Enhancing WRF-Chem Dust Predictions Through Assimilation of Satellite-Based MIDAS Dust Optical Depth Data

Abstract

Accurately modelling the distribution of mineral dust in the atmosphere is a complex task that poses significant challenges. Dust aerosols influence critical atmospheric processes, such as the radiation balance and nutrient deposition, making their study essential for understanding Earth’s dynamics. However, the inherent variability and complexity of dust emissions, transport, and deposition contribute to large uncertainties in aerosol numerical predictions.This study combines advanced numerical modelling with satellite observations, to tackle these challenges and enhance dust forecasts over the Mediterranean region. We use the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate dust activity during September 2021. The simulations are improved by assimilating satellite-based dust optical depth data from the MIDAS (Mineral Dust Aerosol Satellite) product, which provides observations at a spatial resolution of 1°×1°.By integrating MIDAS data, we significantly refine the model’s dust predictions, aligning them more closely with observed conditions. The improved forecasts demonstrate clear benefits, especially for applications in air quality management and solar energy optimization. Additionally, a more accurate representation of dust aerosol provides a solid base for studying aerosol-cloud interactions.These findings highlight the value of blending high-quality observational datasets with sophisticated modelling approaches to address uncertainties in dust aerosol studies.Acknowledgements. This research work has been supported by the EU-funded programme CiROCCO under Grant Agreement No 101086497. Α part of this work has been supported by AIRSENSE (Aerosol and aerosol cloud Interaction from Remote SENSing Enhancement) project, funded from the European Space Agency under Contract No. 4000142902/23/I-NS.