Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-309-2021
https://doi.org/10.5194/amt-14-309-2021
Research article
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15 Jan 2021
Research article | Highlight paper |  | 15 Jan 2021

ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set

Antonis Gkikas, Emmanouil Proestakis, Vassilis Amiridis, Stelios Kazadzis, Enza Di Tomaso, Alexandra Tsekeri, Eleni Marinou, Nikos Hatzianastassiou, and Carlos Pérez García-Pando

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Cited articles

Ackerman, S. A.: Remote sensing aerosols using satellite infrared observations, J. Geophys. Res., 102, 17069–17079, https://doi.org/10.1029/96JD03066, 1997. 
Alizadeh Choobari, O., Zawar-Reza, P., and Sturman, A.: Low level jet intensification by mineral dust aerosols, Ann. Geophys., 31, 625–632, https://doi.org/10.5194/angeo-31-625-2013, 2013. 
Amiridis, V., Wandinger, U., Marinou, E., Giannakaki, E., Tsekeri, A., Basart, S., Kazadzis, S., Gkikas, A., Taylor, M., Baldasano, J., and Ansmann, A.: Optimizing CALIPSO Saharan dust retrievals, Atmos. Chem. Phys., 13, 12089–12106, https://doi.org/10.5194/acp-13-12089-2013, 2013. 
Anderson, T. L., Wu, Y., Chu, D. A., Schmid, B., Redemann, J., and Dubovik, O.: Testing the MODIS satellite retrieval of aerosol fine-mode fraction, J. Geophys. Res., 110, D18204, https://doi.org/10.1029/2005JD005978, 2005. 
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We present the development of the MIDAS (ModIs Dust AeroSol) data set, providing daily dust optical depth (DOD; 550 nm) at a global scale and fine spatial resolution (0.1° x 0.1°) over a 15-year period (2003–2017). 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.