Efficient Weather and Air Quality Forecasts Through AI

Accurate and timely weather forecasts and air quality predictions are essential for designing effective strategies to manage weather-related events and air pollution. These forecasts also play a key role in understanding aerosol-meteorology interactions within weather systems. However, traditional numerical methods, such as chemical transport models (CTMs), are computationally intensive, and their high resource demands limit their practical use in real-time air quality management and weather forecasting. In response to these challenges, we develop a novel approach called DeepCTM4D, which leverages deep learning to replicate CTM simulations, enhancing the computational efficiency of meteorology and air quality modeling in the four-dimensional chemistry space. The DeepCTM4D model is trained to accurately predict atmospheric chemical concentrations based on inputs such as precursor emissions, meteorological factors, and initial conditions. The key advantage of DeepCTM4D lies in its ability to efficiently identify the main drivers of pollution formation and assess how changes in emissions and meteorological conditions influence air quality. The relationships between emissions, meteorol