{"id":3026,"date":"2023-09-28T09:00:00","date_gmt":"2023-09-28T07:00:00","guid":{"rendered":"https:\/\/frogcast.com\/?p=3026"},"modified":"2026-02-13T14:23:52","modified_gmt":"2026-02-13T13:23:52","slug":"intervalles-de-confiance","status":"publish","type":"post","link":"https:\/\/frogcast.com\/fr\/blog\/technology\/confidence-intervals\/","title":{"rendered":"Comprendre les intervalles de confiance en pr\u00e9visions m\u00e9t\u00e9orologiques"},"content":{"rendered":"<p>Les intervalles de confiance sont une source d'information essentielle en pr\u00e9vision m\u00e9t\u00e9orologique. Ils permettent de quantifier objectivement l'incertitude associ\u00e9e \u00e0 une pr\u00e9vision.<\/p>\n\n\n\n<p>Nous d\u00e9finissons ici les intervalles de confiance, d\u00e9crivons comment nous les g\u00e9n\u00e9rons et illustrons leurs utilisations possibles. Avant d'aller plus loin, nous vous recommandons de lire notre <a href=\"https:\/\/frogcast.com\/fr\/blog\/meteorologie\/how-are-weather-forecasts-generated\/\" target=\"_blank\" rel=\"noreferrer noopener\">article sur la pr\u00e9vision m\u00e9t\u00e9orologique<\/a>. Certains concepts et notions seront r\u00e9p\u00e9t\u00e9s ici sans \u00eatre r\u00e9expliqu\u00e9s.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Pr\u00e9visions d\u00e9terministes et probabilistes<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Qu'est-ce qu'une pr\u00e9vision d\u00e9terministe ?<\/h3>\n\n\n\n<p>L'adjectif <strong>d\u00e9terministe fait r\u00e9f\u00e9rence \u00e0 une pr\u00e9vision m\u00e9t\u00e9orologique pour laquelle un seul sc\u00e9nario est disponible<\/strong>. Cela signifie une seule simulation issue d'un seul mod\u00e8le de pr\u00e9vision num\u00e9rique du temps. Dans ce cas, il est impossible de d\u00e9terminer la confiance dans la pr\u00e9vision.<\/p>\n\n\n\n<p>Cette situation peut potentiellement conduire \u00e0 de grandes erreurs, en particulier pour des \u00e9ch\u00e9ances de pr\u00e9vision lointaines ou pour des ph\u00e9nom\u00e8nes m\u00e9t\u00e9orologiques dont l'occurrence et la localisation sont particuli\u00e8rement difficiles \u00e0 pr\u00e9voir.<\/p>\n\n\n\n<p>Pour surmonter cette limitation, il est possible <strong>d'utiliser la pr\u00e9vision d'ensemble<\/strong>, qui propose tout un ensemble de sc\u00e9narios et nous permet de d\u00e9duire l'\u00e9volution la plus pr\u00e9cise de l'\u00e9tat atmosph\u00e9rique.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">G\u00e9n\u00e9rer des sc\u00e9narios de pr\u00e9vision<\/h3>\n\n\n\n<p>Il existe plusieurs fa\u00e7ons de g\u00e9n\u00e9rer cet ensemble de sc\u00e9narios :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Multi-mod\u00e8les<\/strong>: Chaque Service M\u00e9t\u00e9orologique et Hydrologique National (SMHN) dans le monde poss\u00e8de son propre mod\u00e8le de pr\u00e9vision num\u00e9rique du temps. Ils fournissent tous des pr\u00e9visions diff\u00e9rentes, qui constituent autant de sc\u00e9narios possibles.<\/li>\n\n\n\n<li><strong>Multi-membres<\/strong>: Nous avons vu dans <a href=\"https:\/\/frogcast.com\/fr\/blog\/meteorologie\/how-are-weather-forecasts-generated\/\">notre article pr\u00e9c\u00e9dent sur la pr\u00e9vision m\u00e9t\u00e9orologique<\/a> qu'il \u00e9tait possible de tirer parti de la nature chaotique de l'atmosph\u00e8re. Nous pouvons \u00e9galement utiliser l'incertitude concernant l'\u00e9tat initial que fournit le mod\u00e8le. Ici, nous g\u00e9n\u00e9rons un ensemble de pr\u00e9visions \u00e0 partir d'un ensemble d'\u00e9tats initiaux \u00e9quiprobables.<\/li>\n\n\n\n<li><strong>Multi-runs<\/strong>: La plupart des mod\u00e8les de pr\u00e9vision num\u00e9rique du temps op\u00e9rationnels g\u00e9n\u00e8rent une pr\u00e9vision toutes les 3 \u00e0 6 heures. La pr\u00e9vision la plus r\u00e9cente est statistiquement la plus susceptible d'\u00eatre la plus pr\u00e9cise. Cependant, il peut \u00eatre utile de prendre en compte les une ou deux pr\u00e9visions pr\u00e9c\u00e9dentes.