Lessons learned from atmospheric modeling studies after the Fukushima nuclear accident: Ensemble simulations, data assimilation, elemental process modeling, and inverse modeling

• Kajino Mizuo

  Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA) Faculty of Life and Environmental Sciences, University of Tsukuba RIKEN Advanced Institute for Computational Science

• Sekiyama Tsuyoshi Thomas

  Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA)

• Mathieu Anne

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)

• Korsakissok Irène

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)

• Périllat Raphaël

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN) Strathom Energie

• Quélo Denis

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)

• Quérel Arnaud

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN) Bertin Technologies

• Saunier Olivier

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)

• Adachi Kouji

  Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA)

• Girard Sylvain

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN) Phimeca, Engineering

• Maki Takashi

  Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA)

• Yumimoto Keiya

  Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA) Research Institute for Applied Mechanics, Kyushu University

• Didier Damien

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)

• Masson Olivier

  Institut de Radioprotection et de Sûreté Nucléaire (IRSN)

• Igarashi Yasuhito

  Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA)

抄録

Modeling studies on the atmospheric diffusion and deposition of the radiocesium associated with the Fukushima Dai-ichi Nuclear Power Plant accident is reviewed here, with a focus on a research collaboration between l’Institut de Radioprotection et de Sûreté Nucléaire (IRSN)­—the French institute in charge of evaluating the consequences of nuclear accidents and advising authorities in case of a crisis—and the Meteorological Research Institute (MRI) of the Japan Meteorological Agency—an operational weather forecasting center in Japan. While the modelers have come to know that wet deposition is one of the key processes, the size of its influence is unknown. They also know that the simulation results vary, but they do not know exactly why. Under the research collaboration, we aimed to understand the atmospheric processes, especially wet deposition, and to quantify the uncertainties of each component of our simulation using various numerical techniques, such as ensemble simulations, data assimilation, elemental process modeling, and inverse modeling. The outcomes of these collaborative research topics are presented in this paper. We also discuss the future directions of atmospheric modeling studies: data assimilation using the high temporal and spatial resolution surface concentration measurement data, and consideration of aerosol properties such as size and hygroscopicity into wet and dry deposition schemes.

収録刊行物

• ＧＥＯＣＨＥＭＩＣＡＬ　ＪＯＵＲＮＡＬ

  ＧＥＯＣＨＥＭＩＣＡＬ　ＪＯＵＲＮＡＬ 52 (2), 85-101, 2018

  一般社団法人日本地球化学会

キーワード

• ensemble simulation
• data assimilation
• wet deposition
• aerosol properties
• inverse modeling

詳細情報

• CRID: 1390282679528837248
• NII論文ID: 130006708585
• NII書誌ID: AA00654975
• DOI: 10.2343/geochemj.2.0503
• ISSN: 18805973
• ISSN: 00167002
• NDL書誌ID: 028933156
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I028933156
• Web Site: https://www.jstage.jst.go.jp/article/geochemj/52/2/52_2.0503/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可