1. Societal Benefits of Winds Mission Ken Miller Mitretek Systems February 8, 2007

2. 2ControlNumber National Research Council Vision Statement 1 “A healthy, secure, prosperous and sustainable society for all people on Earth” “Understanding the … planet …, how it supports life, and how human activities affect its ability to do so … is one of the greatest intellectual challenges facing humanity...” NRC (April 2005)

3. 3ControlNumber Societal Benefits from Improved Weather Forecasts Using Lidar Winds Improved Operational Weather Forecasts CivilianMilitary Hurricane Track ForecastGround, air & sea operations Agriculture Weapons Delivery Transportation Satellite launch EnergyAerial Refueling Homeland Security Dispersion Forecasts for Air Quality Forecast Nuclear, Biological, Recreation & Chemical Release Science Climate Change Issues Circulation H20, trace gases, aerosol, heat transport Carbon cycle Energy cycle

4. 4ControlNumber Recent estimates for Decadal Survey, ESTO study, Lidar Winds white paper Based on 1995, 1998 estimates (Cordes) 2,3 –Key assumptions Seem conservative Hard to validate Purpose –Update estimates –Added a benefit area –Increased from $228 M to $807 M / yr –Compared with estimates from other programs Quantifiable Economic Benefits

5. 5ControlNumber Findings Benefits greater than in 1995 –Fuel costs –Coastal population –Property values –GDP growth –Inflation –Added offshore drilling rig benefits Magnitudes in line with other case studies Additional benefits could be included Recommend more study of assumptions

6. 6ControlNumber US Economy Affected by Weather 2005 GDP ~ $12.5 Trillion Percent of GDP affected by weather 17 –Nearly 30% directly or indirectly ($3.75 Trillion) –About 10% directly ($1.25 Trillion) Mission benefits large vs. cost

7. 7ControlNumber Benefits Reviewed Cordes Study 2,3 –Quantified $ Reduce hurricane over-warnings Reduce hurricane preventable damage and business interruption Save aviation fuel using wind in routing General forecast improvement –Not $ - Loss of life and limb Considine et al Study 12 –Off-shore drilling rig decision optimization

8. 8ControlNumber Hurricanes: Loss of Life and Limb 11 Not quantified here Before Katrina, Red Cross estimated 25K to 100K deaths in a New Orleans worst case Death rate for hurricanes with > $1 B property damage (20 yr avg to 2005) –All128 / yr –Excluding Katrina, Andrew 34 / yr “…late 20 th century forecasting prevents 90% of hurricane-related mortality that would occur with techniques used in the 1950s”

9. 9ControlNumber Hurricane Over-Warning Savings 10,11 Evacuation cost: popular estimate $1 M / mile Regional dependence –Could exceed $50M / mile in some areas –Much less in other areas Hurricane Floyd (1999) evacuation cost rivaled damage cost 9

10. 10ControlNumber Statistics1995 2 2005 11 –Typical warning 341 miles300- 400 –Affected coast 124 miles 100 –Overwarning217 miles200- 300 Benefits –Cost / mile$145 K$ 1 M –Over-warn cost / landfall$ 32 M –Reduce over-warning/landfall$ 5.4 M (17% * ) $50 M (50 miles) –x 2 landfalls / yr$ 11 M 100 M** –Or scale 1995 to $1M / mile$ 75 M Reduce Hurricane Overwarning * Storm climatology and simulations for global 3D winds in NWP ** Ref 11, better forecasts, not necessarily wind measurement alone 2005 Evacuation Avoidance: $75 to 100 M/yr

11. 11ControlNumber Direct Hurricane Property Damage 11 Much not preventable Hard to demonstrate reduction –Probably improves over time –Growth & property values increase losses –“…no discernable trend from better forecasts or more effective mitigation measures”

12. 12ControlNumber Direct Property Damage Savings (1995) 2 13 yr to 1995 avg damage –Selected “typical” storms w/o Andrew = $1.2 B / yr Assumed –15% preventable with sufficient warning –17% forecast improvement with winds vs. 1995 24 hr –Total 15% x 17% = 2.5% Reduction, typical hurricanes = $30 M / yr

13. 13ControlNumber Update Direct Damage For > $B storms 20 yr avg to 2005 (2005 $) –About 1 landfall / yr > $1B –$7.1 B / yr less Andrew, Katrina –$15.7 B / yr counting Andrew, Katrina

14. 14ControlNumber Update Direct Damage (concluded) Using the lower number –$7.1 B / yr without Andrew, Katrina –Account for lesser hurricanes 22 Divide by.83 Total = $8.5 B / yr Reduce preventable losses 2.5% 2 2005 Savings Estimate = $212 M / yr

15. 15ControlNumber Not in Cordes study Gulf Rigs 12 –Need hurricane track/intensity –Optimize operating decisions: continue, evacuate, stop production –Estimated value of 24 hour forecast Perfect $239 M / yr Imperfect$ 10 M / yr Assume 17% improvement 2 x ($239 M-$10 M) Benefit = $39 M / yr Off-shore Drilling Rigs

16. 16ControlNumber General Forecasting Winds improve accuracy and lead times Forecasts impact the economy How much?

