206 Penn State Shallow Convection Parameterization Scheme, Its Evaluation in WRF and Upgrades

Wednesday, 9 July 2014
Aijun Deng, Pennsylvania State University, University Park, PA; and B. Gaudet and T. Lauvaux

To better represent clouds, especially shallow clouds, in numerical weather prediction and climate models, a mass-flux-based shallow convection parameterization (SCP) was developed at Penn State University (Deng et al. 2003a and b, 2013 and 2014). In the prototype SCP, convection is triggered by the vertical velocities determined by various factors including the turbulent kinetic energy (TKE) within the planetary boundary layer (PBL). The convective parameterization closure is determined using a hybrid approach combining the boundary layer TKE and CAPE removal, depending on the depth of the convective updraft. In addition, there are two predictive equations for cloud water and cloud fraction of neutrally-buoyant clouds (NBC), with cloud production and dissipation processes that take cloud microphysics into account. The model-predicted subgrid-scale NBCs also interact with the model atmospheric radiation calculation to produce more realistic atmospheric radiation fluxes. Preliminary evaluation showed that the PSU SCP was able to produce reasonable cloud fraction, and reduce the model temperature bias through the radiative interaction with partial cloudiness, improving global horizontal irradiance (GHI) reaching the ground. For this case, better verification metrics for both radiation and standard meteorological observations were found from the PSU SCP scheme than from an alternate shallow cumulus parameterization. In this AMS conference presentation, we will present more test results and discuss upgrades, including incorporating mixed-phase processes and the effect of cloud droplet number concentration in the microphysics, and the inclusion of tracer mixing. Together, these changes should allow the scheme to be used to model the mutual interaction of shallow clouds with PM, CO2, and other atmospheric pollutants.
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