Remote Sensing (Dec 2021)

Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean

  • Eleni Marinou,
  • Kalliopi Artemis Voudouri,
  • Ioanna Tsikoudi,
  • Eleni Drakaki,
  • Alexandra Tsekeri,
  • Marco Rosoldi,
  • Dragos Ene,
  • Holger Baars,
  • Ewan O’Connor,
  • Vassilis Amiridis,
  • Charikleia Meleti

DOI
https://doi.org/10.3390/rs13245001
Journal volume & issue
Vol. 13, no. 24
p. 5001

Abstract

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In this work, collocated lidar–radar observations are used to retrieve the vertical profiles of cloud properties above the Eastern Mediterranean. Measurements were performed in the framework of the PRE-TECT experiment during April 2017 at the Greek atmospheric observatory of Finokalia, Crete. Cloud geometrical and microphysical properties at different altitudes were derived using the Cloudnet target classification algorithm. We found that the variable atmospheric conditions that prevailed above the region during April 2017 resulted in complex cloud structures. Mid-level clouds were observed in 38% of the cases, high or convective clouds in 58% of the cases, and low-level clouds in 2% of the cases. From the observations of cloudy profiles, pure ice phase occurred in 94% of the cases, mixed-phase clouds were observed in 27% of the cases, and liquid clouds were observed in 8.7% of the cases, while Drizzle or rain occurred in 12% of the cases. The significant presence of Mixed-Phase Clouds was observed in all the clouds formed at the top of a dust layer, with three times higher abundance than the mean conditions (26% abundance at −15 °C). The low-level clouds were formed in the presence of sea salt and continental particles with ice abundance below 30%. The derived statistics on clouds’ high-resolution vertical distributions and thermodynamic phase can be combined with Cloudnet cloud products and lidar-retrieved aerosol properties to study aerosol-cloud interactions in this understudied region and evaluate microphysics parameterizations in numerical weather prediction and global climate models.

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