J1.1 Collaborative Efforts between the U.S. and U.K. to Advance Prediction of High Impact Weather

Monday, 11 January 2016: 11:00 AM
Room 244 ( New Orleans Ernest N. Morial Convention Center)
John S. Kain, NSSL, Norman, OK; and S. M. Willington, A. J. Clark, M. C. Coniglio, I. L. Jirak, S. J. Weiss, C. D. Karstens, K. H. Knopfmeier, M. Weeks, J. M. Wilkinson, N. Roberts, and H. Lean

NOAA's National Severe Storms Laboratory (NSSL) and Storm Prediction Center (SPC) collaborate on applied research problems of mutual interest. A primary area of engagement in this collaboration is a high resolution numerical prediction system that NSSL runs in coordination with the SPC to provide guidance for operational SPC forecasts. This prediction system is also used for research and as framework for developing post-processed fields of numerical guidance tools that are specifically tailored to the needs of severe weather forecasters.

The UK Met Office (UKMO) has been a world leader over the past decade in the development, implementation, and usage of operational high resolution forecast models. As with NSSL and SPC, the research and forecasting branches of the Met Office have collaborated to optimize the value of their high resolution prediction systems by developing unique tools for visualization, verification, and specialized forecast problems. NSSL and the SPC conduct annual forecasting experiments in the NOAA Hazardous Weather Testbed and the UKMO became contributing partners in these experiments, both in terms of model-forecast data and personnel (forecasters and researchers), beginning in 2012. This collaboration has been exceptionally productive, providing valuable insights into best practices in both high-resolution numerical prediction and human interpretation and communication of the numerical output. It has led to a much better understanding of similarities and differences between numerical models that have different formulations of both dynamical and physical processes. The positive benefits of this exchange, from both scientific and forecasting perspectives, will be highlighted in this presentation.

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