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  • Data for: Impacts of Stochastic Mixing in Idealized Convection-Permitting Simulations of Squall Lines

    Description:
    This study investigates impacts of altering subgrid-scale mixing in “convection-permitting” km-scale horizontal grid spacing (∆h) simulations by applying either constant or stochastic multiplicative factors to the horizontal mixing coefficients within the Weather Research and Forecasting model. In quasi-idealized 1-km ∆h simulations of two observationally based squall line cases, constant enhanced mixing produces larger updraft cores that are more dilute at upper levels, weakens the cold pool, rear inflow jet, and front-to-rear flow of the squall line, and degrades the model’s effective resolution. Reducing mixing by a constant multiplicative factor has the opposite effect on all metrics. Completely turning off parameterized horizontal mixing produces bulk updraft statistics and squall line mesoscale structure closest to a LES “benchmark” among all 1-km simulations, although the updraft cores are too undilute. The stochastic mixing scheme, which applies a multiplicative factor to the mixing coefficients that varies stochastically in time and space, is employed at 0.5-, 1-, and 2-km ∆h. It generally reduces mid-level vertical velocities and enhances upper-level vertical velocities compared to simulations using the standard mixing scheme, with more substantial impacts at 1-km and 2-km ∆h compared to 0.5-km. The stochastic scheme also increases updraft dilution to better agree with the LES for one case, but has less impact on the other case. Stochastic mixing acts to weaken the cold pool but without a significant impact on squall line propagation. It also does not affect the model’s overall effective resolution unlike applying constant multiplicative factors to the mixing coefficients.
    Keyword:
    stochastic, stochastic mixing, WRF, squall line, simulation, weather research and forecasting, and mixing
    Subject:
    Atmospheric Sciences
    Creator:
    Stanford, McKenna, Morrison, Hugh, and Varble, Adam
    Owner:
    MCKENNA STANFORD
    Language:
    English
    Date Uploaded:
    08/17/2020
    Date Modified:
    10/29/2024
    Date Created:
    2019-03-01 to 2020-04-30
    License:
    Public Domain – This data is free of copyright restrictions (e.g. government sponsored data).
    Resource Type:
    Dataset
    Identifier:
    https://doi.org/10.7278/S50DJNGQ6V67