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Title: Overview of the HI-SCALE Field Campaign: A New Perspective on Shallow Convective Clouds

Journal Article · · Bulletin of the American Meteorological Society
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  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Paul Scherrer Inst. (PSI), Villigen (Switzerland)
  3. Univ. of Washington, Seattle, WA (United States)
  4. Brookhaven National Lab. (BNL), Upton, NY (United States)
  5. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Harbin Inst. of Technology (China)
  6. National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
  7. Univ. of Washington, Seattle, WA (United States); Univ. of Eastern Finland, Kuopio (Finland)
  8. University of California, Irvine, Irvine, California
  9. Brookhaven National Lab. (BNL), Upton, NY (United States); Washington Univ., St. Louis, MO (United States)
+ Show Author Affiliations

Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land-atmosphere interactions and aerosol-radiation-cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To face these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Lastly, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.

Research Organization:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0012704
OSTI ID:
1530519
Report Number(s):
BNL-211828-2019-JAAM
Journal Information:
Bulletin of the American Meteorological Society, Vol. 100, Issue 5; ISSN 0003-0007
Publisher:
American Meteorological SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 34 works
Citation information provided by
Web of Science

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Cited By (3)

The Impact of Variable Land‐Atmosphere Coupling on Convective Cloud Populations Observed During the 2016 HI‐SCALE Field Campaign journal August 2019
TDCIMS background-corrected ion signals
  • Lawler, Michael
  • Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) https://doi.org/10.5439/1546801
dataset January 2019
TDCIMS background-corrected ion signals
  • Lawler, Michael
  • Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States) https://doi.org/10.5439/1546802
dataset January 2016

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54 ENVIRONMENTAL SCIENCES