Development of a three-dimensional variational data assimilation system for ¹³⁷Cs based on WRF-Chem model and applied to the Fukushima nuclear accident

Abstract

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 137Cs based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The distribution of 137Cs after the Fukushima nuclear accident in Japan on 15 March 2011 was analysed. The 137Cs background field at 06:00 UTC was assimilated using a 3Dvar system and surface observational data to optimise the 137Cs analysis field. Compared with the background field, the root mean square error (RMSE) and mean bias in the 137Cs 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-hour forecast experiments were conducted to gauge the advancement in 137Cs 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 137Cs concentration.