{"id":2023,"date":"2026-05-15T15:17:19","date_gmt":"2026-05-15T06:17:19","guid":{"rendered":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/?p=2023"},"modified":"2026-05-15T15:51:21","modified_gmt":"2026-05-15T06:51:21","slug":"egu26","status":"publish","type":"post","link":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/en\/2026\/05\/15\/egu26.html","title":{"rendered":"EGU26"},"content":{"rendered":"\n<p>Three papers on predictability of blocking predictability, frontal data assimilation, and machine learning prediction of sea-surface temperature were presented at <a href=\"https:\/\/www.egu26.eu\/\" data-type=\"link\" data-id=\"https:\/\/www.egu26.eu\/\">EGU26<\/a>, held in Vienna, Austria.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Nomura, S. and Enomoto, T.: Dynamical linkage between blocking predictability and jet stream quasi-stationary states, EGU General Assembly 2026, Vienna, Austria, 3\u20138 May 2026, EGU26-20078,&nbsp;<a href=\"https:\/\/doi.org\/10.5194\/egusphere-egu26-20078\">https:\/\/doi.org\/10.5194\/egusphere-egu26-20078<\/a>, 2026.<\/li>\n\n\n\n<li>Nakashita, S. and Enomoto, T.: Evaluation of data assimilation methods suitable for frontal structures, EGU General Assembly 2026, Vienna, Austria, 3\u20138 May 2026, EGU26-21740,&nbsp;<a href=\"https:\/\/doi.org\/10.5194\/egusphere-egu26-21740\">https:\/\/doi.org\/10.5194\/egusphere-egu26-21740<\/a>, 2026.<\/li>\n\n\n\n<li>Enomoto, T., Saito, A., and Nakashita, S.: Machine learning sea-surface temperature forecasting based on empirical orthogonal functions, EGU General Assembly 2026, Vienna, Austria, 3\u20138 May 2026, EGU26-18999,&nbsp;<a href=\"https:\/\/doi.org\/10.5194\/egusphere-egu26-18999\">https:\/\/doi.org\/10.5194\/egusphere-egu26-18999<\/a>, 2026.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Three papers on predictability of blocking predictability, frontal data assimilation, and machine learning pre\u2026 <span class=\"read-more\"><a href=\"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/en\/2026\/05\/15\/egu26.html\">Read More &raquo;<\/a><\/span><\/p>\n","protected":false},"author":10,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_locale":"en_US","_original_post":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/?p=2023","footnotes":""},"categories":[10],"tags":[171,172,173,175,16],"class_list":["post-2023","post","type-post","status-publish","format-standard","hentry","category-research","tag-blocking","tag-data-assimilation-2","tag-machine-learning","tag-sea-surface-temperature-2","tag-predictability","en-US"],"_links":{"self":[{"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/posts\/2023","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/comments?post=2023"}],"version-history":[{"count":3,"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/posts\/2023\/revisions"}],"predecessor-version":[{"id":2030,"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/posts\/2023\/revisions\/2030"}],"wp:attachment":[{"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/media?parent=2023"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/categories?post=2023"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.dpac.dpri.kyoto-u.ac.jp\/wp-json\/wp\/v2\/tags?post=2023"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}