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領域外・複数領域(M)
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セッション小記号
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地球科学一般・情報地球科学(GI)
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セッションID
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M-GI34
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タイトル
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和文
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Data-driven approaches for weather and hydrological predictions
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英文
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Data-driven approaches for weather and hydrological predictions
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タイトル短縮名
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和文
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Data driven study in weather prediction
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英文
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Data driven study in weather prediction
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代表コンビーナ
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氏名
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和文
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小槻 峻司
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英文
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Shunji Kotsuki
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所属
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和文 |
千葉大学 環境リモートセンシング研究センター |
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英文
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Center for Environmental Remote Sensing, Chiba University
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共同コンビーナ 1
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氏名
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和文
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Carolynne Hultquist
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英文
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Carolynne Hultquist
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所属
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和文
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University of Canterbury
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英文
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University of Canterbury
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共同コンビーナ 2
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氏名
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和文
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澤田 洋平
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英文
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Yohei Sawada
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所属
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和文
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東京大学
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英文
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The University of Tokyo
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共同コンビーナ 3
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氏名
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和文
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Ana Carolina Vaz
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英文
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Ana Carolina Vaz
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所属
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和文
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Instituto Nacional de Pesquisas Oceanicas
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英文
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Instituto Nacional de Pesquisas Oceanicas
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共同コンビーナ 4
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氏名
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和文
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関山 剛
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英文
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Thomas Sekiyama
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所属
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和文
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気象庁気象研究所
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英文
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Meteorological Research Institute
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共同コンビーナ 5
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氏名
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和文
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安田 勇輝
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英文
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Yuki Yasuda
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所属
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和文
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海洋研究開発機構
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英文
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Japan Agency for Marine-Earth Science and Technology
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発表言語
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E
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スコープ
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和文
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In the digital era, data-driven techniques are transforming our understanding and prediction capabilities of complex earth systems. This session aims to explore the cutting-edge methodological and applicational studies for weather, climate and hydrological predictions. Key themes includes: (1) methodological studies to deepen data-driven approaches for geoscience problems, (2) machine/deep learning studies applied for extreme weather-related disasters, (3) climate predictive analysis to discern climate variability, trends, and anomalies, (4) integrating remote sensing and ground data to refine prediction models. This session aims to foster a rich dialogue among experts, highlighting both the advancements and challenges in data-driven environmental modeling. Participants will gain insights into current best practices and envision the future trajectory of this rapidly evolving domain.
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英文
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In the digital era, data-driven techniques are transforming our understanding and prediction capabilities of complex earth systems. This session aims to explore the cutting-edge methodological and applicational studies for weather, climate and hydrological predictions. Key themes includes: (1) methodological studies to deepen data-driven approaches for geoscience problems, (2) machine/deep learning studies applied for extreme weather-related disasters, (3) climate predictive analysis to discern climate variability, trends, and anomalies, (4) integrating remote sensing and ground data to refine prediction models. This session aims to foster a rich dialogue among experts, highlighting both the advancements and challenges in data-driven environmental modeling. Participants will gain insights into current best practices and envision the future trajectory of this rapidly evolving domain.
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発表方法
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口頭および（または）ポスターセッション
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