Enhancements of NCAR Auto-Nowcast System Using NRL, ASAP, MM5 and TAMDAR Data
Huaqing Cai, NCAR, Boulder, CO; and R. Roberts, C. Mueller, T. Saxen, D. Megenhardt, M. Xu, S. Trier, E. Nelson, D. Albo, N. rehak, S. Dettling, and N. Oien
The NCAR Auto-Nowcast System (ANC) is a software system that produces automatic, time- and space-specific, routine short term (0-1 hr) thunderstorm nowcasting. It was developed primarily under the sponsorship of Federal Aviation Administration (FAA) and successfully deployed by NCAR in a number of demonstration projects around the world. The ANC ingests a number of predictor fields ranging from storm scale to large scale. It employed a fuzzy logic algorithm, which can be easily modified, to create the final forecasts. As new observational/model data or new techniques become available operationally, enhancements of ANC could be done by incorporating those new data into the current ANC.
This paper will discuss the following new datasets which will be or is being ingested into ANC, 1) Naval Research Lab (NRL) cloud classifier for cumulus cloud identification, 2) Advanced Satellite Aviation Weather Products (ASAP) for cumulus cloud growth, 3) high-resolution MM5 model data for large scale environmental instability variables, and 4) Tropospheric Airborne Meteorological Data Report (TAMDAR) data for more frequent soundings. Impacts of each dataset on the performance of ANC will be evaluated. Future usage of each dataset in ANC will also be discussed.
Extended Abstract (1016K)
Poster Session 11, Advances in 0–6 Hour Forecasting for Aviation Posters
Thursday, 2 February 2006, 9:45 AM-11:00 AM, Exhibit Hall A2
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