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Title: The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E

Abstract

Here, the controls on precipitation onset and the transition from shallow cumulus to congestus are explored using a suite of 16 large–eddy simulations based on the 25 May 2011 event from the Midlatitude Continental Convective Clouds Experiment (MC3E). The thermodynamic variables in the model are relaxed at various timescales to observationally constrained temperature and moisture profiles in order to better reproduce the observed behavior of precipitation onset and total precipitation. Three of the simulations stand out as best matching the precipitation observations and also perform well for independent comparisons of cloud fraction, precipitation area fraction, and evolution of cloud top occurrence. All three simulations exhibit a destabilization over time, which leads to a transition to deeper clouds, but the evolution of traditional stability metrics by themselves is not able to explain differences in the simulations. Conditionally sampled cloud properties (in particular, mean cloud buoyancy), however, do elicit differences among the simulations. The inability of environmental profiles alone to discern subtle differences among the simulations and the usefulness of conditionally sampled model quantities argue for hybrid observational/modeling approaches. These combined approaches enable a more complete physical understanding of cloud systems by combining observational sampling of time–varying three–dimensional meteorological quantities and cloudmore » properties, along with detailed representation of cloud microphysical and dynamical processes from numerical models.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]
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  1. Univ. of Kansas, Lawrence, KS (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1454808
Report Number(s):
BNL-205751-2018-JAAM
Journal ID: ISSN 2169-897X
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research: Atmospheres
Additional Journal Information:
Journal Volume: 123; Journal Issue: 6; Journal ID: ISSN 2169-897X
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; MC3E; LES; congestus; radar; forcing; nudging

Citation Formats

Mechem, David B., and Giangrande, Scott E. The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E. United States: N. p., 2018. Web. doi:10.1002/2017JD027457.
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Mechem, David B., & Giangrande, Scott E. The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E. United States. https://doi.org/10.1002/2017JD027457
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Mechem, David B., and Giangrande, Scott E. Thu . "The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E". United States. https://doi.org/10.1002/2017JD027457. https://www.osti.gov/servlets/purl/1454808.
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@article{osti_1454808,
title = {The Challenge of Identifying Controls on Cloud Properties and Precipitation Onset for Cumulus Congestus Sampled During MC3E},
author = {Mechem, David B. and Giangrande, Scott E.},
abstractNote = {Here, the controls on precipitation onset and the transition from shallow cumulus to congestus are explored using a suite of 16 large–eddy simulations based on the 25 May 2011 event from the Midlatitude Continental Convective Clouds Experiment (MC3E). The thermodynamic variables in the model are relaxed at various timescales to observationally constrained temperature and moisture profiles in order to better reproduce the observed behavior of precipitation onset and total precipitation. Three of the simulations stand out as best matching the precipitation observations and also perform well for independent comparisons of cloud fraction, precipitation area fraction, and evolution of cloud top occurrence. All three simulations exhibit a destabilization over time, which leads to a transition to deeper clouds, but the evolution of traditional stability metrics by themselves is not able to explain differences in the simulations. Conditionally sampled cloud properties (in particular, mean cloud buoyancy), however, do elicit differences among the simulations. The inability of environmental profiles alone to discern subtle differences among the simulations and the usefulness of conditionally sampled model quantities argue for hybrid observational/modeling approaches. These combined approaches enable a more complete physical understanding of cloud systems by combining observational sampling of time–varying three–dimensional meteorological quantities and cloud properties, along with detailed representation of cloud microphysical and dynamical processes from numerical models.},
doi = {10.1002/2017JD027457},
journal = {Journal of Geophysical Research: Atmospheres},
number = 6,
volume = 123,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2018},
month = {Thu Mar 01 00:00:00 EST 2018}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1002/2017JD027457
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Cited by: 2 works
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Figures / Tables:

Figure 1 Figure 1: Synoptic configuration at 1800 UTC. Albedo from GOES visible satellite imagery is combined with isobars of the surface pressure field (in hPa) from North American Regional Reanalysis [Mesinger et al., 2006] and a multiradar composite of reflectivity from WSR-88D Next Generation Weather Radar (NEXRAD). The magenta cross representsmore » the location of the ARM Central Facility, and the multiple S's indicate MC3E sounding sites. Concentric circles represent 75- and 150-km regions for the variational analysis and radar precipitation estimates. Adapted from Mechem et al. [2015].« less
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    Works referencing / citing this record:

    xsacrgridrhi.c0 v1.0 from 20181228
    dataset, January 2020

    • Wang, Meng; Giangrande, Scott; Hardin, Joseph
    • 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)
    • DOI: 10.5439/1578281
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      Figure 14 (p. 62)figure
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