Potential ways of parameterizing vertical turbulent fluxes of hydrometeors are examined using a high-resolution simulation of continental deep convection. The cloud-resolving model uses a double-moment microphysics scheme that contains prognostic variables for four hydrometeor types: rain, graupel, cloud ice, and snow. The benchmark simulation with a horizontal grid spacing of 250 m is analyzed to evaluate three different ways of parameterizing the turbulent vertical fluxes of hydrometeors: an eddy-diffusion approximation, a quadrant-based decomposition, and a scaling method that accounts for within-quadrant (subplume) correlations. Results show that the downgradient nature of the eddy-diffusion approximation enforces transport of mass away from concentrated regions, whereas the benchmark simulation indicates that the vertical transport often moves mass from below the level of maximum concentration to aloft. Unlike the eddy-diffusion approach, the quadrimodal decomposition is able to capture the signs of the flux gradient but underestimates the magnitudes. The scaling approach, which accounts empirically for within-quadrant correlations, improves the representation of the vertical fluxes for all hydrometeors except snow. A sensitivity study is performed to illustrate how vertical transport effects on the vertical distribution of hydrometeors are compounded by accompanying changes in microphysical process rates. Results from the sensitivity tests show that suppressing rain or graupel transport drastically alters vertical profiles of cloud ice and snow through changes in the distribution of cloud water, which in turn governs the production of cloud ice and snow aloft. Last, a viable subgrid-scale hydrometeor transport scheme in an assumed probability density function parameterization is discussed.
As an alternative to traditional precipitation analysis and forecast verification, 1D and 2D spectral decompositions of NCEP/Stage IV and Multi-Radar Multi-Sensor (MRMS) precipitation products and convective-scale model forecasts are examined. Both the stage IV and MRMS analyses and the model forecasts show a similar weak power-law behavior in 1D spectral decompositions, although the MRMS analysis does not drop off in power at wavelengths less than approximately 20 km as found in the stage IV analysis. The convective-scale forecasts produce similar behavior to the MRMS when the forecast model’s effective resolution is sufficient. Neither the MRMS analyses nor the forecasts suggest the existence of a break in the spectral slope at the scales for which the analyses and forecasts are valid. The 2D spectra of both observations and forecasts, expressed in terms of an absolute wavenumber and azimuthal angle, show power varying significantly as a function of azimuthal angle for a given wavenumber. This azimuthal anisotropy is significant, and is dominated by the second mode (wavenumber 2). The phase of the mode is the result of the orientation of precipitation features and, hence, convective system orientations and propagation. Observations show a shift in orientation (phase) over May–June–July. The convective forecasts reproduce this shift in phase, although with a consistent but small phase error.
Coarse‐resolution climate models increasingly rely on probability density functions (PDFs) to represent subgrid‐scale variability of prognostic variables. While PDFs characterize the horizontal variability, a separate treatment is needed to account for the vertical structure of clouds and precipitation. When subcolumns are drawn from these PDFs for microphysics or radiation parameterizations, appropriate vertical correlations must be enforced via PDF overlap specifications. This study evaluates the representation of PDF overlap in the Subgrid Importance Latin Hypercube Sampler (SILHS) employed in the assumed PDF turbulence and cloud scheme called the Cloud Layers Unified by Binormals (CLUBB). PDF overlap in CLUBB‐SILHS simulations of continental and tropical oceanic deep convection is compared with overlap of PDF of various microphysics variables in cloud‐resolving model (CRM) simulations of the same cases that explicitly predict the 3‐D structure of cloud and precipitation fields. CRM results show that PDF overlap varies significantly between different hydrometeor types, as well as between PDFs of mass and number mixing ratios for each species—a distinction that the current SILHS implementation does not make. In CRM simulations that explicitly resolve cloud and precipitation structures, faster falling species, such as rain and graupel, exhibit significantly higher coherence in their vertical distributions than slow falling cloud liquid and ice. These results suggest that to improve the overlap treatment in the subcolumn generator, the PDF correlations need to depend on hydrometeor properties, such as fall speeds, in addition to the currently implemented dependency on the turbulent convective length scale.
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