PNNL

The Science

Different types of clouds have different radiative forcing. This means researchers quantifying the role of clouds on the global energy budget, as well as regional or global water cycles, need accurate classification of cloud types for Earth system models. A team of researchers, including scientists at the U.S. Department of Energy’s Pacific Northwest National Laboratory, used 13 years of data to develop an automated algorithm that identifies seven different cloud types at the Atmospheric Radiation Measurement (ARM) site in the U.S. Southern Great Plains. The algorithm identified daily and seasonal cycles for different cloud types. The team also developed a method to identify fair-weather shallow cumuli clouds, a type of low cloud that is currently challenging to simulate in climate models.

The Impact

Researchers often want to only study certain meteorological conditions, such as days with deep convection. The new cloud type analysis, called cldtype, assists researchers by narrowing down their search for particular cloud types. The fair-weather shallow cumuli cloud identification program, called shallowcumulus, further subcategorizes the low clouds in cldtype to identify fair-weather cumuli. This was motivated by the Large-eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity which focusses on days with these clouds at the ARM site in the Southern Great Plains. Overall, having the different cloudy periods categorized makes it easier for researchers to take advantage of ARM’s large suite of instrumentation.

Summary

The research team used cloud observations from 1997 to 2009 collected at the ARM site in the U.S. Southern Great Plains to generate an automated algorithm that classifies clouds into seven types: low clouds, congestus, deep convection, altocumulus, altostratus, cirrostratus/anvil, and cirrus. This classification was based on the physical qualities of cloud top, cloud base, and physical thickness of cloud layers measured with millimeter-wavelength cloud radar and micropulse lidar.

Additionally, the team developed a second algorithm to identify fair-weather shallow cumulus events using cloud fraction information collected from 2000 to 2008 with a total-sky imager and ceilometer. The events identified automatically were in close agreement with fair-weather shallow cumulus events identified manually. The automated analysis only missed six cases out of 70 possible events during the spring to summer seasons (May–August).

PNNL Contact

Bill Gustafson, Pacific Northwest National Laboratory, William.Gustafson@pnnl.gov

Funding

This research was supported by the Office of Biological and Environmental Research (BER) of the U.S. Department of Energy (DOE) as part of the Atmospheric Radiation Measurement (ARM) facility, an Office of Science user facility and by the National Research Foundation of Korea grant funded by the South Korean government. Data were obtained from the ARM facility, a U.S. DOE Office of Science user facility sponsored by the Office of BER. Larry Berg and Yunyan Zhang were supported by the Atmospheric System Research program in BER.