<\/li>\n\n\n\n<li><strong>Rayon spatio-temporel<\/strong>: Pour certaines variables m\u00e9t\u00e9orologiques, la localisation et le timing sont difficiles \u00e0 \u00e9tablir (par exemple les pr\u00e9cipitations). Dans ces cas, il peut \u00eatre int\u00e9ressant d'utiliser non seulement le point de grille du mod\u00e8le et le pas de temps d'int\u00e9r\u00eat, mais aussi les points environnants et les pas de temps pr\u00e9c\u00e9dents et suivants.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">De la pr\u00e9vision d'ensemble aux quantiles<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Comprendre les quantiles<\/h3>\n\n\n\n<p>Imaginons maintenant que nous disposons d'un ensemble tr\u00e8s riche de pr\u00e9visions. Il fournit 100 sc\u00e9narios de pr\u00e9visions de temp\u00e9rature \u00e0 notre point d'int\u00e9r\u00eat. Comment traiter et tirer parti de ce volume de donn\u00e9es ? Les statistiques !<\/p>\n\n\n\n<p>Pour chaque pas de temps, nous allons d'abord trier nos valeurs de temp\u00e9rature pr\u00e9vues par ordre croissant. Nous pouvons ensuite facilement en d\u00e9duire les <strong>quantiles<\/strong>, qui divisent notre ensemble de donn\u00e9es en intervalles \u00e9quiprobables.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Comment fonctionnent les quantiles<\/h3>\n\n\n\n<p>Par exemple, le quantile 10 % (P10) est la valeur de temp\u00e9rature attendue telle que 10 % de notre ensemble est inf\u00e9rieur et 90 % est sup\u00e9rieur. Le quantile 50 % (P50), \u00e9galement connu sous le nom de m\u00e9diane, est la pr\u00e9vision de temp\u00e9rature telle que 50 % de notre ensemble est inf\u00e9rieur et 50 % est sup\u00e9rieur. <\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ce que les quantiles nous disent<\/h3>\n\n\n\n<p>Les quantiles fournissent des informations essentielles sur la distribution statistique de notre ensemble. Ils nous renseignent donc sur la confiance que nous pouvons avoir dans la pr\u00e9vision.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Exemple 1 : Confiance \u00e9lev\u00e9e<\/h4>\n\n\n\n<p>Si notre ensemble donne une valeur m\u00e9diane de 25\u00b0C, un quantile P10 de 24\u00b0C et un P90 de 27\u00b0C, cela signifie que l'ensemble est relativement homog\u00e8ne. Nous avons 80 % de chances d'avoir une temp\u00e9rature entre 24\u00b0C et 27\u00b0C.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Exemple 2 : Confiance faible<\/h4>\n\n\n\n<p>En revanche, si pour la m\u00eame valeur m\u00e9diane de 25\u00b0C, les quantiles P10 et P90 sont respectivement de 17\u00b0C et 30\u00b0C, cela signifie que les diff\u00e9rents sc\u00e9narios sont largement dispers\u00e9s et que la pr\u00e9vision est incertaine.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1000\" height=\"300\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_precipitation_forecasts_with_quantiles1.webp\" alt=\"Dynamic precipitation forecast chart displaying a blue median line surrounded by shaded blue confidence intervals (quantiles) representing rainfall intensity uncertainty.\" class=\"wp-image-3030\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_precipitation_forecasts_with_quantiles1.webp 1000w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_precipitation_forecasts_with_quantiles1-300x90.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_precipitation_forecasts_with_quantiles1-768x230.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_precipitation_forecasts_with_quantiles1-18x5.webp 18w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Visualiser l'incertitude des pr\u00e9cipitations. Au lieu d'une seule valeur, notre API fournit la distribution compl\u00e8te des probabilit\u00e9s (quantiles), vous permettant de voir la gamme des intensit\u00e9s de pr\u00e9cipitations possibles.