7B.4
Progress Over the Last Decade in the Development and Use of Convection-Allowing Models in Operational Severe Weather Prediction

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Tuesday, 30 June 2015: 2:15 PM
Salon A-5 (Hilton Chicago)
Steven J. Weiss, NOAA/NWS/NCEP/SPC, Norman, OK; and I. L. Jirak, C. J. Melick, J. S. Kain, A. J. Clark, M. C. Coniglio, M. Xue, F. Kong, M. Pyle, E. Rogers, B. Ferrier, E. Aligo, J. R. Carley, G. DiMego, S. G. Benjamin, C. R. Alexander, and M. Weisman

Notable advances in computing power, data assimilation, and NWP modeling science during the last decade enabled a transformational change in the character and quality of convection storm guidance available for operational severe weather forecasters. Although initial efforts in near-real time stormscale prediction was pioneered by OU/CAPS in the middle-1990s during the VORTEX field project, the first generation of ~4 km grid spacing WRF models to explicitly predict convective storms began in the 2003-2004 period when NCAR and NCEP/EMC produced daily real time experimental forecasts over large geographic domains. Despite known limits of predictability on the convective storm-scale, testing and evaluations of these models in the BAMEX field project and early Hazardous Weather Testbed (HWT) Spring Forecasting Experiments demonstrated potential skill in predicting mesoscale convective mode including simulated supercells 24-36 hrs in advance.

The subsequent evolution of convection-allowing model (CAM) development and testing is highlighted through the current time, spearheaded by annual experimental activities in the HWT and year-round testing and use in Storm Prediction Center forecast operations. This includes innovative methods to extract and display characteristics of simulated convective storms from CAM gridded data (e.g., hourly maximum fields to identify storm tracks of rotating updrafts), the implementation of operational CAMs developed by NCEP/EMC and ESRL/GSD, and the gradual emergence of experimental CAM ensemble systems that help address convective-scale uncertainty through the creation of probabilistic guidance fields.