Assimilating spaceborne lidar dust extinction can improve dust forecasts

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.

A SmallSat Concept to Resolve Diurnal and Vertical Variations of Aerosols, Clouds, and Boundary Layer Height

“…Case studies assimilating lidar vertical profiles show a 20-40% improvement in the simulation of volcanic plume distributions, aerosol backscatter, aerosol number concentration, and surface mass concentrations (Escribano et al 2022;Ye et al 2021;El Amraoui et al 2020;Hughes et al 2016;Sekiyama et al 2010;Zhang et al 2011). Operational and quasi-operational global aerosol models do not currently assimilate lidar vertical profiles since data are not available in NRT and the data lacks the spatiotemporal information needed to improve model predictions on a global scale (Benedetti et al 2018).…”

Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts

“…Also, work is being done to allow the chemistry to be solved using runtime configuration approaches (Dawson et al, 2021) and exploiting GPUs heterogeneous architectures (Guzmán-Ruiz et al, 2020). (Hunt et al, 2007;Miyoshi and Yamane, 2007;Schutgens et al, 2010;Di Tomaso et al, 2017;Escribano et al, 2022) coupled to the model through I/O routines, and which requires the model to be run in an ensemble mode. These complex simulations are handled through the Autosubmit workflow manager (Manubens-Gil et al, 2016;Uruchi et al, 2021), which allows also to process the necessary input files, and to post-process and archive the model outputs in an easy way.…”

“…The model is the reference forecast system of the Barcelona Dust Forecast Center (https://dust.aemet.es/), and the Regional Center for Northern Africa, the Middle East and Europe (NAMEE) of the Sand and Dust Storms Warning Advisory and Assessment System (SDS-WAS) of the World Meteorological Organization (WMO). Moreover, MONARCH has the capability to improve dust estimates through data assimilation techniques, both using column integrated (Di Tomaso et al, 2017;Di Tomaso et al, 2022) and vertically resolved (Escribano et al, 2022) satellite dust retrievals, and it has been recently used to derive a dust regional reanalysis for the NAMEE domain (Di Tomaso et al, 2022) with unprecedented high resolution.…”

Modeling coarse and giant desert dust particles

“…2009) on the CALIPSO level-2 version 4 products (Winker et al, 2009). The LIVAS pure-dust product has been used in a variety of dust-oriented studies, including the investigation of the dust sources and the seasonal transition of the dust transport pathways (Marinou et al, 2017;Proestakis et al, 2018), the evaluation of the performance of atmospheric and dust transport models (e.g., Tsikerdekis et al, 2017;Solomos et al, 2017;Konsta et al, 2018), the evaluation of new satellitebased products (e.g., Georgoulias et al, 2016;Chimot at al., 2017;Georgoulias et al, 2020;Gkikas et al, 2021), and dust assimilation experiments (Escribano et al, 2022). Herein, the LIVAS pure-dust extinction product is used for the assessment of the simulated dust vertical patterns.…”