The role of observation nudging in high-resolution simulations of the 2016 Tunisian dust storm event

This study investigates a severe dust storm event in Tunisia during March 2016, utilizing high-resolution 1 km Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) simulations, enhanced with observation nudging assimilation techniques. The research aims to evaluate the atmospheric simulations method impacts, especially about severe weather events that present significant challenges to meteorological modelling. The meteorological models were validated against observed data for temperature and wind, establishing a robust framework for assessing model performance. The reconstructed meteorology demonstrated correlations exceeding 90% for temperature and wind speed, and 50% for wind direction. Additionally, the effectiveness of observation nudging (ON) was specifically examined in terms of its impact on the predictions of aerosol optical depth at 550 nm (AOD550) using Aerosol Robotic NETwork (AERONET) data, as well as particulate matter concentrations, in conjunction with Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA2) reanalysis datasets. The findings underscore the pivotal role of observation nudging in enhancing model predictions by closely aligning them with observed atmospheric conditions. This study demonstrates that strategic implementation of objective analysis can significantly reduce broad-scale meteorological and air dispersion errors, thus improving the reliability and accuracy of atmospheric chemistry models during complex dust storm scenarios.