ITR/AP: Collaborative Research: Diversifying Ensembles with Stochastic Convection

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An effective way of using increasing powerful computer systems in improving numerical weather prediction is through ensemble forecasting. Experience shows that the ensemble forecasts do not diverge enough so that often the true state of the atmosphere lies outside the spread of the ensemble. In this project, the PIs seek to broaden the spread by exploring the variability of model's climate through the perturbation of the convective processes. The approach is to introduce stochastic noise in cumulus parameterization schemes to account for the sub-grid scale effects. The design of the fluctuations will be based on high-resolution data from cloud-resolving models, radars, aircraft, and radiosondes. These ideas will be implemented in models with different types of complexity. The results have the potential of providing new methods for improving ensemble forecast systems at major meteorological centers and also leading to improved understanding of existing cumulus parameterization. This project is being done collaboratively between Drs. Brian Mapes and Thomas Hamill, CIRES, University of Colorado and Dr. Steven Mullen, University of Arizona.