1 . FY12-13 GIMPAP Project Proposal Title Page date: 6 August 2012

1. 10 1. FY12-13 GIMPAP Project Proposal Title Pagedate: 6 August 2012 Title: Enhanced downslope windstorm prediction with GOES warning indicators Status: Renewal Duration: 2 years Project Leads: Dr. Daniel Lindsey / NESDIS/RAMMB / dan.lindsey@noaa.gov Dr. Anthony Wimmers / U.Wisc-Madison/CIMSS / wimmers@ssec.wisc.edu Other Participants: Dan Bikos / CIRA, Ft. Collins, CO Eric Thaler / NWS Boulder, CO Randy Graham / NWS Salt Lake City, UT Stan Czyzyk/ NWS Las Vegas, NV Ken Pomeroy / NWS Western Region Scientific Services Division

2. 2. Project Summary • CIRA has developed a model-based downslope windstorm prediction model valid at one location • CIMSS has developed a GOES-based downslope signatures algorithm • Combine the two methods and expand to several sites in the Western U.S. • Develop an experimental downslope windstorm probability product using both GOES and model-based predictors valid at these locations

3. 3. Motivation / Justification • The growing population of the American West is expanding into areas prone to downslope windstorms, affecting personal safety and increasing the amount of vulnerable traffic Damage from a Wasatch Front downslope wind event in 2000. Image courtesy of Randy Graham, NWS SLC.

4. 3. Motivation / Justification • Numerical models (even the newer high resolution models) struggle with accurately forecasting downslope windstorms • The NWS has a need for more accurate and usable windstorm forecasting tools • Daily weather forecast is one of NOAA’s Mission Goals Semi tractor trailers rolled over from a January 2008 downslope wind event in eastern Oregon

5. 4. Methodology • Select a few locations in Colorado, Utah and Nevada prone to downslope windstorms, and collect their surface observations over several years • Using NARR data, determine the best model thresholds and predictors for high wind events at each location • Collect GOES and NARR data for several high wind event case studies, and optimize the model to reduce false alarms and create an improved GOES-derived downslope signatures product • Test the method on synthetic satellite imagery already being generated from the NSSL WRF model • Combine this improved GOES-derived product with the model predictors to create objective downslope windstorm probability models for each site

6. 5. Expected Outcomes • The production of a mature, GOES-derived downslope signatures product. • A next-generation downslope prediction model valid at select stations in the Mountain West using NWP, GOES and possibly synthetic satellite imagery. • At least one publication highlighting the satellite product, empirical model innovations and surface station validation. • As we apply the methods to the individual sites, we will consider how well a single approach can be generalized across terrain, and what modifications are required for different sites. This will advance the applied science of downslope wind forecasting, and build the foundation for a more generalized model valid across an entire region.

7. 6. First Year - Preliminary Results FY12 Milestones: • Choose surface stations and collect several years of data - We’ve coordinated with the SOOs at Boulder, Salt Lake City, and Las Vegas and have come up with a list of surface stations prone to downslope winds • Collect and examine NARR data to determine ideal model predictors for each site - As soon as the surface sites are finalized, NARR data will be collected and analyzed • Develop an updated version of the GOES-derived downslope signatures algorithm to work with the prediction model - The algorithm has been adapted to work with the regression model, and will be optimized in iterations as we do cal/val with NARR data (next slide)

8. 6. First Year - Preliminary Results Map showing initial surface stations to be used in the study Centerville, UT Fort Collins, CO Boulder, CO Fremont Junction, UT White Reef, UT

9. 6. First Year - Preliminary Results • Produced more GOES-derived downslope predictor tools for input into logistic regression: Downslope gradient Stationary pattern score Terrain pattern matching (& more)

10. 7. Possible Path to Operations • The new, mature product will replace the current experimental downslope forecasting page. • Given a successful validation, we will seek PSDI funding for transition to operations when appropriate. • We anticipate building on this research with an attempt to generalize the product for all terrains.

11. 8. FY13 Milestones • Optimize the prediction model that combines the GOES-derived algorithm with NWP model fields • Determine whether WRF forecast synthetic imagery is a viable predictor • Set up an experimental real-time forecast system for the chosen sites • Prepare at least 1 paper and present results at conferences

12. 9. Funding Request (K)

13. 10. Spending Plan FY13 • FY13 $88,000 Total Project Budget 1a. Grant to CIRA - $42,000 • % FTE (D. Bikos) - 42% • Travel - 0 • Publication charge - 0 1b. Grant to CIMSS - $45,000 • % FTE (Wimmers) - 30% • Travel (conference) - 2,000 • Publication charge - 3,000 2. Federal Publication Charges – $3,000