Members of the Atmospheric Radiation Measurement (ARM) user facility’s science team are major contributors to radiation and cloud research. Scientists and investigators using ARM publish about 150 peer-reviewed journal articles per year, and ARM data are used in many studies published by other scientific organizations. These documented research efforts represent tangible evidence of ARM’s contribution to advances in almost all areas of atmospheric radiation and cloud research.
Research Highlights
Recent Highlights
Analytical expression of marine stratocumulus virga depth evaluated with observations
15 June 2018
Yang, Fan; Vogelmann, Andrew M.
Supported by:
Research area: Cloud Processes
Virga is drizzle below cloud base that evaporates before reaching the Earth’s surface. Virga results from the interaction of many different cloud processes with the environment. Researchers at Brookhaven National Laboratory and Michigan Technological University obtained virga statistics from observations and derived a new, simple analytical expression for drizzle virga [...]
WRF hindcasts of cold front passages over the ARM Eastern North Atlantic site
13 June 2018
Naud, Catherine M.; Booth, James
Supported by:
Research area: Cloud Processes
Low-level clouds behind extratropical cyclone cold fronts are a significant source of biases in General Circulation Models (GCMs), and the ARM East North Atlantic (ARM-ENA) site offers a great opportunity to address this problem. In order to isolate and compare the cloud-scale physics involved in low-level clouds with observations from [...]
Global, remotely sensed ice production in mid-level stratiform mixed-phase clouds
12 June 2018
Zhang, Damao; Vogelmann, Andrew M.
Supported by:
Research area: Cloud Processes
Ice particle production in supercooled liquid clouds has a dramatic impact on the clouds' radiative properties, but it is not well understood and parameterized in models. Mid-level stratiform mixed-phase clouds have a simple dynamic environment and straightforward ice growth trajectory that enables using radar reflectivity measurements to quantitatively infer variations [...]