UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available after Summer 2014.
Currently, the 84-node cluster tara still operates independently,
until it becomes part of maya at the end of Summer 2014.
Please see the 2013 Resources Pages under the Resources tab for tara information.
A Statistical Comparison of Small-Scale Features in Simulated Tropical Cyclones and High-Resolution Observational Data
Lynn Sparling and Samuel Trahan, Department of Physics, UMBC, and Scott Braun, NASA Goddard Space Flight Center
A Statistical Comparison of Small-Scale Features in Simulated Tropical Cyclones and High-Resolution Observational Data abstract: Over the past few decades, there has been significant improvement in tropical cyclone track prediction but not much improvement in intensity prediction. There is evidence that small-scale processes in the inner core may play an important role in hurricane intensity but these processes, and what triggers them, are only partially understood. The features - eyewall vorticity waves, mesovortices and hot towers - are on the order of kilometers wide and rapidly varying. Hence high-resolution models are thought to be necessary, but it is not clear how well high-resolution features in simulated tropical cyclones match those in the real world. It
is also unclear what resolutions are necessary to allow a simulation to accurately model the effects of these features on the large-scale state.
This research uses WRF-based numerical weather prediction models to imulate
tropical cyclones that have been observed to have periods of strong inner core activity. We compare statistical properties of high-resolution features in the simulation to the same statistical properties in observational data. Using that methodology, we analyze how these periods are represented in the model under different large-scale flow patterns and using different model resolutions and parameterizations.