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Overcoming Forecast Challenges of Complex Terrain: Use of R/WIS to Improve Operations

Overcoming Forecast Challenges of Complex Terrain: Use of R/WIS to Improve Operations. Mary M. Cairns Senior Staff Meteorologist OFCM. Overview. Purpose Background Objectives and Results Process Keys to Success Summary. Purpose.

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Overcoming Forecast Challenges of Complex Terrain: Use of R/WIS to Improve Operations

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  1. Overcoming Forecast Challenges of Complex Terrain: Use of R/WIS to Improve Operations Mary M. Cairns Senior Staff Meteorologist OFCM

  2. Overview • Purpose • Background • Objectives and Results • Process • Keys to Success • Summary

  3. Purpose Demonstrate how a collaborative effort with funding is essential for transitioning user-based needs (e.g., FHWA) into operational results

  4. Background • State Department of Transportation • >1300 Environmental Sensor Stations [c.2001] • NWS and FHWA joint research effort • COMET Request for Proposals • Partnership project selected • NOAA/NWS Reno, NV • Nevada DOT • University of Nevada Desert Research Institute • 2 years • Complex terrain forecasts and NDOT operational challenges • Can R/WIS help?

  5. Objectives and Results • Objectives • Improve pavement forecasts through improved model input • Improve NWS operational forecasts through better model guidance • Improve operational decisions regarding snow and control operations • Develop an NDOT public travelers forecast • Results • Improved quality of R/WIS data & archival • Improved issuance and verification of operational forecasts and warnings • Developed data assimilation system for numerical weather prediction model • Provided improved guidance to NDOT pavement temperature forecasts

  6. Process • Agency defined need and funding • Partnership • Mutual goals • Share and use of RSS data • Advance regional mesoscale modeling capabilities • Improve road condition forecasting • Expertise and enthusiasm • Technology • Scientific knowledge • Results driven

  7. Pavement Temperature Model Output

  8. Keys to Success • Expertise and enthusiasm • Test beds or test sites needed • Includes management of R&D priorities & associated projects • Adequate funding/resources for all involved • “Customer” based research with operational result • Project based on high-impact events • Collaboration was a learning process • Leadership support

  9. Example of Success California Department of Boating and Waterways MM5 Real-Time Forecasts – Supporting shipping and boating operations DRI Atmospheric and Dispersion Modeling Program Dr. Darko Koracin, PI

  10. Summaryaka Purpose revisited Demonstrated how a collaborative effort with funding is essential for transitioning user-based needs (e.g., FHWA) into operational results

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