インターナショナルセッション（口頭発表）
セッション記号 A (大気水圏科学)
» A-AS 大気科学・気象学・大気環境
[A-AS03] Understanding extremes: high-resolution models, dense observations and the emergent role of big data
2015年5月26日(火) 11:00 〜 12:45
301A (3F)
コンビーナ:*Eugenio Realini(Geomatics Research & Development Srl)、Saji Hameed(The University of Aizu)、津田 敏隆(京都大学生存圏研究所)、座長:Eugenio Realini(Geomatics Research & Development Srl)、Saji Hameed(The University of Aizu)
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.
