a. FY12-13 GIMPAP Project Proposal Title Page version 25 October 2011

1. a. FY12-13 GIMPAP Project Proposal Title Page version 25 October 2011 Title: Enhanced downslope windstorm prediction with GOES warning indicators Status: New product development 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. b. 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. c. 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. c. 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. d. Methodology • Leverage CIRA’s model-based windstorm prediction platform (Lindsey et al. 2011) and CIMSS’s satellite-based downslope signatures product (Wimmers and Feltz, 2010) • CIRA’s model-based statistical method uses output from the 0- to 84-hour NAM forecast to produce probabilities of high wind events in Ft. Collins, CO • Logistic regression was used on 13 years of NARR data and observations from Christman Field Weather station Example output from the Lindsey et al. (2011) model-based high wind prediction model valid for Ft. Collins, CO Lindsey, D. T., B. McNoldy, Z. Finch, D. Henderson, D. Lerach, R. Seigel, J. Steinweg-Woods, E. Stuckmeyer, D. Van Cleave, G. Williams, and M. Woloszyn, 2011: A high wind statistical prediction model for the northern front range of Colorado. Elec. Jou. Oper. Meteor., 2011-EJ03. Wimmers, A. J. and W. Feltz, 2010: Mountain wave detection as a hazard awareness tool for GOES-R, 6th Annual Symposium on Future National Operational Environmental Satellite Systems-NPOESS and GOES-R, AMS annual meeting.

6. d. Methodology GOES-derived downslope signatures product WV Tb gradient at slope GOES WV Tb (K) • Windstorms associate with a positive change in water vapor Tb in the downslope direction

7. d. Methodology GOES-derived downslope signatures product (50-80 mph wind event in Ferron, UT) GOES WV Tb (K) Downslope signature score • The downslope signatures derived product makes a grid-domain calculation using WV channel Tb and the underlying surface elevation to find these patterns, and estimate their region of influence.

8. d. Methodology GOES-derived downslope signatures product (50-80 mph wind event in Ferron, UT) GOES WV Tb (K) Downslope signature score • Preliminary work reveals a natural 4-6 hour lead-time between upper-level drying signatures and downslope windstorms

9. d. Methodology GOES-derived downslope signatures product: Plans for new development • Reduce false alarm rate by merging with CIRA’s predictive model. CIRA’s method filters for atmospheric conditions to provide complementary added value. • Develop the product further to make it resilient to phase-shifts between WV gradients and terrain downslopes. This will greatly increase accuracy.

10. d. Methodology: Project design • 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

11. e. 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.

12. e. 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.

13. f. Milestones Year 1: • Choose surface stations and collect several years of data • Collect and examine NARR data to determine ideal model predictors for each site • Develop an updated version of the GOES-derived downslope signatures algorithm to work with the prediction model Year 2: • 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

14. g. Funding Request (K)

15. g. Spending Plan FY12 • FY12 $79,000 Total Project Budget 1a. Grant to CIRA - $39,500 • % FTE (D. Bikos) - 40% • Travel - 0 • Publication charge - 0 1b. Grant to CIMSS - $39,500 • % FTE (Wimmers) - 30% • Travel - 0 • Publication charge - 0

16. g. Spending Plan FY13 • FY13 $90,000 Total Project Budget 1a. Grant to CIRA - $40,000 • % FTE (D. Bikos) - 40% • Travel - 0 • Publication charge - 0 1b. Grant to CIMSS - $45,000 • % FTE (Wimmers) - 30% • Travel (conference) - 2,000 • Publication charge - 3,000 2. Federal Travel – D. Lindsey to a conference 2,000 3. Federal Publication Charges – 3,000