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1000\" height=\"300\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_temperatures_forecasts_with_quantiles.webp\" alt=\"Temperature forecast graph showing a red median line with shaded pink and red bands representing the P10-P90 and P25-P75 confidence intervals.\" class=\"wp-image-3029\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_temperatures_forecasts_with_quantiles.webp 1000w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_temperatures_forecasts_with_quantiles-300x90.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_temperatures_forecasts_with_quantiles-768x230.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/exemple_temperatures_forecasts_with_quantiles-18x5.webp 18w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Lire la dispersion. Une zone ombr\u00e9e large indique une incertitude \u00e9lev\u00e9e, tandis qu'une bande \u00e9troite sugg\u00e8re une confiance \u00e9lev\u00e9e. Ce graphique illustre comment les quantiles r\u00e9v\u00e8lent la fiabilit\u00e9 d'une pr\u00e9vision de temp\u00e9rature.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u00c9tude de cas : Risque de gel en agriculture<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Le d\u00e9fi de la protection contre le gel<\/h3>\n\n\n\n<p>Le gel est l'un des al\u00e9as les plus redout\u00e9s par les agriculteurs. L'impact du gel sur les vignobles et les vergers peut \u00eatre d\u00e9vastateur s'il survient au moment de la floraison. Il peut entra\u00eener une r\u00e9duction voire une perte totale des r\u00e9coltes.<\/p>\n\n\n\n<p>Les producteurs investissent beaucoup de temps, d'\u00e9nergie et de ressources financi\u00e8res dans la protection de leurs terres contre cet al\u00e9a.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Les m\u00e9thodes de protection<\/h3>\n\n\n\n<p>Il existe plusieurs fa\u00e7ons de se prot\u00e9ger contre le gel :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Aspersion<\/strong>: Elle consiste \u00e0 irriguer les parcelles et \u00e0 couvrir les bourgeons d'une couche de glace pour les maintenir \u00e0 une temp\u00e9rature proche de 0\u00b0C.<\/li>\n\n\n\n<li><strong>R\u00e9chauffement atmosph\u00e9rique<\/strong>: Les agriculteurs utilisent des br\u00fbleurs ou des fils chauffants.<\/li>\n\n\n\n<li><strong>Ventilation de l'air<\/strong>: Les tours antigel (\u00e9galement connues sous le nom de brasseurs d'air ou d'h\u00e9licopt\u00e8res) emp\u00eachent l'accumulation d'air froid dans les m\u00e8tres inf\u00e9rieurs de l'atmosph\u00e8re.<\/li>\n<\/ul>\n\n\n\n<p>Toutes ces techniques sont tr\u00e8s co\u00fbteuses. Il est essentiel d'utiliser des mesures et des pr\u00e9visions m\u00e9t\u00e9orologiques de haute qualit\u00e9 pour :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Les activer lorsqu'il y a un risque de gel et \u00e9viter la perte de r\u00e9coltes<\/li>\n\n\n\n<li>Limiter les fausses alarmes et \u00e9viter ainsi les co\u00fbts qui y sont associ\u00e9s<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Un exemple pratique<\/h3>\n\n\n\n<p>Nous proposons ici d'utiliser un cas simplifi\u00e9 pour illustrer l'apport des quantiles dans une pr\u00e9vision de temp\u00e9rature. Cela montrera comment ils aident \u00e0 activer les moyens de lutte contre le gel des cultures.<\/p>\n\n\n\n<p>Prenons la pr\u00e9vision de temp\u00e9rature suivante pour les prochaines 48 heures pour notre point d'int\u00e9r\u00eat. Nous supposons que les agriculteurs activeront leurs mesures de protection contre le gel en cas de pr\u00e9vision indiquant une temp\u00e9rature inf\u00e9rieure \u00e0 0\u00b0C.