A model intercomparison of the atmospheric dispersion of cesium‐137 (¹³⁷Cs) emitted after the Fukushima Daiichi Nuclear Power Plant accident in Japan was conducted to understand the behavior of atmospheric ¹³⁷Cs 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 ¹³⁷Cs events observed by measurements, and the figure of merit in space for the total deposition of ¹³⁷Cs exceeded 80. The lower score of the atmospheric ¹³⁷Cs than that of the deposition originated from the difference in timing between observed and simulated atmospheric ¹³⁷Cs. Our analyses indicated that meteorological data were most critical for reproducing the atmospheric ¹³⁷Cs events. The results further revealed that differences in ¹³⁷Cs 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 ¹³⁷Cs, and those with strong diffusion succeeded in capturing the high ¹³⁷Cs concentrations observed; however, they also tended to overestimate the concentrations.

Wet deposition, including both in- and below-cloud scavenging, is critical for the atmospheric transport modeling of ¹³⁷Cs following the Fukushima Daiichi Nuclear power plant (FDNPP) accident. Although intensively investigated, wet deposition simulation is still subject to uncertainties of meteorological inputs and wet scavenging modeling, leading to biased ¹³⁷Cs transport prediction. To reduce the dual uncertainties, in- and below-cloud wet scavenging schemes of ¹³⁷Cs were simultaneously integrated into Weather Research and Forecasting-Chemistry (WRF-Chem), yielding online coupled modeling of meteorology and the two wet scavenging processes. The integration was performed using 25 combinations of different in- and below-cloud schemes, covering most schemes in the literature. Two microphysics schemes were also tested to better reproduce the precipitation. The 25 models and the ensemble mean of 9 representative models were systematically compared with the below-cloud-only WRF-Chem model, using the cumulative deposition and atmospheric concentrations of ¹³⁷Cs measurements. The results reveal that, with the Morrison's double moment cloud microphysics scheme, the developed models could better reproduce the rainfall and substantially improve the cumulative deposition simulation. The in-cloud scheme is influential to the model behaviors and those schemes considering cloud parameters also improve the atmospheric concentration simulations, whereas the others solely dependent on the rain intensity are sensitive to meteorology. The ensemble mean achieves satisfactory performance except one plume event, but still outperforms most models.