Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations

We present a supervised machine learning (ML) approach to improve the accuracy of the regional horizontal distribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry transport model over North Africa and the Arabian Peninsula using Moderate Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. Our method produces daily AOD maps with enhanced precision and full spatial domain coverage, which is particularly relevant for regions with a high aerosol abundance, such as the Sahara Desert, where there is a dramatic lack of ground-based measurements for validating chemistry transport simulations. We use satellite observations and some geophysical variables to train four popular regression models, namely multiple linear regression (MLR), random forests (RF), gradient boosting (XGB), and artificial neural networks (NN). We evaluate their performances against satellite and independent ground-based AOD observations. The results indicate that all models perform similarly, with RF exhibiting fewer spatial artifacts. While the regression slightly overcorrects extreme AODs, it remarkably reduces biases and absolute errors and significantly improves linear correlations with respect to the independent observations. We analyze a case study to illustrate the importance of the geophysical input variables and demonstrate the regional significance of some of them.

Automated summary

We propose a supervised machine learning approach to improve the accuracy of CHIMERE chemistry transport model in simulating the regional distribution of aerosol optical depth (AOD) over North Africa and the Arabian Peninsula. Our method utilizes MODIS AOD satellite observations to generate daily AOD maps with enhanced precision and full spatial domain coverage, which is crucial for regions with limited ground-based measurements. Four popular regression models, namely MLR, RF, XGB, and NN, are trained using satellite observations and geophysical variables, and their performances are evaluated against satellite and independent ground-based AOD observations. The results show that all models perform similarly, with RF exhibiting fewer spatial artifacts while slightly overcorrecting extreme AOD values.