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.govdan.lindsey@noaa.gov Dr. Anthony Wimmers / U.Wisc-Madison/CIMSS / wimmers@ssec.wisc.eduwimmers@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 1

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 2

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 3 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 4 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) 5 Example output from the Lindsey et al. (2011) model-based high wind prediction model valid for Ft. Collins, CO 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 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 Windstorms associate with a positive change in water vapor Tb in the downslope direction 6 GOES WV Tb (K)

7. d. Methodology GOES-derived downslope signatures product 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. (50-80 mph wind event in Ferron, UT) GOES WV Tb (K) Downslope signature score 7

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

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. 9

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 10

11. e. Expected Outcomes 1.The production of a mature, GOES-derived downslope signatures product. 2.A next-generation downslope prediction model valid at select stations in the Mountain West using NWP, GOES and possibly synthetic satellite imagery. 3.At least one publication highlighting the satellite product, empirical model innovations and surface station validation. 4.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. 11

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. 12

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 13

14. g. Funding Request (K) Funding SourcesProcurement Office Purchase Items FY12FY13 GIMPAPStAR Total Project Funding 7990 StAR Grant to CI (CIRA) 39.540 StAR Grant to CI (CIMSS) 39.545 StAR Federal Travel 02 StAR Federal Publication 03 StAR Federal Equipment 00 StAR Transfers to other agencies 00 Other Sources 14

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 15

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 16