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Published September 16, 2017 | Published + Supplemental Material
Journal Article Open

Cloud-resolving model intercomparison of an MC3E squall line case: Part I-Convective updrafts

Abstract

An intercomparison study of a midlatitude mesoscale squall line is performed using the Weather Research and Forecasting (WRF) model at 1 km horizontal grid spacing with eight different cloud microphysics schemes to investigate processes that contribute to the large variability in simulated cloud and precipitation properties. All simulations tend to produce a wider area of high radar reflectivity (Z_e > 45 dBZ) than observed but a much narrower stratiform area. The magnitude of the virtual potential temperature drop associated with the gust front passage is similar in simulations and observations, while the pressure rise and peak wind speed are smaller than observed, possibly suggesting that simulated cold pools are shallower than observed. Most of the microphysics schemes overestimate vertical velocity and Ze in convective updrafts as compared with observational retrievals. Simulated precipitation rates and updraft velocities have significant variability across the eight schemes, even in this strongly dynamically driven system. Differences in simulated updraft velocity correlate well with differences in simulated buoyancy and low-level vertical perturbation pressure gradient, which appears related to cold pool intensity that is controlled by the evaporation rate. Simulations with stronger updrafts have a more optimal convective state, with stronger cold pools, ambient low-level vertical wind shear, and rear-inflow jets. Updraft velocity variability between schemes is mainly controlled by differences in simulated ice-related processes, which impact the overall latent heating rate, whereas surface rainfall variability increases in no-ice simulations mainly because of scheme differences in collision-coalescence parameterizations.

Additional Information

© 2017 American Geophysical Union. Received 9 FEB 2017; Accepted 24 JUL 2017; Accepted article online 11 AUG 2017; Published online 6 SEP 2017. Manuscript Authored by Battelie Memorial Institute Under Contract Number DE-ACOS-76Rl01830 with the US Department of Energy. The US Government retains and the publisher, by accepting this article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan: (http://energy.gov/downloads/doe-public-access-plan). This study was supported by the U.S. Department of Energy (DOE) Atmospheric System Research (ASR) Program. The Pacific Northwest National Laboratory (PNNL) is operated for the DOE by Battelle Memorial Institute under contract DE-AC06-76RLO1830. This research used PNNL Institutional Computing resources and also resources at the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. DOE under contract DE-AC02-05CH1123. Bin Han and Chen were supported by the National Basic Research Program of China (2013CB430105). Varble was supported by U.S. DOE ASR DE-SC0008678. Morrison was supported by U.S. DOE ASR DE-SC0008648. Morrison and Varble were also supported by U.S. DOE ASR DE-SC0016476. Giangrande is an employee of Brookhaven Science Associates LLC under contract DE-AC02-98CH10886 with the U.S. DOE. The National Center for Atmospheric Research is sponsored by the U.S. National Science Foundation. Y. Wang is supported by the ROSES14-ACMAP project. The PNNL Institutional Computing (PIC) resources were used for the model simulations of this study. The simulation data are available at the PNNL PIC and can be obtained by contacting the corresponding author, Jiwen Fan (jiwen.fan@pnnl.gov). We also acknowledge the Atmospheric Radiation Measurement (ARM) Climate Research Facility, a user facility of the U.S. DOE, Office of Science, sponsored by the Office of Biological and Environmental Research, and support from the ASR program of that office. DOE ARM data sets used in this study can be obtained from the ARM archive at http://www.arm.gov and ARM External Data Center at https://www.arm.gov/xdc/. The Oklahoma MESONET data are downloaded from https://www.mesonet.org/index.php/weather/category/past_data_files with the help of Lulin Xue and Xia Chu at NCAR.

Attached Files

Published - Fan_et_al-2017-Journal_of_Geophysical_Research__Atmospheres.pdf

Supplemental Material - jgrd54034-sup-0001-Supporting-Information_f.pdf_v=1_s=a214234c95338dd05ed3734e18c6f31af17f96b1

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Fan_et_al-2017-Journal_of_Geophysical_Research__Atmospheres.pdf
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Additional details

Created:
August 21, 2023
Modified:
October 18, 2023