| Project/Area Number |
16K16378
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Natural disaster / Disaster prevention science
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| Research Institution | University of Tsukuba |
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Principal Investigator |
MATSUEDA MIO 筑波大学, 計算科学研究センター, 助教 (80738691)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 異常気象 / 予測可能性 / アンサンブル予報 / 天候レジーム / 数値予報 / 熱波 / 豪雨 / 大気顕著現象 / 寒波 / 気象災害 / 顕著現象 |
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| Outline of Final Research Achievements |
In the study, there are three focused topics: 1. predictability of winter Euro-Atlantic (EA) regimes, 2. predictability of regime-related heatwave in EA summer, and 3. predictability of severe weather events occurred during the research period (e.g. the 2018 Western Japan Heavy Rainfall). Regarding the winter regimes, the most interesting result is that the longer the NAO－ events persist, the higher the skill of forecasts initialised on NAO－. The skill dependency on regime duration is less clearly observed for the other regimes. Regarding the regime-related heatwave, 6 of 8 detected EA summer regimes are related to well-known heatwaves. The UK-France heatwave regime was least predictable. Regarding the predictability of the 2018 western Japan heavy rainfall which was highly predicted by NCEP operational forecast, joint analysis using operational forecasts and ensemble simulation with NCEP initial conditions and an ECMWF model revealed why ECMWF had lower skill for the event.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では、熱波・寒波・豪雨・暴風などの顕著現象をもたらす天候レジームの予測可能性を調べたが、予測精度がどのような場合に高く(低く)なるのかを明らかにしたことは、学術的にも、減災の観点からも大変重要な結果である。また、予報ができなかった原因を探るために、各国の気象局が日々の予報で使っている初期値とモデルを入れ替える実験を行った。これは、予測できなかった原因が、初期値にあったのか、モデルにあったかを切り分ける非常に有益なツールであり、このような事例解析を積み重ねて初期値またはモデルを改良していくことは、より精度の高い天気予報を提供し、人々がより安全な生活を送ることへと繋がる。
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