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Atmospheric Sciences & Global Change
Research Highlights

August 2018

Mission Possible: Estimating Cloud Area from Sky Images

A new method provides a previously unavailable data set that could help improve the representation of shifty shallow cumulus clouds in atmospheric models.

Cloud-based complex land-atmospheric interactions
Complex land-atmosphere interactions, such as the exchange of water and energy, often involve clouds and the combination of their size and shape—or cloud equivalent diameters. Enlarge Image.

The Science

Complex interactions between Earth's surface and the atmosphere, such as the exchange of water and energy, often involve clouds and the combination of their size and shape—or cloud equivalent diameters. When it comes to shallow cumuli—puffy, cotton ball-like clouds with rapidly evolving cloud equivalent diameters—available ground-based and satellite sensors fall short of capturing these changes at the level of detail required to assess and improve the representation of shallow cumuli in atmospheric models.

Researchers from the U.S. Department of Energy's (DOE) Pacific Northwest National Laboratory, Lewis & Clark College, and the Cooperative Institute for Research in Environmental Sciences at the University of Colorado, Boulder, teamed up to develop a method for obtaining the required information on cloud equivalent diameter with high time resolution. They introduced a simple, computationally inexpensive method using low-cost, ground-based sky images.

The Impact

The new method provides a previously unavailable data set for process studies of the convective boundary layer and for evaluation of shallow cumuli growth, maturation, and dissipation in cloud-resolving models. Because many atmospheric research sites worldwide deploy low-cost, ground-based sky imagers, the research team plans to apply the new method to existing and future studies at different locations.


Shallow cumuli are usually small (less than 1 kilometer across), but their complex, rapidly changing shape and size make it notoriously difficult to track their evolution with standard, high-cost ground-based and satellite observations, and subsequently represent them accurately in atmospheric models. However, visual observations of shallow cumuli are very straightforward, as these clouds are very easy to see floating in the sky overhead. Taking advantage of the strong visual contrast between the clouds and blue sky, the research team used available ground-based sky images to estimate the area of individual clouds at a previously unachievable rate.

Researchers looked at images obtained from a total sky imager and complementary information on cloud base height provided by lidar measurements to estimate the cloud equivalent diameter over a wide range of cloud sizes (about 0.01 to 3.5 kilometers) with 30-second time resolution. The team demonstrated the feasibility of the new method by comparing cloud area distributions obtained from ground-based and satellite images collected from 2004 to 2017 at the DOE Atmospheric Radiation Measurement (ARM) user facility's Southern Great Plains atmospheric observatory. The new method provides unique cloud area information required for better understanding of the lifetime of shallow cumuli.

Because of its simplicity and the worldwide deployment of sky imagers, the method has the potential to become widely used for important climate-related research applications.


Sponsors: The U.S. Department of Energy's Atmospheric System Research Grant DE-SC0016084 and KP1701000/57131 and M.J. Murdock Charitable Trust Grant 320-1685 supported this research.

Research Area: Climate and Earth Systems Science

Research Team: Jessica M. Kleiss and Erin A. Riley, Lewis & Clark College; Charles N. Long, Cooperative Institute for Research in Environmental Sciences; and Laura D. Riihimaki, Larry K. Berg, Victor R. Morris, and Evgueni Kassianov, PNNL

Reference: J.M. Kleiss, E.A. Riley, C.N. Long, L.D. Riihimaki, L.K. Berg, V.R. Morris, E. Kassianov, "Cloud Area Distributions of Shallow Cumuli: A New Method for Ground-Based Images." Atmosphere 9(7), 258 (2018). []

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