With the rapid development of artificial intelligence, data-driven approaches are reshaping atmospheric science research. In areas such as numerical weather prediction, satellite remote sensing, and cloud process analysis, deep learning and generative models have shown clear advantages over traditional methods. This presentation introduces recent research progress of our group at the intersection of artificial intelligence and atmospheric science, highlighting representative applications in meteorology and satellite remote sensing. The talk covers atmospheric models developed by our group, satellite cloud image nowcasting models, visible-band image generation based on geostationary satellites, and long-term studies on AI-based all-day global cloud property retrieval. The presentation aims to demonstrate the frontier applications of artificial intelligence in atmospheric science and to explore its potential for high-resolution weather forecasting and intelligent remote sensing