Meteorological analysis and prediction on the low-dimensional space

2017 Fiscal Year Final Research Report

• Project/Area Number: 26310201
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Partial Multi-year Fund
• Section: 特設分野
• Research Field: Mathematical Sciences in Search of New Cooperation
• Research Institution: Hokkaido University
• Principal Investigator: Inatsu Masaru 北海道大学, 理学研究院, 教授 (80422450)
• Co-Investigator(Kenkyū-buntansha): 中野 直人 京都大学, 国際高等教育院, 特定講師 (30612642) ; 荒井 迅 中部大学, 創発学術院, 教授 (80362432)
• Co-Investigator(Renkei-kenkyūsha): Mukougawa Hitoshi 京都大学, 大学院理学研究科, 教授 (20261349)
• Project Period (FY): 2014-07-18 – 2018-03-31
• Keywords: 温帯低気圧 / 数理科学

Outline of Final Research Achievements

Meteorological low-frequency variability and prediction: A 5,000-yr integeration with an AGCM was conducted. We considered the relation between data-orbit manifold and 4-dimensional stochastic differential equation, focusing on the troposphere in boreal winter. After the breeding-growing-mode method was implemented in the AGCM, we compared multiple realisation in the long-term integration on the phase-space analysis with the initial-value growing.
Geometrical change of extratropical cyclones: We developed a brand-new cyclone tracking programme with persistent homology. By reconstructing data with merge-trees based on maxima and saddles of the scaler field, the neighbouring among homology classes were clarified. The cyclone identification with this algorithm is consistent with conventional meteorological analysis.

Free Research Field

気象学