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

Mizuo Kajino, Tsuyoshi Thomas Sekiyama, Anne Mathieu, Irène Korsakissok, Raphaël Périllat, Denis Quélo, Arnaud Quérel, Olivier Saunier, Kouji Adachi, Sylvain Girard, Takashi Maki, Keiya Yumimoto, Damien Didier, Olivier Masson, Yasuhito Igarashi

Research output: Contribution to journalReview article

8 Citations (Scopus)

Abstract

Modeling studies on the atmospheric diffusion and deposition of the radiocesium associated with the Fukushima Daiichi 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.

Original languageEnglish
Pages (from-to)85-101
Number of pages17
JournalGEOCHEMICAL JOURNAL
Volume52
Issue number2
DOIs
Publication statusPublished - Jan 1 2018

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nuclear accident
atmospheric modeling
lessons learned
assimilation
data simulation
wet deposition
accidents
data assimilation
Accidents
modeling
simulation
hygroscopicity
Japan
atmospheric diffusion
aerosol property
weather forecasting
dry deposition
Weather forecasting
nuclear power plant
accident

All Science Journal Classification (ASJC) codes

  • Geophysics
  • Geochemistry and Petrology

Cite this

Lessons learned from atmospheric modeling studies after the Fukushima nuclear accident : Ensemble simulations, data assimilation, elemental process modeling, and inverse modeling. / Kajino, Mizuo; Sekiyama, Tsuyoshi Thomas; Mathieu, Anne; Korsakissok, Irène; Périllat, Raphaël; Quélo, Denis; Quérel, Arnaud; Saunier, Olivier; Adachi, Kouji; Girard, Sylvain; Maki, Takashi; Yumimoto, Keiya; Didier, Damien; Masson, Olivier; Igarashi, Yasuhito.

In: GEOCHEMICAL JOURNAL, Vol. 52, No. 2, 01.01.2018, p. 85-101.

Research output: Contribution to journalReview article

Kajino, M, Sekiyama, TT, Mathieu, A, Korsakissok, I, Périllat, R, Quélo, D, Quérel, A, Saunier, O, Adachi, K, Girard, S, Maki, T, Yumimoto, K, Didier, D, Masson, O & Igarashi, Y 2018, 'Lessons learned from atmospheric modeling studies after the Fukushima nuclear accident: Ensemble simulations, data assimilation, elemental process modeling, and inverse modeling', GEOCHEMICAL JOURNAL, vol. 52, no. 2, pp. 85-101. https://doi.org/10.2343/geochemj.2.0503
Kajino, Mizuo ; Sekiyama, Tsuyoshi Thomas ; Mathieu, Anne ; Korsakissok, Irène ; Périllat, Raphaël ; Quélo, Denis ; Quérel, Arnaud ; Saunier, Olivier ; Adachi, Kouji ; Girard, Sylvain ; Maki, Takashi ; Yumimoto, Keiya ; Didier, Damien ; Masson, Olivier ; Igarashi, Yasuhito. / Lessons learned from atmospheric modeling studies after the Fukushima nuclear accident : Ensemble simulations, data assimilation, elemental process modeling, and inverse modeling. In: GEOCHEMICAL JOURNAL. 2018 ; Vol. 52, No. 2. pp. 85-101.
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