Abstract. North Africa and the Middle East encompass the most active dust sources on the planet. Due to the limited availability of ground-based aerosol observations across the deserts, spaceborne retrievals represent the most reliable source of information for monitoring dust particles over these vast areas. In the current study, we present a synergistic approach incorporating aerosol retrievals acquired by active (CALIOP) and passive (POLDER-3, MODIS) instruments mounted on satellites of the A-Train constellation. Our main objective is to dynamically (in terms of space and time) estimate the dust lidar ratio (LR) at 532 nm throughout a 12-year period (2006–2017) by collocating columnar aerosol optical depth observations (POLDER-3/GRASP, MIDAS) and vertically resolved dust aerosol profiles obtained by CALIPSO. According to our findings, the derived dust LRs reveal a clear spatial variability. The highest LRs are found over major Saharan source regions such as the Bodélé Depression and the Libyan Desert, while moderate to low values dominate the Arabian Peninsula. Enhanced values also appear across Central Asia, particularly over the Karakum and Kyzylkum deserts. The corresponding uncertainty fields demonstrate that LR estimates are most robust over regions with a larger number of dust cases, whereas higher uncertainties occur along transition zones – such as the Sahel and parts of Central Asia – where dust mixing or aerosol-type misclassification increases retrieval variability. A key objective of this work is to establish a robust methodology for deriving aerosol-speciated LRs using synergies between active and passive observations. Although focused here on dust over North Africa and the Middle East, the same framework can be readily applied to other CALIPSO aerosol subtypes – such as marine, polluted dust, or smoke – when implemented over regions where these aerosol types predominate. The advent of the EarthCARE satellite mission, along with the incorporation of new aerosol models into the forthcoming CALIPSO Version 5 aerosol retrieval algorithm, will serve as a reference for our calculations. In this context, our findings also highlight that a synergy of multisensor aerosol products with modelling tools can enhance the spatiotemporal representation of aerosol properties, such as the LR, further improving retrieval utility.
Moustaka et al. (Wed,) studied this question.