セッション概要

大気水圏科学(A) セッション小記号 大気科学・気象学・大気環境(AS) セッションID A-AS03 タイトル 和文 Understanding extremes: high-resolution models, dense observations and the emergent role of big data 英文 Understanding extremes: high-resolution models, dense observations and the emergent role of big data タイトル短縮名 和文 Understanding weather/climate extremes 英文 Understanding weather/climate extremes 代表コンビーナ 氏名 和文 Realini Eugenio 英文 Eugenio Realini 所属 和文 Geomatics Research & Development Srl 英文 Geomatics Research & Development Srl メールアドレス eugenio.realini@g-red.eu 共同コンビーナ 1 氏名 和文 Hameed Saji 英文 Saji Hameed 所属 和文 The University of Aizu 英文 The University of Aizu メールアドレス saji.uaizu@gmail.com 共同コンビーナ 2 氏名 和文 津田 敏隆 英文 Toshitaka Tsuda 所属 和文 京都大学生存圏研究所 英文 Research Institute for Sustainable Humanosphere メールアドレス tsuda@rish.kyoto-u.ac.jp 国際セッション開催希望 国際セッションとしての開催を希望する 発表主要言語 英語 スコープ 和文 Weather and climate extremes are an inherent part of climate with enormous impacts on society and the environment. Extreme weather events result from complex interactions among several mesoscale processes. While climatic extremes are basically a result of low-frequency coupled instabilities of the ocean-atmosphere-land system, they involve a range of scales beside the climate scale. Complex interactions among the mesoscale, intraseasonal and interannual scales are important in determining the intensity and impact of climate extremes. Recent advances in high performance computing have led to increasingly realistic models of weather and climate that have improved both in terms of resolution and representation of physical processes. It has increasingly become possible to use a single model to simulate both weather and climate extremes and to explore the interaction across scales. On the other hand, the increasing diffusion of sensor technologies allow for the densification of atmosphere-observing systems, providing observation datasets characterized by high space and time resolutions. More than ever before, we have access to large and dense data sets both from simulations and observations that can be used to understand, monitor or predict extremes. The full exploitation of such large datasets, including their use in data assimilation, and for understanding the mechanisms for extremes, poses relevant challenges to data analysis, in relation to the performance of current computing systems. This session presents research works in high-resolution numerical weather and climate or multi-scale models and downscaling experiments; dense monitoring systems for atmospheric observations with high spatial and time resolutions, and assimilation of such observations into numerical models; methods and technologies related to the analysis of large and complex datasets of observations ("big data") for forecasting extremes. 英文 Weather and climate extremes are an inherent part of climate with enormous impacts on society and the environment. Extreme weather events result from complex interactions among several mesoscale processes. While climatic extremes are basically a result of low-frequency coupled instabilities of the ocean-atmosphere-land system, they involve a range of scales beside the climate scale. Complex interactions among the mesoscale, intraseasonal and interannual scales are important in determining the intensity and impact of climate extremes. Recent advances in high performance computing have led to increasingly realistic models of weather and climate that have improved both in terms of resolution and representation of physical processes. It has increasingly become possible to use a single model to simulate both weather and climate extremes and to explore the interaction across scales. On the other hand, the increasing diffusion of sensor technologies allow for the densification of atmosphere-observing systems, providing observation datasets characterized by high space and time resolutions. More than ever before, we have access to large and dense data sets both from simulations and observations that can be used to understand, monitor or predict extremes. The full exploitation of such large datasets, including their use in data assimilation, and for understanding the mechanisms for extremes, poses relevant challenges to data analysis, in relation to the performance of current computing systems. This session presents research works in high-resolution numerical weather and climate or multi-scale models and downscaling experiments; dense monitoring systems for atmospheric observations with high spatial and time resolutions, and assimilation of such observations into numerical models; methods and technologies related to the analysis of large and complex datasets of observations ("big data") for forecasting extremes. 発表方法希望 口頭および（または）ポスターセッション 招待講演