Atmospheric Radiation Measurement Climate Research Facility US Department of Energy

Midlatitude Continental Convective Clouds Experiment (MC3E)

22 April 2011 - 6 June 2011

Lead Scientist: Michael Jensen

Observatory: SGP

Convective processes play a critical role in the Earth's energy balance through the redistribution of heat and moisture in the atmosphere and their link to the hydrological cycle. Accurate representation of convective processes in numerical models is vital towards improving current and future simulations of Earth’s climate system. Despite improvements in computing power, current operational weather and global climate models are unable to resolve the natural temporal and spatial scales important to convective processes and therefore must turn to parameterization schemes to represent these processes. In turn, parameterization schemes in cloud-resolving models need to be evaluated for their generality and application to a variety of atmospheric conditions. Data from field campaigns with appropriate forcing descriptors have been traditionally used by modelers for evaluating and improving parameterization schemes. To this end, the Midlatitude Continental Convective Cloud Experiment (MC3E), a joint field program involving NASA Global Precipitation Measurement Program and ARM investigators, was conducted in south-central Oklahoma during the April to May 2011 period. The experiment leveraged from the unprecedented observing infrastructure available in the central United States, combined with an extensive sounding array. Our goal was to provide the most complete characterization of convective cloud systems and their environment that had ever been obtained, providing constraints for model cumulus parameterizations that had never before been available. Several different components of convective processes tangible to the convective parameterization problem were targeted, such as pre-convective environment and convective initiation, updraft/downdraft dynamics, condensate transport and detrainment, precipitation and cloud microphysics, influence on the environment and radiation, and a detailed description of the large-scale forcing. This intensive observation period used a new multi-scale observing strategy with the participation of a network of distributed sensors (both passive and active). The approach was to document in 3D not only precipitation, but also clouds, winds, and moisture in an attempt to provide a holistic view of convective clouds and their feedback with the environment. A goal was to measure cloud and precipitation transitions and environmental quantities that are important for convective parameterization in large-scale models and cloud-resolving model simulations. With unprecedented observing capabilities comes a greater responsibility to develop synthesis data products suitable for model studies and evaluation. Thus, special emphasis was given to the development of a systematic dialogue with the ASR modeling group for the development of such 3D data products.

This experiment seeks to use a multi-scale, multi-frequency, multi-platform observational strategy to provide unprecedented detail in characterizing convection and its environment, providing constraints for model cumulus parameterizations and spaceborne measurements of precipitation over land that have never before been available. The key goals are to:

  1. Advance the understanding of the different components of convective simulation and microphysical parameterization
  2. Improve the fidelity of rainfall estimates over land

Additional Information

Steering Committee

  • Michael Jensen, Brookhaven National Laboratory
  • Walter Petersen, NASA Marshall Space Flight Center
  • Anthony Del Genio, NASA Goddard Institute of Space Studies
  • Scott Giangrande, McGill University
  • Andrew Heymsfield, NSF National Center for Atmospheric Research
  • Gerald Heymsfield, NASA Goddard Space Flight Center
  • Aurther Hou, NASA Goddard Space Flight Center
  • Pavlos Kollias, McGill University
  • Brad Orr, Argonne National Laboratory
  • Steven Rutledge, Colorado State University
  • Matthew Schwaller, NASA Goddard Space Flight Center
  • Edward Zipser, University of Utah
  • Co-Investigators

    Anthony Del Genio
    Scott Giangrande
    Pavlos Kollias

    Related Publications


    Carlin J and A Ryzhkov. 2019. "Estimation of Melting-Layer Cooling Rate from Dual-Polarization Radar: Spectral Bin Model Simulations." Journal of Applied Meteorology and Climatology, 58(7), 10.1175/JAMC-D-18-0343.1.

    Cui W, X Dong, B Xi, J Fan, J Tian, J Wang, and T McHardy. 2019. "Understanding Ice Cloud‐Precipitation Properties of Three Modes of Mesoscale Convective Systems During PECAN." Journal of Geophysical Research: Atmospheres, 124(7), 10.1029/2019JD030330.

