NWC

Convective Forecasts with a Global Atmospheric Model? Possibilities for Numerical Weather Prediction in the Next Decade

Dr. William Skamarock
Mesoscale and Microscale Meteorology Laboratory, NCAR,Boulder, CO

05 May 2015, 4:00 PM

National Weather Center, Room 1313
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

Convection permitting NWP forecasts use regional models and short-range forecasts periods because of computational constraints and the inherent limitations of downscaling with a regional model. Using global models that permit local refinement, existing computers are capable of producing CONUS-scale explicit convective forecasts over intermediate-range periods within operational time windows. The Model for Prediction Across Scales (MPAS) uses an unstructured spherical centroidal Voronoi mesh that allows for smooth cell-size variations between coarse and fine resolution regions on the mesh, and we been using it to produce experimental convective-scale global model forecasts. The first major problem that must be addressed in configuring this multi-scale forecast model involves model physics.

Meshes that resolve or permit nonhydrostatic dynamics in fine-resolution regions (dx ~ few kilometers), but only hydrostatic dynamics in coarse-resolution regions (dx > 10 km), require model physics that can effectively parameterize the sub-grid dynamics in both regimes and in the grey-zone between these regimes. Of particular concern is the parameterization of deep convection. We will present forecast results from the Model for Prediction Across Scales (MPAS) testing several approaches to the convective parameterization issue for meshes that span the grey zone, including scale-aware extensions of the Grell-Freitas (GF) scheme and the approach of using no deep-convection parameterization at all. Using the scale-aware GF scheme, we are performing daily 5-day global forecasts with CONUS-scale explicit convection during this month (May 2015) to evaluate the efficacy of these convective-scale global model forecasts.

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