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Based on the Weather Research and Forecasting Model Coupled with Chemistry (WRF-Chem) atmospheric chemistry model, a parameterization scheme for the radioactive isotope caesium (137Cs), considering processes such as advection, turbulent diffusion, dry deposition, and wet deposition, was constructed, enabling the spatial distribution simulation of the concentration and deposition of 137Cs. The experimental simulation studies were carried out during the high emission period of the Fukushima nuclear accident (from 11 to 17 March 2011). Two sets of comparison experiments, with or without deposition, were designed, the effects of wind field and precipitation on the spatial transport and ground deposition of 137Cs were analyzed, and the influence of wind field and precipitation on 137Cs vertical transport was analyzed in detail. The results indicate that the model can accurately simulate the meteorological and 137Cs variables. On 15 March, 137Cs dispersed towards the Kanto Plain in Japan under the influence of northeastern winds. In comparison to the experiment without deposition, the concentration of 137Cs in the Fukushima area decreased by approximately 286 Bq·m−3 in the deposition experiment. Under the influence of updrafts in the non-deposition experiment, a 137Cs cloud spread upward to a maximum height of 6 km, whereas in the deposition experiment, the highest dispersion of the 137Cs cloud only reach a height of 4 km. Affected by the wind field, dry deposition is mainly distributed in Fukushima, the Kanto Plain, and their eastern ocean areas, with a maximum dry deposition of 5004.5 kBq·m−2. Wet deposition is mainly influenced by the wind field and precipitation, distributed in the surrounding areas of Fukushima, with a maximum wet deposition of 725.3 kBq·m−2. The single-station test results from the deposition experiment were better than those for the non-deposition experiment: the percentage deviations of the Tokyo, Chiba, Maebashi, and Naraha stations decreased by 61%, 69%, 46%, and 51%, respectively, and the percentage root mean square error decreased by 46%, 25%, 38%, and 48%, respectively.

Nuclear explosions and accidents release large amounts of radionuclides that harm human health and the environment. Accurate forecasting of nuclide pollutants and assessment of the ramifications of nuclear incidents are necessary for the emergency response and disaster assessment of nuclide pollution. In this study, we developed a three-dimensional variational (3Dvar) system to assimilate ^137 Cs based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The distribution of ^137 Cs after the Fukushima nuclear accident in Japan on 15 March 2011 was analysed. The ^137 Cs background field at 06:00 UTC was assimilated using a 3Dvar system and surface observational data to optimise the ^137 Cs analysis field. Compared with the background field, the root mean square error (RMSE) and mean bias in the ^137 Cs analysis field decreased by 98% and 94%, respectively. The average fraction of predictions within factors of 2 (FAC2), 5 (FAC5), and 10 (FAC10) increased from 0.67, 0.72, and 0.72 to 0.90, 1.00, and 1.00, respectively. This substantial enhancement indicated the effectiveness of the 3DVar system in mitigating the uncertainty associated with the background field. Two 12 h forecast experiments were conducted to gauge the advancement in ^137 Cs forecasting facilitated by data assimilation (DA). The control experiment was conducted without DA, whereas the assimilation experiment was conducted with DA. Compared with the control experiment, the average FAC2, FAC5, and FAC10 in the assimilation experiment increased by 28%, 30%, and 29%, respectively. The average RMSE decreased by 33%. The mean bias and correlation coefficient increased by 41% and 36%, respectively. These results indicated that the 3Dvar method improves the forecast accuracy of ^137 Cs concentration.

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.

Determining the source location and release rate are critical in assessing the environmental consequences of atmospheric radionuclide releases, but remain challenging because of the huge multi-dimensional solution space. We propose a generalized spatiotemporally-decoupled two-step framework to reduce the dimension of the solution space in each step and improves the reconstruction accuracy, which is applicable to non-constant releases. The decoupling process is conducted by applying a temporal sliding-window average filter to the observations, thereby reducing the influence of temporal variations in the release rate and ensuring that the features of the filtered data are dominated by the source location. A machine learning model is trained to link these features to the source location, enabling independent source localization. Then the release rate is determined using projected alternating minimization with the L1-norm and total variation regularization algorithm. Validation using SCK-CEN 41Ar experimental data demonstrates that the localization error is less than 1%, and the temporal variations, peak release rate, and total release are reconstructed accurately. The proposed method exhibits higher accuracy and a smaller uncertainty range than the correlation-based and Bayesian methods. Furthermore, it achieves stable performance with different hyperparameters and produces low error levels even with only a single observation site. [ABSTRACT FROM AUTHOR]