Vertical Retrieval of AOD using SEVIRI data, Case Study: European Continent

Abstract

Abstract. Accurately determining Aerosol Optical Depth (AOD) across various altitudes with sufficient spatial and temporal resolution is crucial for effective aerosol monitoring, given the significant variations over time and space. While ground-based observations provide detailed vertical profiles, satellite data are crucial for addressing spatial and temporal gaps. This study utilizes profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to estimate vertical AOD values at 1.5, 3, 5, and 10 km layers. These estimations are achieved with spatial and temporal resolutions of 3 km × 3 km and 15 minutes, respectively, over Europe. We employed machine learning models—XGBoost (XGB) and Random Forest (RF)—trained on SEVIRI data from 2017 to 2019 for the estimations. Validation using CALIOP AOD retrievals in 2020 confirmed the reliability of our findings, emphasizing the importance of wind speed (Ws) and wind direction (Wd) in improving AOD estimation accuracy. A comparison between seasonal and annual models revealed slight variations in accuracy, leading to the selection of annual models as the preferred approach for estimating SEVIRI AOD profiles. Among the annual models, the RF model demonstrated superior performance over the XGB model at higher layers, yielding more reliable AOD estimations. Further validation using data from EARLINET stations across Europe in 2020 indicated that the XGB model achieved better agreement with EARLINET AOD profiles, with R² values of 0.81, 0.77, 0.71, and 0.56, and RMSE values of 0.03, 0.01, 0.02, and 0.005, respectively.