Journal Article

Physics‐Guided CNN‐LSTM Model With Multi‐Head Attention for Aerosol Optical Depth Prediction

Yujun, Tan, Zhongrong, Shi, Yarong, Li, Yadong, Yang, Xiancun, Zhou, Shengnan, Zhou, Jing, Zhang, Zeyang, Liu

2026

Overview

Accurate aerosol optical depth (AOD) prediction remains challenging due to complex aerosol‐radiation interactions and highly variable spatio‐temporal patterns. Three critical scientific issues motivate this work: understanding whether and how physical principles can enhance deep learning predictions, identifying which aerosol properties most strongly govern AOD variations, and improving the prediction of extreme AOD events critical for air quality management. Herein, utilizing MERRA‐2 reanalysis data (1980–2024) over the Huaihe River Basin in eastern China, a Physics‐Guided deep learning framework is presented for Aerosol Optical Depth (AOD) prediction. The model proposed integrates Convolutional Neural Networks (CNN), Long Short‐TermMemory (LSTM) networks, and multi‐head attention mechanisms to capture both spatio‐temporal features and physical relationships of aerosol properties. Three key aspects are involved: First, a hybrid deep learning model is developed and evaluated, which combines CNNs for spatial correlation extraction, bidirectional LSTM for temporal dependency modeling, and multi‐head attention for feature interaction learning. Second, a comprehensive feature importance analysis is conducted by examining the relationships between different aerosol properties (mass concentration, scattering coefficient, and Ångström exponent) and AOD prediction, offering physical insights into the model's decision‐making process. Third, a specialized approach is proposed for extreme AOD event prediction, focusing on early detection and accurate forecasting of high‐AOD episodes. Overall, the results demonstrate the model's efficacy in capturing both regular AOD variations and extreme events, with the Physics‐Guided architecture showing superior performance compared to traditional methods. This integrated approach enhances AOD prediction accuracy and deepens insights into aerosol‐radiation interactions, thereby improving atmospheric monitoring and air quality forecasting. While MERRA‐2 has inherent temporal delays, this framework provides valuable capabilities for historical trend analysis, numerical model validation, and can be readily adapted for real‐time applications through transfer learning with satellite observations. Plain Language Summary Aerosol Optical Depth (AOD) is a crucial measure of how much sunlight is blocked by particles in the air, affecting both climate and air quality. Traditional methods for predicting AOD often struggle with accuracy and efficiency. This study develops a new artificial intelligence model that combines physical principles with deep learning techniques to predict AOD over the Huaihe River Basin in eastern China. Our model shows significant improvements in prediction accuracy, particularly in identifying extreme pollution events. By analyzing different types of aerosol properties, we found that the way particles scatter light is more important for predictions than their mass. The model performs better in summer than in winter, likely due to winter's more complex weather conditions. This improved prediction system could help better forecast air quality and provide early warnings for severe pollution events, benefiting public health and environmental management. Key Points A hybrid deep learning model is developed and evaluated, for feature interaction learning A comprehensive feature importance analysis is conducted by examining the relationships between different aerosol properties A specialized approach is proposed for extreme AOD event prediction

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: aerosol optical depth / Aerosols / Air quality / Atmospheric monitoring / Atmospheric sciences / Datasets / Deep learning / Mathematical models / multi‐head attention mechanism / Optical analysis / Outdoor air quality / Physics / physics‐guided deep learning / Radiation / River basins / Scattering coefficient