HR: 0800h
AN: ED51B-0430    [Abstracts]
TI: Promoting Interests in Atmospheric Science at a Liberal Arts Institution
AU: * Roussev, S
EM: sirousse@coastal.edu
AF: Coastal Carolina University, 755 Highway 544, Conway, SC 29528, United States
AU: Sherengos, P M
EM: pmsheren @coastal.edu
AF: Coastal Carolina University, 755 Highway 544, Conway, SC 29528, United States
AU: Limpasuvan, V
EM: var@coastal.edu
AF: Coastal Carolina University, 755 Highway 544, Conway, SC 29528, United States
AU: Xue, M
EM: mxue@ou.edu
AF: University of Oklahoma, National Weather Center, Suite 2500 120 David Boren Blvd, Norman, OK 73072, United States
AB: Coastal Carolina University (CCU) students in Computer Science participated in a project to set up an operational weather forecast for the local community. The project involved the construction of two computing clusters and the automation of daily forecasting. Funded by NSF-MRI, two high-performance clusters were successfully established to run the University of Oklahoma's Advance Regional Prediction System (ARPS). Daily weather predictions are made over South Carolina and North Carolina at 3-km horizontal resolution (roughly 1.9 miles) using initial and boundary condition data provided by UNIDATA. At this high resolution, the model is cloud- resolving, thus providing detailed picture of heavy thunderstorms and precipitation. Forecast results are displayed on CCU's website (https://marc.coastal.edu/HPC) to complement observations at the National Weather Service in Wilmington N.C. Present efforts include providing forecasts at 1-km resolution (or finer), comparisons with other models like Weather Research and Forecasting (WRF) model, and the examination of local phenomena (like water spouts and tornadoes). Through these activities the students learn about shell scripting, cluster operating systems, and web design. More importantly, students are introduced to Atmospheric Science, the processes involved in making weather forecasts, and the interpretation of their forecasts. Simulations generated by the forecasts will be integrated into the contents of CCU's course like Fluid Dynamics, Atmospheric Sciences, Atmospheric Physics, and Remote Sensing. Operated jointly between the departments of Applied Physics and Computer Science, the clusters are expected to be used by CCU faculty and students for future research and inquiry-based projects in Computer Science, Applied Physics, and Marine Science.
DE: 3329 Mesoscale meteorology
DE: 3355 Regional modeling
DE: 3364 Synoptic-scale meteorology
SC: Education and Human Resources [ED]
MN: 2007 Fall Meeting