/mi><mi>o<\/mi><msubsup><mi>n<\/mi><mi>i<\/mi><mrow><mn>1<\/mn><mi>h<\/mi><\/mrow><\/msubsup><mo fence=\"true\" form=\"postfix\">|<\/mo><\/mrow><\/mrow><annotation encoding=\"application\/x-tex\">MAE = \\frac{1}{n} \\sum_{i=1}^{n} \\left| Forecast_i^{1h} &#8211; Observation_i^{1h} \\right|<\/annotation><\/semantics><\/math><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Gridded reference data: ERA5 Reanalysis<\/h2>\n\n\n\n<p><strong><a href=\"https:\/\/confluence.ecmwf.int\/display\/CKB\/ERA5%3A+data+documentation\" target=\"_blank\" rel=\"noreferrer noopener\">Les donn\u00e9es de r\u00e9analyse ERA5 du ECMWF<\/a><\/strong> ont \u00e9t\u00e9 utilis\u00e9es comme pseudo-observations maill\u00e9es pour \u00e9valuer les diff\u00e9rentes pr\u00e9visions. Ce jeu de donn\u00e9es fournit des informations au pas de temps horaire sur une grille spatiale de 0,25\u00b0. Il combine les donn\u00e9es de mod\u00e8le avec les observations pour g\u00e9n\u00e9rer une nouvelle meilleure estimation de l'\u00e9tat de l'atmosph\u00e8re. Plus d'informations sont disponibles sur <strong><a href=\"https:\/\/cds.climate.copernicus.eu\/cdsapp#!\/dataset\/reanalysis-era5-single-levels?tab=overview\" target=\"_blank\" rel=\"noreferrer noopener\">le site du Copernicus Climate Data Store<\/a><\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">FROGCAST vs mod\u00e8les PNT (ou NWP)<\/h2>\n\n\n\n<p>Nous avons compar\u00e9 les pr\u00e9visions de FROGCAST \u00e0 7 <a href=\"https:\/\/wikipedia.org\/wiki\/Numerical_weather_prediction\">mod\u00e8les PNT<\/a>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>ARPEGE-EU et AROME (M\u00e9t\u00e9o-France)<\/li>\n\n\n\n<li>ICON-EU et ICON-D2 (DWD)<\/li>\n\n\n\n<li>GFS (NCEP)<\/li>\n\n\n\n<li>GDPS (CMC)<\/li>\n\n\n\n<li>IFS-HRES (ECMWF)<\/li>\n<\/ul>\n\n\n\n<p>Les pr\u00e9visions de chaque mod\u00e8le ont \u00e9t\u00e9 prises une fois par jour (run de 0 UTC) pour les 24 premi\u00e8res \u00e9ch\u00e9ances horaires. Afin d'assurer une comparaison \u00e9quitable, tous les mod\u00e8les sont reprojet\u00e9s et r\u00e9\u00e9chantillonn\u00e9s sur la grille ERA5 de 0,25\u00b0.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Performances spatiales selon les variables<\/h3>\n\n\n\n<p>Les performances spatiales pour la temp\u00e9rature \u00e0 2m sont pr\u00e9sent\u00e9es sur la Figure 1 en valeur absolue (a) et en diff\u00e9rence absolue entre les mod\u00e8les individuels et FROGCAST (b). Pour cette variable, les meilleurs scores sont obtenus par les mod\u00e8les ICON, ICON-D2 et IFS. Cependant, pour chaque point de grille du domaine, <strong>FROGCAST surpasse tous les mod\u00e8les individuels et r\u00e9duit significativement la MAE, notamment sur les zones continentales<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"410\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/mae-temperature-maps-1024x410.webp\" alt=\"Cartes thermiques comparatives montrant l&#039;erreur absolue moyenne (MAE) pour les pr\u00e9visions de temp\u00e9rature \u00e0 2 m \u00e0 travers l&#039;Europe pour FROGCAST et 7 autres mod\u00e8les mondiaux de pr\u00e9vision num\u00e9rique du temps.\" class=\"wp-image-3140\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/mae-temperature-maps-1024x410.webp 1024w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/mae-temperature-maps-300x120.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/mae-temperature-maps-768x307.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/mae-temperature-maps-1536x615.webp 1536w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/mae-temperature-maps-18x7.webp 18w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/mae-temperature-maps.webp 1612w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"410\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/difference-mae-temperature-maps-1024x410.webp\" alt=\"Cartes thermiques affichant la diff\u00e9rence spatiale de l&#039;erreur absolue moyenne (MAE) entre FROGCAST et les mod\u00e8les m\u00e9t\u00e9orologiques individuels, mettant en \u00e9vidence le gain de pr\u00e9cision de la m\u00e9thode de fusion.