Subseasonal forecasting\u2014predicting temperature and precipitation 2 to 6 weeks ahead\u2014is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, to counter these errors, we introduce an\u00a0adaptive bias correction<\/i>\u00a0(ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We show that, when applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60\u201390% (over baseline skills of 0.18\u20130.25) and precipitation forecasting skill by 40\u201369% (over baseline skills of 0.11\u20130.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions.<\/p>\n","protected":false},"excerpt":{"rendered":"
Subseasonal forecasting\u2014predicting temperature and precipitation 2 to 6 weeks ahead\u2014is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,198583],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[252520,267906],"msr-conference":[],"msr-journal":[265488],"msr-impact-theme":[261670],"msr-pillar":[],"class_list":["post-886275","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-ecology-environment","msr-locale-en_us","msr-field-of-study-climate-change","msr-field-of-study-subseasonal-forecasting"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2023-6-15","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.nature.com\/articles\/s41467-023-38874-y?utm_campaign=related_content&utm_source=EARTHENV&utm_medium=communities","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"guest","value":"soukayna-mouatadid","user_id":666945,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=soukayna-mouatadid"},{"type":"guest","value":"paulo-orenstein","user_id":608775,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=paulo-orenstein"},{"type":"guest","value":"genevieve-flaspohler","user_id":666984,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=genevieve-flaspohler"},{"type":"guest","value":"judah-cohen","user_id":608778,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=judah-cohen"},{"type":"user_nicename","value":"Miruna Oprescu","user_id":37353,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Miruna Oprescu"},{"type":"guest","value":"ernest-fraenkel","user_id":359195,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=ernest-fraenkel"},{"type":"user_nicename","value":"Lester Mackey","user_id":36161,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Lester Mackey"}],"msr_impact_theme":["Resilience"],"msr_research_lab":[199563],"msr_event":[],"msr_group":[847351],"msr_project":[608757],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":608757,"post_title":"Subseasonal Climate Forecasting","post_name":"subseasonal-climate-forecasting","post_type":"msr-project","post_date":"2019-09-16 06:15:14","post_modified":"2023-07-27 12:48:19","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/subseasonal-climate-forecasting\/","post_excerpt":"Water managers in the western United States rely on long-term forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. 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