Abstract
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 spaceborne 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 spaceborne 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 spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. 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 an 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 discrimination of desert dust from other aerosol types.
Highlights
The spatial and temporal distribution of atmospheric aerosol can be optimally estimated by combining observations and numerical models using data assimilation (DA) techniques
Consistency can be checked when analyses are compared with an observational dataset used for the assimilation
When the datasets are not assimilated, the comparison is performed with independent satellite observations
Summary
The spatial and temporal distribution of atmospheric aerosol can be optimally estimated by combining observations and numerical models using data assimilation (DA) techniques. Representing its vertical structure both within the planetary boundary layer over sources and in the free troposphere in the outflow areas is important for predicting its long-range transport and associated impacts (O’Sullivan et al, 2020) Despite this important role, the dust vertical structure in models and forecasts is poorly constrained by observations (Benedetti et al, 2014). Only lidar measurements, either from space or from ground, can deliver vertical profiles of the atmospheric dust Our work involves both modelling and data assimilation aspects along with the handling of observations and their uncertainty. In our study we assimilate pixels with dust retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue AOD product (Hsu et al, 2019) along with LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles from CALIOP as described in Amiridis et al (2013, 2015) and Marinou et al (2017).
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