    Grabowski W, H Morrison, S Shima, G Abade, P Dziekan, and H Pawlowska. 2019. "Modeling of Cloud Microphysics: Can We Do Better?" Bulletin of the American Meteorological Society, 100(4), 10.1175/BAMS-D-18-0005.1.

    Jouan C and J Milbrandt. 2019. "The importance of the ice-phase microphysics parameterization for simulating the effects of changes to CCN concentrations in deep convection." Journal of the Atmospheric Sciences, 76(6), 10.1175/JAS-D-18-0168.1.

    Finlon J, G McFarquhar, S Nesbitt, R Rauber, H Morrison, W Wu, and P Zhang. 2019. "A novel approach for characterizing the variability in mass-dimension relationships: results from MC3E." Atmospheric Chemistry and Physics, 19(6), 10.5194/acp-19-3621-2019.

    Oue M, P Kollias, A Shapiro, A Tatarevic, and T Matsui. 2019. "Investigation of observational error sources in multi-Doppler-radar three-dimensional variational vertical air motion retrievals." Atmospheric Measurement Techniques, 12(3), doi:10.5194/amt-12-1999-2019.
    Research Highlight

    Tao W, T Iguchi, and S Lang. 2019. "Expanding the Goddard CSH Algorithm for GPM: New Extratropical Retrievals." Journal of Applied Meteorology and Climatology, 58(5), 10.1175/JAMC-D-18-0215.1.

    Zeng X, G Skofronick-Jackson, L Tian, A Emory, W Olson, and R Kroodsma. 2019. "Analysis of the Global Microwave Polarization Data of Clouds." Journal of Climate, 32(1), 10.1175/JCLI-D-18-0293.1.

    Han B, J Fan, A Varble, H Morrison, C Williams, B Chen, X Dong, S Giangrande, A Khain, E Mansell, J Milbrandt, J Shpund, and G Thompson. 2019. "Cloud-Resolving Model Intercomparison of an MC3E Squall Line Case: Part II. Stratiform Precipitation Properties." Journal of Geophysical Research: Atmospheres, 124(2), 10.1029/2018JD029596.


    Wang J, X Dong, and B Xi. 2018. "Investigation of Liquid Cloud Microphysical Properties of Deep Convective Systems: 2. Parameterization of Raindrop Size Distribution and its Application for Convective Rain Estimation." Journal of Geophysical Research: Atmospheres, , 10.1029/2018JD028727. ONLINE.

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    Campaign Data Sets

    IOP Participant Data Source Name Final Data
    Mary Jane Bartholomew Video Disdrometer Order Data
    Jennifer Comstock High Volume Precip Spectrometer Order Data
    Jennifer Comstock High Volume Precip Spectrometer Order Data
    Xiquan Dong Ice-Cloud Order Data
    Michael Jensen Balloon Sonde Order Data
    Michael Jensen Balloon Sonde Adjust Order Data
    Michael Jensen Convective Available Potential Energy (CAPE) Order Data
    Michael Jensen Microwave Radiometer Profiler Order Data
    Alyssa Matthews S-band Radar Order Data
    Mathew Schwaller GPM Ground Validation Autonomous Parsivel Unit (APU) MC3E Order Data
    Mathew Schwaller GPM Ground Validation Two-Dimensional Video Disdrometer (2DVD) MC3E Order Data
    Jason Tomlinson Ultra High Sensitivity Aerosol Spectrometer Order Data
    Jason Tomlinson Ultra High Sensitivity Aerosol Spectrometer Order Data
    David Turner AERI Retrieved Thermodynamic Profiles and Cloud Properties Order Data
    Christopher Williams 449 MHz Profiler Order Data
    Christopher Williams Parcivel Disdrometer Order Data
    Christopher Williams S-band Radar Order Data
    Christopher Williams Surface Meteorology Order Data
    Christopher Williams Vertical Air Motion Order Data
    Shaocheng Xie Single Column Model Forcing Order Data