| Project/Area Number |
17H01705
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
Matsui Tomoko 統計数理研究所, モデリング研究系, 教授 (10370090)
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| Co-Investigator(Kenkyū-buntansha) |
村上 大輔 統計数理研究所, データ科学研究系, 助教 (20738249)
山形 与志樹 国立研究開発法人国立環境研究所, 地球環境研究センター, 主席研究員 (90239864)
AMES MATTHEW 統計数理研究所, 統計的機械学習研究センター, 特任助教 (90794769)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥15,990,000 (Direct Cost: ¥12,300,000、Indirect Cost: ¥3,690,000)
Fiscal Year 2019: ¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2018: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2017: ¥5,850,000 (Direct Cost: ¥4,500,000、Indirect Cost: ¥1,350,000)
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| Keywords | 時空間解析 / 地表面温度 / 地球温暖化 / 空間・環境統計 |
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| Outline of Final Research Achievements |
We developed the spatio-temporal modeling method based on ST-BLUE to capture the extreme conditions of heat waves with high accuracy. Since it was found that modeling of regions with different characteristics, such as urban centers and suburbs, must be done separately, we developed a method for local prediction of heat wave conditions. Using this method, we estimated the mean, variance, skewness, and kurtosis of the processes behind the surface temperature for each location in the Tokyo metropolitan area, and found that (1) the temperature was high in the western part of central Tokyo, (2) the temperature dispersion is large along the sea coast and mountains, (3) the kurtosis was high in the city center and the temperature was extremely high, and (4) the temperature was high in the tail of the basin between central Tokyo, Kumagaya, and Gunma.
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| Academic Significance and Societal Importance of the Research Achievements |
本手法により、時々刻々変化する熱波リスク事象の状況把握を高精度化し、そのためのモデル選択やシステム同定の研究を活性化することが期待される。また、高精度な時空間分布の推定を実施して、都市のヒートアイランド現象の変動要因の解明を可能とする。このシミュレーション結果は熱波対策立案の支援に繋がることが期待される。さらに、今後の著しい温暖化影響が予想される国内外の都市を対象に、本手法を活用した都市のレジリエンス研究への応用が期待できる。
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