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  1. 防災科研関係論文

Prediction of meso-γ-scale local heavy rain by ground-based cloud radar assimilation with water vapor nudging

https://nied-repo.bosai.go.jp/records/3065
https://nied-repo.bosai.go.jp/records/3065
a450de8b-8070-475f-9105-f47ff149abc1
Item type researchmap(1)
公開日 2023-05-24
タイトル
言語 en
タイトル Prediction of meso-γ-scale local heavy rain by ground-based cloud radar assimilation with water vapor nudging
言語
言語 eng
著者 Ryohei Kato

× Ryohei Kato

en Ryohei Kato

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Shingo Shimizu

× Shingo Shimizu

en Shingo Shimizu

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Tadayasu Ohigashi

× Tadayasu Ohigashi

en Tadayasu Ohigashi

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Takeshi Maesaka

× Takeshi Maesaka

en Takeshi Maesaka

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Ken-ichi Shimose

× Ken-ichi Shimose

en Ken-ichi Shimose

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Koyuru Iwanami

× Koyuru Iwanami

en Koyuru Iwanami

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抄録
内容記述タイプ Other
内容記述 Meso-γ-scale (2?20 km) local heavy rain (LHR) can cause fatalities through the sudden rise of rivers and flooding of roads. To help prevent this loss of life, we developed prediction methods for these types of meteorological hazards. We assimilated ground-based cloud radar (Ka-band radar) data that can capture cloud droplets before raindrops form and attempted to predict LHR with a cloud resolving numerical weather prediction (NWP) model. High-temporal (1-min interval) three-dimensional cloud-radar data obtained through special observation were assimilated using a water vapor nudging method in the pre-rain stage of an LHR-causing cumulonimbus. While rainfall was not predicted by the NWP model without assimilation, LHR was predicted approximately 20 min after the conclusion of cloud-radar data assimilation cycling. Results suggest that NWP with cloud-radar data assimilation in the pre-rain stage has great potential for predicting LHR, and can lead to an early evacuation warning and subsequent evacuation of vulnerable populations.
言語 en
書誌情報 en : Weather and Forecasting

発行日 2022
出版者
言語 en
出版者 American Meteorological Society
DOI
関連識別子 10.1175/WAF-D-22-0017.1
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