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Title: Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Field Campaign Report

Technical Report ·
DOI:https://doi.org/10.2172/1574024· OSTI ID:1574024
 [1];  [2];  [3];  [4];  [2];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [6];  [12];  [6];  [13];  [14];  [12]
  1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Utah, Salt Lake City, UT (United States)
  2. Univ. of Illinois at Urbana-Champaign, IL (United States)
  3. Univ. of Buenos Aires (Argentina)
  4. Universidad Nacional de Córdoba (Argentina)
  5. Univ. of Oklahoma, Norman, OK (United States)
  6. Colorado State Univ., Fort Collins, CO (United States)
  7. Univ. of Utah, Salt Lake City, UT (United States)
  8. National Center for Atmospheric Research (NCAR), Boulder, CO (United States)
  9. Univ. of Washington, Seattle, WA (United States)
  10. Brookhaven National Laboratory (BNL), Upton, NY (United States)
  11. Stony Brook Univ., NY (United States)
  12. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
  13. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  14. Univ. of Colorado, Boulder, CO (United States)
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General circulation models and downscaled regional models exhibit persistent biases in deep convective initiation location and timing, cloud top height, stratiform area and precipitation fraction, and anvil coverage (e.g., Del Genio 2012, Del Genio et al. 2012, Hohenegger and Stevens 2013, Song et al. 2013). Despite important impacts on the distribution of atmospheric heating, moistening, momentum, and precipitation (e.g., Hartmann et al. 1984, Fritsch et al. 1986, Houze 1989, 2004, Donner et al. 2001, Del Genio and Kovari 2002, Schumacher et al. 2004, Nesbitt et al. 2006, Storelvmo 2012), nearly all climate models fail to represent mesoscale convective organization (Del Genio 2012), while system evolution is not represented at all (Ovchinnikov et al. 2006). Recent advances in cumulus parameterization coupled with increasing model resolution have improved predictions, but even relatively higher-resolution models without parameterized deep convection have some persistent kinematic and microphysical biases (e.g., Blossey et al. 2007, Matsui et al. 2009, Luo et al. 2010, Lang et al. 2011, Varble et al. 2011, Fridlind et al. 2012, Hagos et al. 2014, Varble et al. 2014a-b, Fan et al. 2017, Stanford et al. 2017, Han et al. 2019). To improve representation of convective systems in models requires adequate characterization of their predictability as a function of environmental conditions. Because of the significant sensitivities of deep convective initiation, intensity, lifetime, propagation, and mesoscale convective organization to many factors including multi-scale atmospheric circulations, ambient environmental stability, humidity, wind distributions, and cloud microphysical processes, this characterization relies on comprehensively observing many cases of convective initiation, non-initiation, organization, and non-organization.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Contributing Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1574024
Report Number(s):
DOE/SC-ARM-19-028
Country of Publication:
United States
Language:
English

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