Model Intercomparison of Atmospheric ¹³⁷Cs From the Fukushima Daiichi Nuclear Power Plant Accident: Simulations Based on Identical Input Data

Abstract

A model intercomparison of the atmospheric dispersion of cesium-137 ( 137 Cs) emitted after the Fukushima Daiichi Nuclear Power Plant accident in Japan was conducted to understand the behavior of atmospheric 137 Cs in greater detail. The same meteorological data with a fine spatiotemporal resolution and an emission inventory were applied to all models to exclude the differences among the models originating from differences in meteorological and emission data. The meteorological data were used for initial, boundary, and nudging data or offline meteorological field. Furthermore, a horizontal grid with the same resolution as that of the meteorological data was adopted for all models. This setup enabled us to focus on model variability originating from the processes included in each model, for example, physical processes. The multimodel ensemble captured 40% of the atmospheric 137 Cs events observed by measurements, and the figure of merit in space for the total deposition of 137 Cs exceeded 80. The lower score of the atmospheric 137 Cs than that of the deposition originated from the difference in timing between observed and simulated atmospheric 137 Cs. Our analyses indicated that meteorological data were most critical for reproducing the atmospheric 137 Cs events. The results further revealed that differences in 137 Cs concentrations among the models originated from deposition and diffusion processes when the meteorological field was simulated reasonably well. The models with small deposition fluxes produced higher scores for atmospheric 137 Cs, and those with strong diffusion succeeded in capturing the high 137 Cs concentrations observed; however, they also tended to overestimate the concentrations.

Reproducibility of Surface Wind and Tracer Transport Simulations over Complex Terrain Using 5-, 3-, and 1-km-Grid Models

“…The tracer (Cs-137) transport simulations were performed by an offline Eulerian regional air quality model, which was driven by the 5-, 3-, and 1-km-grid meteorological analyses mentioned above. The offline Eulerian regional air quality model was originally developed by Kajino et al (2012Kajino et al ( , 2019a for nonradioactive aerosol simulations and has subsequently been used for Fukushima nuclear pollutant simulations by Adachi et al (2013), Sekiyama et al (2015Sekiyama et al ( , 2017, Kajino et al (2016Kajino et al ( , 2018Kajino et al ( , 2019b, Inomata et al (2018), Kitayama et al (2018), Mathieu et al (2018), Sato et al (2018), Sekiyama and Iwasaki (2018), and Iwasaki et al (2019).…”

“…The precipitation and foggy areas are not exactly identical among the three resolution models, which leads to large discrepancies in the deposition location. The authors have previously performed some Fukushima simulations with and without deposition processes (Sato et al 2018;Iwasaki et al 2019). The impact of wet deposition on the simulations appeared large in the northern part of the Kanto Plain probably because of drizzle or fog on 15 March 2011 over the Kanto Plain.…”

Radioxenon Releases from A Nuclear Power Plant: Stack Data and Atmospheric Measurements

“…The model includes important aerodynamic processes such as dry and wet deposition along the transportation path. The model is well adapted to handle dispersion of radioactive materials and has been successfully validated (Sato et al 2018) and applied in several studies (Björnham et al 2017;Grahn et al 2015).…”

Application of linear minimum variance estimation to the multi-model ensemble of atmospheric radioactive Cs-137 with observations

“…The MME technique is applied for weather forecasting (Stensrud et al, 2000;Gneiting et al, 2005), climate projections (Knutti et al, 2010;Taylor et al, 2012), short-lived climate forcer assessments (Lamarque et al, 2013;Myhre et al, 2013), air quality forecasting (Solazzo et al, 2012;Sessions et al, 2015) and atmospheric dispersion predictions (Draxler et al, 2015;Sato et al, 2018). The members of the ensemble are widely spread for the use of various numerical models with perturbed initial conditions and various physical and chemical modules.…”

“…The pure average method is a popular method for MME-based climate studies according to the concept of "one model, one vote" (Knutti et al, 2010;Weigel et al, 2010) or evidence of improvements in the concentrations of pollutants in air quality and atmospheric dispersion simulations (McKeen et al, 2005;van Loon et al, 2007;Sessions et al, 2015;Kitayama et al, 2018). The weighting scheme, especially with a relatively smaller ensemble size, can be adopted to eliminate the common biases and improve the ensemble results in the weather forecast (Krishnamurti et al, 1999), climate studies (Haughton et al, 2015), air quality forecasts (Casanova and Ahrens, 2009) and atmospheric dispersion predictions (Nakajima et al, 2017;Sato et al, 2018). The selected scheme is used in the air quality simulations (Solazzo et al, 2012(Solazzo et al, , 2013Solazzo and Galmarini, 2015a) and the atmospheric dispersion simulations Solazzo and Galmarini, 2015b).…”