Cloud vertical distribution from combined surface and space radar/lidar observations at two Arctic atmospheric observations Journal Article uri icon



  • Abstract. Detailed and accurate vertical distributions of cloud properties (such as cloud fraction, cloud phase, and cloud water content) and their changes are essential to accurately calculate the surface radiative flux and to depict the mean climate state. Surface- and space-based active sensors including radar and lidar are ideal to provide this information due to their superior capability to detect clouds and retrieve cloud microphysical properties. In this study, we compared the annual cycles of cloud property vertical distributions from satellite active sensors and surface-based active sensors at two Arctic atmospheric observation stations, Barrow and Eureka. We used this data to identify the sensors’ respective strengths and limitations and to develop a blended cloud property vertical distribution by combining both sets of observations. Results show that surface-based observations offer a more detailed cloud property vertical distribution from the surface up to 11 km above mean sea level (AMSL) with limitations in the middle and high altitudes; the annual mean total cloud fraction from space-based observations see 25–40 % fewer clouds below 0.5 km than that from surface-based observations, and space-based observations also show much less ice cloud and mixed phase cloud, and slightly greater liquid cloud from the surface to 1 km; space-based observations show comparable cloud fraction between 1 km and 2 km AMSL, and greater cloud fraction above 2 km AMSL than that from surface-based observations. The blended product combines the strength of both products to provide a more reliable annual cycle of cloud property vertical distribution annual cycle from the surface to 11 km AMSL. This information can be valuable for deriving an accurate surface radiative budget in the Arctic and for cloud parameterization evaluation in weather and climate models.;

publication date

  • January 12, 2017

has restriction

  • green

Date in CU Experts

  • November 7, 2020 9:00 AM

Full Author List

  • Liu Y; Shupe MD; Wang Z; Mace G

author count

  • 4

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