greyzone
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+ | //Figure 1. Visualization of the grey zone in turbulent transport in a subtropical marine shallow cumulus topped boundary layer. Shown is the ratio of the subgrid flux to the total flux as a function of horizontal resolution of a series of LES simulations of the RICO shallow cumulus case, featuring a scale-adaptive version of the EDMF scheme. The intermediate values reflect the range of resolutions at which the resolved contribution is as large as the subgrid contribution, | ||
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+ | In recent years the capacity and speeds of supercomputers have improved rapidly. As a result, GCM resolutions are now feasible at which some previously parameterized processes become at least partially resolved. This situation is sometimes referred to as the "grey zone". The range of resolutions covered by the grey zone depends on the process of interest. While for deep convection it is usually assumed to be situated below 10 km, for boundary layer processes the grey zone can be found at much smaller grid-spacings. As a consequence, | ||
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+ | One of the main goals of the InScAPE research group is to develop scale-adaptive schemes for moist convective transport and clouds in the boundary layer for use in next-generation circulation models. Topics of interest include the size statistics of cumulus cloud populations, | ||
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+ | Related papers: | ||
+ | * Exploring a size-filtered mass flux scheme [[https:// | ||
+ | * Powerlaw scaling in subsampled cumulus cloud populations [[https:// | ||
+ | * Turbulent transport in the Grey Zone: A large-eddy simulation model intercomparison study of the CONSTRAIN cold air outbreak case [[https:// | ||
+ | * A binomial stochastic framework for efficiently modeling discrete statistics of convective populations [[https:// | ||
greyzone.1505315145.txt.gz · Last modified: 2017/09/13 17:05 by schemann