\" class=\"wp-image-3145\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/difference-mae-temperature-maps-1024x410.webp 1024w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/difference-mae-temperature-maps-300x120.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/difference-mae-temperature-maps-768x308.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/difference-mae-temperature-maps-1536x615.webp 1536w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/difference-mae-temperature-maps-18x7.webp 18w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/difference-mae-temperature-maps.webp 1613w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Figure 1 \u2013 Cartes de MAE de temp\u00e9rature pour chaque mod\u00e8le individuel en valeur absolue et relativement \u00e0 FROGCAST<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">R\u00e9sultats d\u00e9taill\u00e9s pour toutes les variables<\/h3>\n\n\n\n<p>Les r\u00e9sultats concernant les 6 variables sont rassembl\u00e9s dans les tableaux 1 \u00e0 4. Les scores relatifs \u00e0 ARPEGE, ICON, GFS et GDPS sont moyenn\u00e9s sur le m\u00eame domaine complet et peuvent \u00eatre compar\u00e9s (Tableau 1). Pour les trois mod\u00e8les restants, les scores correspondent \u00e0 leur domaine r\u00e9gional sp\u00e9cifique et doivent \u00eatre consid\u00e9r\u00e9s s\u00e9par\u00e9ment.<br><br>Pour tous les param\u00e8tres atmosph\u00e9riques, FROGCAST surpasse syst\u00e9matiquement chaque mod\u00e8le PNT individuel. Les am\u00e9liorations sont notables :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Temp\u00e9rature \u00e0 2m am\u00e9lior\u00e9e de 0,2 \u00e0 0,5\u00b0C (25% \u00e0 40%)<\/li>\n\n\n\n<li>Vitesse du vent \u00e0 10m am\u00e9lior\u00e9e de 0,29 \u00e0 0,41 m\/s (24% \u00e0 39%)<\/li>\n<\/ul>\n\n\n\n<p>Ces diff\u00e9rences varient beaucoup spatialement avec les meilleures performances de FROGCAST sur les zones continentales (non illustr\u00e9).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"806\" height=\"366\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-1-mae-scores-on-full-domain1.webp\" alt=\"Tableau de donn\u00e9es comparant les scores d&#039;erreur absolue moyenne (MAE) pour FROGCAST par rapport \u00e0 ARPEGE-EU, ICON-EU, GFS et GDPS sur 6 variables m\u00e9t\u00e9orologiques sur l&#039;ensemble du domaine.\" class=\"wp-image-3144\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-1-mae-scores-on-full-domain1.webp 806w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-1-mae-scores-on-full-domain1-300x136.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-1-mae-scores-on-full-domain1-768x349.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-1-mae-scores-on-full-domain1-18x8.webp 18w\" sizes=\"(max-width: 806px) 100vw, 806px\" \/><figcaption class=\"wp-element-caption\">Tableau 1 \u2013 Sur l'ensemble du domaine spatial, FROGCAST surpasse tous les mod\u00e8les de pr\u00e9vision num\u00e9rique du temps (ARPEGE, ICON, GFS, GDPS) pour toutes les variables test\u00e9es, y compris le vent et l'irradiance.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"492\" height=\"377\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-2-mae-scores-on-arome-s-domain1.webp\" alt=\"Tableau comparatif des erreurs de pr\u00e9vision entre FROGCAST et le mod\u00e8le AROME de M\u00e9t\u00e9o-France pour 6 variables atmosph\u00e9riques.