A survey of real-time 3DVAR analyses conducted during the 2010 Experimental Warning Program spring experiment
Travis M. Smith, CIMMS/Univ. of Oklahoma and NOAA/NSSL, Norman, OK; and K. M. Kuhlman, K. L. Ortega, K. L. Manross, D. W. Burgess, J. Gao, and D. J. Stensrud
A dynamically-adaptive three-dimensional variational data assimilation (3DVAR) system was run in real-time as part of the 2010 Experimental Warning Program (EWP) spring experiment conducted in the NOAA Hazardous Weather Testbed. The EWP brings scientists and operational forecasters together to provide feedback and enable collaboration on research projects related to improving National Weather Service warning services for severe convective weather events. The real-time 3DVAR system has the ability to automatically detect, and analyze severe local hazardous weather by identifying mesocyclones at high spatial resolution (1km horizontal resolution) and high time frequency (every 5 minutes) using data primarily from the national WSR-88D radar network, and NCEP's North American Mesoscale (NAM) model product. It is a first step in the long-term “Warn-on-Forecast” research project to enhance tornado warning lead times by assimilating multiple data sources into a dynamically consistent analysis that provides the initial conditions for storm-scale numerical model forecasts.
As this project is in the early stages, the data generated in spring 2010 provide the first opportunity to examine how this information may be used in operations to improve the understanding of the structure and behavior of severe storms. We evaluate the realism of the assimilated data fields and their derivatives, such as the 3D wind field, vorticity, and divergence. Trends in these fields are compared to radar and other sensors to determine the strengths of the 3DVAR analysis as well as areas where improvement is needed.
Extended Abstract (2.3M)
Poster Session 5, Novel Instrumentation and Data Processing Techniques Posters
Tuesday, 12 October 2010, 3:00 PM-4:30 PM, Grand Mesa Ballroom ABC
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