Assimilating spaceborne lidar dust extinction improves dust forecasts

Abstract

Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from space-borne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating space-borne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the space-borne lidar profiles upon the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of two months over Northern Africa, the Middle East and Europe. We assimilate DOD derived from VIIRS/SUOMI-NPP Deep Blue, and for the first time CALIOP-based LIVAS pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AERONET Sun photometers and ground-based lidar dust extinction profiles from field campaigns (CyCARE and Pre-TECT). Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discriminating desert dust from other aerosol types.