\" class=\"wp-image-3143\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-2-mae-scores-on-arome-s-domain1.webp 492w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-2-mae-scores-on-arome-s-domain1-300x230.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-2-mae-scores-on-arome-s-domain1-16x12.webp 16w\" sizes=\"(max-width: 492px) 100vw, 492px\" \/><figcaption class=\"wp-element-caption\">Tableau 2 \u2013 M\u00eame face \u00e0 AROME, la r\u00e9f\u00e9rence haute r\u00e9solution pour la France, la technologie de fusion de FROGCAST offre des taux d'erreur plus faibles, notamment pour la Temp\u00e9rature (0,55\u00b0C vs 0,96\u00b0C) et l'IHG.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"484\" height=\"383\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-3-mae-scores-on-icon-d2-s-domain1.webp\" alt=\"Tableau comparatif des erreurs de pr\u00e9vision entre FROGCAST et le mod\u00e8le ICON-D2 du DWD pour 6 variables atmosph\u00e9riques.\" class=\"wp-image-3142\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-3-mae-scores-on-icon-d2-s-domain1.webp 484w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-3-mae-scores-on-icon-d2-s-domain1-300x237.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-3-mae-scores-on-icon-d2-s-domain1-15x12.webp 15w\" sizes=\"(max-width: 484px) 100vw, 484px\" \/><figcaption class=\"wp-element-caption\">Tableau 3 \u2013 Dans le domaine ICON-D2 (Allemagne), FROGCAST maintient son avance, d\u00e9montrant que notre approche multi-mod\u00e8les reste robuste face aux mod\u00e8les r\u00e9gionaux sp\u00e9cialis\u00e9s.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"466\" height=\"127\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-4-mae-scores-on-ifs-s-domain1.webp\" alt=\"Tableau comparatif montrant la pr\u00e9cision des pr\u00e9visions de temp\u00e9rature de FROGCAST par rapport au mod\u00e8le ECMWF IFS-HRES.\" class=\"wp-image-3141\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-4-mae-scores-on-ifs-s-domain1.webp 466w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-4-mae-scores-on-ifs-s-domain1-300x82.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/table-4-mae-scores-on-ifs-s-domain1-18x5.webp 18w\" sizes=\"(max-width: 466px) 100vw, 466px\" \/><figcaption class=\"wp-element-caption\">Tableau 4 \u2013 FROGCAST obtient une erreur absolue moyenne plus faible (0,56\u00b0C) compar\u00e9 au renomm\u00e9 mod\u00e8le CEPMMT IFS-HRES (0,75\u00b0C) pour les pr\u00e9visions de temp\u00e9rature.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">FROGCAST ouvre la voie vers des pr\u00e9visions m\u00e9t\u00e9o pr\u00e9cises<\/h2>\n\n\n\n<p>FROGCAST fournit des <a href=\"https:\/\/frogcast.com\/fr\/nos-previsions-meteorologiques\/\">pr\u00e9visions m\u00e9t\u00e9orologiques probabilistes<\/a> haute performance pour n'importe quel endroit dans le monde et une large gamme de param\u00e8tres atmosph\u00e9riques. Cette \u00e9tude met en \u00e9vidence la pertinence de ses r\u00e9sultats et qu'il surpasse tous les mod\u00e8les PNT individuels. Les algorithmes de FROGCAST sont optimis\u00e9s pour chaque localisation et variable m\u00e9t\u00e9orologique, garantissant ainsi les meilleures pr\u00e9visions pour tous vos besoins sp\u00e9cifiques.<\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>FROGCAST surpasse les mod\u00e8les num\u00e9riques de pr\u00e9vision m\u00e9t\u00e9orologique individuels sur six variables m\u00e9t\u00e9orologiques, r\u00e9duisant les erreurs de pr\u00e9vision jusqu'\u00e0 40 % sur une \u00e9valuation de 3 mois \u00e0 l'\u00e9chelle europ\u00e9enne.<\/p>","protected":false},"author":2,"featured_media":3051,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[27],"tags":[],"class_list":["post-3139","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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