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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-222
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-222
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 22 Jun 2020

Submitted as: research article | 22 Jun 2020

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This preprint is currently under review for the journal AMT.

ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset

Antonis Gkikas1, Emmanouil Proestakis1, Vassilis Amiridis1, Stelios Kazadzis2,3, Enza Di Tomaso4, Alexandra Tsekeri1, Eleni Marinou5, Nikos Hatzianastassiou6, and Carlos Pérez García-Pando4,7 Antonis Gkikas et al.
  • 1Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Athens, 15236, Greece
  • 2Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center, Switzerland
  • 3Institute of Environmental Research and Sustainable Development, National Observatory of Athens, Greece
  • 4Earth Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain
  • 5Deutsches Zentrum für Luft-und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 6Laboratory of Meteorology, Department of Physics, University of Ioannina, Ioannina, Greece
  • 7ICREA, Passeig Lluís Companys 23, 08010Barcelona, Spain

Abstract. Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD at 550 nm) at global scale and fine spatial resolution (0.1° × 0.1°) over a decade (2007–2016). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and CALIOP, respectively, are taken into account for the estimation of the total DOD uncertainty (including measurement and sampling uncertainties). MERRA-2 dust fractions are in very good agreement with CALIOP column-integrated dust fractions across the dust belt, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and CALIOP DODs strongly agree when it comes to annual and seasonal spatial patterns; however, deviations of dust loads' intensity are evident and regionally dependent. Overall, MIDAS is well correlated with ground-truth AERONET-derived DODs (R = 0.882), only showing a small negative bias (−0.009 or −5.307 %). Among the major dust areas of the planet, the highest R values (up to 0.977) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.

Antonis Gkikas et al.

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ModIs Dust AeroSol (MIDAS) A. Gkikas, E. Proestakis, V. Amiridis, S. Kazadzis, E. Di Tomaso, A. Tsekeri, E. Marinou, N. Hatzianastassiou, and C. Pérez García-Pando https://doi.org/10.5281/zenodo.3719222

Antonis Gkikas et al.

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Short summary
We present the development of the MIDAS (ModIs Dust AeroSol) dataset providing daily dust optical depth (DOD at 550 nm) at global scale and fine spatial resolution (0.1° x 0.1°) over a decade (2007-2016). It has been developed via the synergy of MODIS-Aqua and MERRA-2 data while CALIOP and AERONET retrievals are used for its assessment. MIDAS upgrades existing dust observational capabilities and it is suitable for dust climatological studies, model evaluation and data assimilation.
We present the development of the MIDAS (ModIs Dust AeroSol) dataset providing daily dust...
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