領域外・複数領域(M)
セッション小記号 地球科学一般・情報地球科学(GI)
セッションID M-GI26
タイトル 和文 Data-driven approaches for weather and hydrological predictions
英文 Data-driven approaches for weather and hydrological predictions
タイトル短縮名 和文 Data driven study in weather prediction
英文 Data driven study in weather prediction
代表コンビーナ 氏名 和文 小槻 峻司
英文 Shunji Kotsuki
所属 和文 千葉大学　環境リモートセンシング研究センター
英文 Center for Environmental Remote Sensing, Chiba University
共同コンビーナ 1 氏名 和文 松岡 大祐
英文 Daisuke Matsuoka
所属 和文 海洋研究開発機構
英文 Japan Agency for Marine-Earth Science and Technology
共同コンビーナ 2 氏名 和文 岡崎 淳史
英文 Atsushi Okazaki
所属 和文 千葉大学
英文 Chiba University
共同コンビーナ 3 氏名 和文 澤田 洋平
英文 Yohei Sawada
所属 和文 東京大学
英文 The University of Tokyo
発表言語 E
スコープ 和文 
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.
英文 
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.
発表方法 口頭および（または）ポスターセッション
招待講演 本田 匠 (北海道大学大学院理学研究院)
安田 勇輝 (東京工業大学)
白石 健太 (千葉大学)
時間 講演番号 タイトル 発表者
口頭発表 5月30日 PM1
13:45 - 14:00 MGI26-01 Machine learning enables real-time proactive quality control: A proof-of-concept study 本田 匠
14:00 - 14:15 MGI26-02 Predictability of climate tipping: data assimilation approach 久保 亘
14:15 - 14:30 MGI26-03 Short-term deep-learning rainfall prediction in Japan 金子 凌
14:30 - 14:45 MGI26-04 Advancing Multi-Hazard Worst-Case Scenario Analysis: Maximizing the Potential of Large Ensemble Tropical Cyclone Forecasts Md. Rezuanul Islam
14:45 - 15:00 MGI26-05 Forecasting monthly sea surface temperature using Koopman mode decomposition 塩尻 大也
15:00 - 15:15 MGI26-06 Earth System Modeling in Latent Space: A Time-Varying Parameter Approach 澤田 洋平
口頭発表 5月30日 PM2
15:30 - 15:45 MGI26-07 Super-Resolution Data Assimilation Using Denoising Diffusion Probabilistic Models 安田 勇輝
15:45 - 16:00 MGI26-08 台風予測改善のための衛星観測を用いた大気モデルのパラメータ推定 廣瀬 郁希
16:00 - 16:15 MGI26-09 Uncertainty Quantification of Flood Hazards Focusing on Parameters of a Rainfall-Runoff-Inundation Coupled Model 日浦 直紀
16:15 - 16:30 MGI26-10 深層学習による北半球夏期季節内変動(BSISO)の予測と説明可能性 前田 優樹
16:30 - 16:45 MGI26-11 Downscaling Precipitation Data using Convolutional Neural Network Coupled with Wasserstein Generative Adversarial Networks 白石 健太
講演番号 タイトル 発表者
ポスター発表 5月30日 PM3
MGI26-P01 Real-Time Forecasting of Energy Consumption in Residential Buildings Jui Sheng Chou
MGI26-P02 Disentanglement of the features in Rankine vortices using VAE 井貫 恵多朗
MGI26-P03 Machine learning based post-processing to enhance daily rainfall forecasts of July 2019-2021 over the Kyushu region focusing on surface conditions at remote moisture sources Muditha Madusanka Dantanarayana
MGI26-P04 Improving global precipitation estimates from rain gauge observations using local ensemble data assimilation 武藤 裕花