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Sc\u00e9nario 1 : Pr\u00e9vision d\u00e9terministe uniquement<\/h4>\n\n\n\n<p>Dans le premier cas, nous avons uniquement une pr\u00e9vision d\u00e9terministe (rouge). Elle n'indique pas que le seuil de d\u00e9clenchement de 0\u00b0C sera franchi. Malheureusement, la temp\u00e9rature chute \u00e0 -2\u00b0C la deuxi\u00e8me nuit. Cela cause des dommages aux cultures.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1000\" height=\"625\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_without_quantiles.webp\" alt=\"Standard deterministic temperature forecast line graph compared to actual observation, illustrating a false alarm scenario for frost risk.\" class=\"wp-image-3028\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_without_quantiles.webp 1000w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_without_quantiles-300x188.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_without_quantiles-768x480.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_without_quantiles-18x12.webp 18w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Le co\u00fbt des d\u00e9cisions binaires. Une pr\u00e9vision d\u00e9terministe standard (ligne rouge) pr\u00e9dit du gel, d\u00e9clenchant des mesures de protection co\u00fbteuses. Cependant, la temp\u00e9rature r\u00e9elle (ligne bleue) est rest\u00e9e positive \u2014 r\u00e9sultant en une perte d'argent.<\/figcaption><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\">Sc\u00e9nario 2 : Pr\u00e9vision avec quantiles<\/h4>\n\n\n\n<p>Dans le deuxi\u00e8me cas, nous disposons \u00e9galement de deux quantiles, P20 et P80, pour quantifier la confiance dans notre pr\u00e9vision.<\/p>\n\n\n\n<p>La premi\u00e8re nuit, le quantile P20 reste \u00e9galement au-dessus de 0\u00b0C. Il semble donc certain que la temp\u00e9rature restera positive.<\/p>\n\n\n\n<p>Cependant, l'incertitude est plus \u00e9lev\u00e9e pour la deuxi\u00e8me nuit. Le risque de gel devient significatif.<\/p>\n\n\n\n<p>Gr\u00e2ce \u00e0 cette information, l'agriculteur aura pu \u00e9viter d'activer son \u00e9quipement pr\u00e9ventif inutilement la premi\u00e8re nuit. Mais il aura bien prot\u00e9g\u00e9 ses cultures la deuxi\u00e8me nuit !<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"625\" src=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_with_quantiles.webp\" alt=\"Probabilistic frost risk chart showing P20 and P80 confidence intervals, demonstrating how uncertainty data prevents unnecessary anti-frost activation.\" class=\"wp-image-3027\" srcset=\"https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_with_quantiles.webp 1000w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_with_quantiles-300x188.webp 300w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_with_quantiles-768x480.webp 768w, https:\/\/frogcast.com\/wp-content\/uploads\/2025\/12\/agriculture_case_study_forecast_with_quantiles-18x12.webp 18w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><figcaption class=\"wp-element-caption\">Prise de d\u00e9cision plus intelligente. En analysant le quantile P20 (ligne orange), l'agriculteur voit qu'il y a une forte probabilit\u00e9 que la temp\u00e9rature reste effectivement au-dessus de z\u00e9ro la premi\u00e8re nuit, \u00e9conomisant le co\u00fbt du d\u00e9ploiement des chauffages.<\/figcaption><\/figure>","protected":false},"excerpt":{"rendered":"<p>D\u00e9couvrez comment les intervalles de confiance et les pr\u00e9visions d'ensemble permettent de quantifier l'incertitude des pr\u00e9visions m\u00e9t\u00e9orologiques et d'am\u00e9liorer la prise de d\u00e9cision.<\/p>","protected":false},"author":2,"featured_media":2975,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[27],"tags":[],"class_list":["post-3026","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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