BiXiao: An AI-dirven Atmospheric Environmental Forecasting Model with Non-continuous Grids

Abstract. High-precision and efficient atmospheric environmental forecasting is essential for protecting public health and supporting environmental management. However, traditional physics-based numerical models, while mechanistically interpretable, struggle to balance computational cost and forecast accuracy. Although artificial intelligence(AI) has advanced rapidly in meteorological forecasting, most existing AI models are not optimized for atmospheric environmental prediction and rely heavily on gridded inputs, limiting their ability to integrate site observations and their operational applicability. To overcome these limitations, we develop BiXiao, a new-generation AI-based atmospheric environmental forecasting model. BiXiao features a heterogeneous architecture with non-continuous grids, coupling independent meteorological and environmental modules for synergistic use of multi-source data. The meteorological module employs a 3D Swin Transformer(Swin3D) to process structured meteorological fields, while the environmental module directly assimilates discrete station data, enabling operational urban-scale forecasts. Testing in the Beijing-Tianjin-Hebei region shows that BiXiao completes 72-hour forecasts for six major pollutants across all key cities within 30 seconds. Compared with mainstream numerical models(CAMS and WRF-Chem), BiXiao achieves substantially higher computational efficiency and forecast accuracy, particularly during heavy pollution events.