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  • 标题:Physically Constrained Stochastic Shallow Convection in Realistic Kilometer‐Scale Simulations
  • 本地全文:下载
  • 作者:Mirjana Sakradzija ; Daniel Klocke
  • 期刊名称:Journal of Advances in Modeling Earth Systems
  • 电子版ISSN:1942-2466
  • 出版年度:2018
  • 卷号:10
  • 期号:11
  • 页码:2755-2776
  • DOI:10.1029/2018MS001358
  • 出版社:John Wiley & Sons, Ltd.
  • 摘要:A new configuration of a parameterization for shallow convection in the Icosahedral Nonhydrostatic Model (ICON) is described and tested on a single realistic test case. As a test case, a shallow convective day over Germany is simulated using four different configurations of ICON. These configurations differ by the choice of the shallow convection parameterization, which can be deterministic, stochastic, or completely switched off. As the fourth configuration, the ICON large eddy simulation setup is used as a reference against which the three other ICON model configurations are tested and compared at resolutions from 1 to 10 km. It is demonstrated that a deterministic mass flux closure combined with the stochastic sampling of the cloud base mass fluxes corrects the spatial and temporal distribution of cloudiness. The mean vertical structure of the cloud layer and vertical profiles of the thermodynamic variables in the boundary layer are also improved. The stochastic parameterization adapts to the model resolution by its formulation, while a limited scale‐aware behavior is present in the outcome of the simulations. This limitation stems from the resolution dependence of the resolved dynamics, which produces incorrect distributions of cloudiness, and scale‐dependence opposite of what is expected based on the reference large eddy simulation results. The deterministic version of the convection scheme cannot correct the behavior of the resolved dynamics, while the stochastic version corrects the resolved dynamics to some extent and improves the overall behavior across resolutions.
  • 关键词:shallow convection;stochastic parameterization;mass flux approach;convective gray zone;NWP
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