Analysis of Dust Aerosols in the PMAp Satellite Climate Data Record&#160;
Authors: Sundström Anu-Maija , Doutriaux-Boucher Marie , Fierli Federico , Jafariserajehlou Soheila , Clarisse Lieven , Whitburn Simon , Cacciari Alessandra , Czyzewska Dominika , Mantovani Simone , Cavicchi Mario , Fougnie Bertrand 
Published: March 2024

Satellites provide a powerful tool to monitor dust at global scale, both at near real time as well as over a longer time period. In this work we introduce a new satellite-based dataset on dust, that is derived from the&#160; Polar Multi-sensor Aerosol optical properties product (PMAp) Climate Data Record (CDR). The first PMAp CDR was released by EUMETSAT in September 2022 (http://doi.org/10.15770/EUM_SEC_CLM_0053). It provides 13 years (2007-2019) of global observations on Aerosol Optical Depth (AOD) at 550 nm, and aerosol type, including dust. The PMAp aerosol properties are derived using multi-instrument approach, where simultaneous observations from the Global Ozone Monitoring Experiment-2 (GOME-2), Infrared Atmospheric Sounding Interferometer (IASI), and Advanced Very High Resolution Radiometer (AVHRR) onboard Metop-A and Metop-B satellites are exploited. The PMAp retrieval algorithm and the synergy concept&#160; is described in detail by Grzegorski &#160;et al. (2022).Level 2 PMAp data provide pixel-level classification of aerosol types. Dust detection in the multi-instrument approach is based on IASI observations using method developed by Clarisse et al. (2013), while AOD at 550 nm is retrieved using GOME-2 measurements. &#160;The PMAp aerosol type classification is used to extract dust-dominated pixels from the CDR dataset and to define dust-related AOD. Comparisons against ground-based AERONET observations over Sahara and the Saharan outflow area show a slight positive bias of about 0.02 for PMAp dust AOD at 550 nm, whereas the positive bias tends to increase at AERONET stations in the Asian continent.&#160; Results also show that PMAp dust AOD generally catches well the dynamic variations of aerosol loading at the AERONET stations. To assess more broadly the spatial and temporal variation of the PMAp dust AOD at continental scale, comparisons against other existing satellite-based dust products, including IASI dust AOD provided by the Free University of Brussels (ULB) (Clarisse et al., 2019) and the ModIs Dust AeroSol (MIDAS) global dataset (Gkikas et al., 2021) will be carried out.&#160;&#160;Acknowledgements: This work is supported by EUMETSAT Copernicus User Guidance project.&#160;References:Grzegorski, M., Poli, G., Cacciari, A., Jafariserajehlou, S., Holdak, A., Lang, R.,Vazquez-Navarro, M., Munro, R., and Fougnie, B.: Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation. Remote Sens. 2022, 14, 85. https://doi.org/10.3390/rs14010085.Clarisse, L., Coheur, P.F., Prata, F., Hadji-Lazaro, J., Hurtmans, D., and Clerbaux, C.: A unified approach to infrared aerosol remote sensing and type specification. Atmos. Chem. Phys. 2013, 13, 2195&#8211;2221,https://doi.org/10.5194/acp-13-2195-2013.Clarisse, L., Clerbaux, C., Franco, B., Hadji-Lazaro, J., Whitburn, S., Kopp, A. K., et al.: A decadal data set of global atmospheric dust retrieved from IASI satellite measurements. J. Geophys. Res., 2019, 124, 1618&#8211; 1647, https://doi.org/10.1029/2018JD029701.Gkikas, A., Proestakis, E., Amiridis, V., Kazadzis, S., Di Tomaso, E., Tsekeri, A., Marinou, E., Hatzianastassiou, N., and P&#233;rez Garc&#237;a-Pando, C.: ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set, Atmos. Meas. Tech., 2021, 14, 309&#8211;334, https://doi.org/10.5194/amt-14-309-2021.