Evaluating Variable-Resolution CAM-SE as a Numerical Weather Prediction Tool
The global modeling community has traditionally struggled simulating meso-alpha and meso-beta scale (25-500 km) systems in the atmosphere such as tropical cyclones, strong fronts, and squall lines. With traditional General Circulation Model (GCM) resolutions of 50-300 km, these features have been under-resolved and require significant parameterization at the sub-grid scale. In an effort to help alleviate these issues, the use of limited area models (LAMs) with high resolution has become popular, although, by definition, these models typically lack two-way communication with the exterior domain. Variable-resolution global dynamical models can serve as the bridge between traditional global forecast models and high-resolution LAMs by applying fine grid spacing in areas of interest. These models can utilize existing computing platforms to model high resolutions on a regional basis while maintaining global continuity, therefore eliminating the need for externally-forced and possib ly numerically and physically inconsistent boundary conditions required by LAMs.
A statically-nested, variable-mesh option has recently been introduced into the National Center for Atmospheric Research (NCAR) Community Atmosphere Model's (CAM) Spectral Element (SE) dynamical core. We present short-term CAM-SE model simulations of historical tropical cyclones and compare the model's prediction of storm track and intensity to other global and regional models used operationally by hurricane forecast centers. Additionally, we explore the model's ability to simulate other weather phenomenon traditionally unavailable to global modelers such as mesoscale convective systems and precipitation lines associated with frontal passages. We also discuss the performance of existing parameterizations in CAM with respect to high-resolution modeling as well as consider the potential computational benefits in using a variable-resolution setup as an operational tool for both weather and climate prediction.