17. 17ControlNumber Some Industries Affected by Weather 16, 18

18. 18ControlNumber Chapman study 20 –Estimated gains from maximum forecast improvement from NWS modernization –$1.2 B / year in 1992 Cordes 2 –Assumed winds provide 5% of max –$60 M / yr ($1992) General Forecasting (1995) 2

19. 19ControlNumber Scale 1995 General Forecasting Estimate by GDP 1992 –GDP = 5.6 Trillion 1992$ –Benefit was $60M –0.0011 % of GDP 2005 GDP estimate = $12.5 Trillion 2005 benefit scales to > $137 M / yr

20. 20ControlNumber Forecast Improvements Example 1: Households Household benefit estimates for better forecasts $1.7 B / yr (2003) 13 $1.87 B / yr (2005) 14 This doesn’t include industry benefits

21. 21ControlNumber Forecast Benefits Example 2: ENSO 17 El Nino Southern Oscillation (ENSO) savings estimate –U.S. agriculture $200-300 M –U.S. corn storage$ 10- 25 M –NW US salmon fishery$ 1 M Total $211-326 M Can winds help?

22. 22ControlNumber Forecast Benefits Example 3: Est. Marginal GOES-R Benefits 15 $M / yr Agriculture –Avoided irrigation costs 15 41 –Orchard frost mitigation 9 Transportation –Flight delays 41 –Trucking 28 – 56 Recreational Boating 86 - 130 Energy Utilities 486 - 507 Total of Case Studies 691 - 784

23. 23ControlNumber Conclusions from General Forecasting Examples Big benefits: examples = $1191 to 1428 M / yr Is our $137 M “in the ballpark” or low? Can add important benefits to list

24. 24ControlNumber U.S. Airlines Fuel Savings User preferred routing –Critical capability for FAA Next Generation Air Transportation System (NGATS) –Wind optimal routing can save fuel Benefits: economic, environmental, energy security

25. 25ControlNumber U.S. Airlines Fuel: 1995 Estimate 2 Background –Fuel consumption effects 50 knot wind ~ 11% fuel impact (FAA, early 1980s) Haul extra fuel for unknown wind conditions –Real time vs. NWS forecast winds cut flight time 4.2% (simulation early 1980s) Cordes 2 lidar fuel savings estimates –0.5% domestic –1.0% international, less wind information available 2006 Savings= $107 M / yr (1994$)

26. 26ControlNumber 2006 (annualized Jan - Nov data) 5 –19.3 billion gallons @ average $1.972 / gal = $38 B –72% for domestic flight, 28% international Estimated savings with wind data –Domestic $137 M –International $106 M Update U.S. Airlines Fuel 2006 US Airlines Savings Estimate= $243 M

27. 27ControlNumber U.S. Military Aviation Fuel for 2006 (Cordes 1998) 3 Military Aviation Savings ~ $20 M (1994$)

28. 28ControlNumber Military Aviation – More Recent Numbers 21 AF jet fuel usage 2.6 B gallons / year 53% over continental US Cost –~ $2.40 / gal –vs. $0.63 in 1995 study –Transport to plane $1.30 / gal

29. 29ControlNumber Military Aviation Update* * Basis is 2.6 B gallons / yr for AF, $2.40 / gal for fuel, $1.30 / gal for transport to plane, estimated Navy usage using ratio from Ref 3.

30. 30ControlNumber 2006 Annual Benefits Estimates ($M)

31. 31ControlNumber Conclusions Dollar benefit estimates have increased –Fuel costs –Increased coastal population and property values –Growing GDP –Added offshore drilling rig benefits Magnitudes seem in line with other weather case studies Assumptions should be reviewed Significant benefit areas may not be included yet

