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Evaluation of Deep Convective Transport In Storms From Different Convective Regimes During The Dc3 Field Campaign Using WRF-CHEM With Lightning Data Assimilation


Deep convective transport of surface moisture and pollution from the planetary boundary layer to the upper troposphere and lower stratosphere affects the radiation budget and climate. This study analyzes the deep convective transport in three different convective regimes from the 2012 Deep Convective Clouds and Chemistry field campaign: 21 May Alabama air mass thunderstorms, 29 May Oklahoma supercell severe storm, and 11 June mesoscale convective system (MCS). Lightning data assimilation within the Weather Research and Forecasting (WRF) model coupled with chemistry (WRF-Chem) is utilized to improve the simulations of storm location, vertical structure, and chemical fields. Analysis of vertical flux divergence shows that deep convective transport in the 29 May supercell case is the strongest per unit area, while transport of boundary layer insoluble trace gases is relatively weak in the MCS and air mass cases. The weak deep convective transport in the strong MCS is unexpected and is caused by the injection into low levels of midlevel clean air by a strong rear inflow jet. In each system, the magnitude of tracer vertical transport is more closely related to the vertical distribution of mass flux density than the vertical distribution of trace gas mixing ratio. Finally, the net vertical transport is strongest in high composite reflectivity regions and dominated by upward transport.

Article / Publication Data
Available Metadata
Accepted On
June 14, 2017
Fiscal Year
Peer Reviewed
Publication Name
Journal of Geophysical Research Atmospheres
Published On
July 16, 2017
Publisher Name
American Geophysical Union
Print Volume
Print Number
Page Range
Submitted On
January 07, 2017


Authors who have authored or contributed to this publication.

  • Yunyao Li - lead None
  • Megan M. Bela - fifth Other noaa
    Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder
    NOAA/Global Systems Laboratory