11th Conference on Atmospheric Radiation and the 11th Conference on Cloud Physics

Wednesday, 5 June 2002
Arctic Cloud Properties Derived from Multispectral MODIS and AVHRR Data
Douglas A. Spangenberg, AS&M, Inc., Hampton, VA; and P. Heck, P. Minnis, Q. Trepte, S. Sun-Mack, T. Uttal, and X. Dong
Poster PDF (655.8 kB)
Understand the impact of clouds on Earth's climate requires improved measurements of their microphysical properties. In the Arctic there is minimal contrast between the clouds and the background snow surface. It is often difficult to determine cloud amount and retrieve microphysical properties using satellite data. This paper examines a variety of methods to derive cloud properties from various combinations of multispectral satellite data. Variants of the Clouds and the Earth's Radiant Energy System (CERES) cloud mask are used to discriminate clouds from the clear-sky background. A visible infrared near-infrared technique (VINT), a visible-infrared solar-infrared split-window technique (VISST), a near-infrared infrared solar-infrared technique (NIST), and a solar-infrared infrared split-window technique (SIST) are used to retrieve cloud microphysical properties during daytime over the Arctic from various satellite imagers. The 1-km 0.65 (visible), 1.6 (near-infrared), 3.7 (solar-infrared), 11.0 (infrared), and 12.0 (split-window) Ám bands on the Terra Moderate-Resolution Imaging Spectrometer (MODIS) are used to derive cloud phase, particle size, optical depth, height, temperature, and ice or liquid water path using all three methods. For the National Oceanic and Atmospheric Administration (NOAA)-12,14,16 Advanced Very High Resolotion Radiometer (AVHRR) 1km data, the VINT, VISST, and SIST algorithms are applied. Also, a modification to the VISST algorithm that uses the 0.91 micron band is used to derive the cloud properties. The retrievals are performed over the Surface Heat Budget of the Arctic Ocean (SHEBA) and ARM North Slope of Alaska (NSA) sites. The satellite-derived cloud microphysical properties are then validated by comparing them with cloud properties derived from the NOAA Environmental Technologies Laboratory (ETL) cloud radar.

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