32. 32ControlNumber References 1. National Academy of Science, Earth Science and Applications from Space, Briefing of Decadal Survey Findings, AMS Town Hall, 1/15/07 http://www.nap.edu/catalog/11820.html http://www.nap.edu/catalog/11820.html 2. Cordes, J. J., “Economic Benefits and Costs of Developing and Deploying A Space- Based Wind Lidar,” GWU, NOAA Contract 43AANW400233, March 1995 3. Cordes, J. J., Memorandum to W. Baker, “Projected Benefits in Military Fuel Savings from Lidar,” June, 1998 4. Kakar, R., et al, “An Advanced Earth Science Mission Concept Study for GLOBAL WIND OBSERVING SOUNDER,” NASA HQ, December 2006. 5. Air Transport Association, Jan thru Nov 2006, average airline paid price and consumption: http://www.airlines.org/economics/energy/MonthlyJetFuel.htmhttp://www.airlines.org/economics/energy/MonthlyJetFuel.htm 6. http://fermat.nap.edu/books/0309087155/html/9.html reference to Zeiger and Smith, 1998http://fermat.nap.edu/books/0309087155/html/9.html 7. http://www.oilendgame.com/pdfs/MediaKit/MediaWtOEg_MilFacts.pdfhttp://www.oilendgame.com/pdfs/MediaKit/MediaWtOEg_MilFacts.pdf 8. NOAA National Climatic Data Center, www.ncdc.noaa.gov/oa/reports/billionz.htmlwww.ncdc.noaa.gov/oa/reports/billionz.html 9. WeatherZine No. 18, October 1999, http://sciencepolicy.colorado.edu/zine/archives/1- 29/txt/zine18.txthttp://sciencepolicy.colorado.edu/zine/archives/1- 29/txt/zine18.txt 10.UCAR Quarterly, Spring 1999, http://www.ucar.edu/communications/quarterly/spring99/USWRP.html http://www.ucar.edu/communications/quarterly/spring99/USWRP.html 11. H. Willoughby, E. Rappaport, F. Marks, “Hurricane forecasting, the state of the art,” Hurricane Socioeconomic Working Group, Feb 16-18, 2005, http://www.sip.ucar.edu/pdf/01_Hurricane_Forecasting_the_State_of theArt.1.pdf http://www.sip.ucar.edu/pdf/01_Hurricane_Forecasting_the_State_of theArt.1.pdf

33. 33ControlNumber References 12. T. Considine et al, “The value of hurricane forecasts to oil and gas producers in the Gulf of Mexico”, Journal of Applied Meteorology, 43, 1270-1281. http://www.isse.ucar.edu/HP_rick/energy.html http://www.isse.ucar.edu/HP_rick/energy.html 13. “The Economic Value of Current and Improved Weather Forecasts to U.S. Households”, NOAA Magazine, 2003 http://www.magazine.noaa.gov/stories/mag99.htmhttp://www.magazine.noaa.gov/stories/mag99.htm 14. J. Lazo, NCAR, “What are Weather Forecasts Worth?” CANSEE, October 28, 2005 15. Williamson, Hertzfeld, Cordes, “The Socio-Economic Value of Improved Weather and Climate Information”, GWU, 2002 http://www.gwu.edu/~spi/Socio- EconomicBenefitsFinalREPORT2.pdfhttp://www.gwu.edu/~spi/Socio- EconomicBenefitsFinalREPORT2.pdf 16. “Methodologies for the Assessment of Costs and Benefits of Meteorological Services,” http://www.wmo.ch/web/spla/R&Op-II(02)APPENDIX_D.doc http://www.wmo.ch/web/spla/R&Op-II(02)APPENDIX_D.doc 17. Weiher et al, “Valuing Weather Forecasts”, 2003 http://www.economics.noaa.gov/librarly/documents/social_science_initiative/workshop_ briefing_book-ww.pdf http://www.economics.noaa.gov/librarly/documents/social_science_initiative/workshop_ briefing_book-ww.pdf 18. Teisberg, “Valuing Weather Forecasts: Methods, Examples, Next Steps,” http://www.economics.noaa.gov/librarly/documents/social_science_initiative/workshop_ briefing_book-ww.pdf http://www.economics.noaa.gov/librarly/documents/social_science_initiative/workshop_ briefing_book-ww.pdf 19.”Inventory of Estimates of Value of Weather Information and References” http://www.economics.noaa.gov/librarly/documents/social_science_initiative/workshop_ briefing_book-ww.pdf http://www.economics.noaa.gov/librarly/documents/social_science_initiative/workshop_ briefing_book-ww.pdf

34. 34ControlNumber References 20. R. Chapman, “Benefit-Cost analysis for the modernization and associated restructuring of the National Weather Service,” NISTIR 4867. Report to NIST, 1992 21. M. Babcock, USAF, memoranda to K. Miller, January 2007 22. R. Pielke and Landsea, “Normalized Hurricane Damages in the United States:1925- 1995,” Weather and Forecasting, 13:621-631 http://www.aoml.noaa.gov/hrd/Landsea/USdmg/ http://www.aoml.noaa.gov/hrd/Landsea/USdmg/

35. 35ControlNumber Backup Charts

36. 36ControlNumber Weather in Economic Decision Making 2 Simple Decision Model P = Probability of adverse weather event L = Loss from adverse weather event S = Savings by preventive action (given adverse event) C = Cost of action Cost =0 if no adverse event Then: Expected loss without action = PL Expected loss with action = P(L-S) + C If PS > C, it is rational to act

37. 37ControlNumber Better Forecasts Make Better Decisions P is the weather forecast (neglecting the complexities) If less uncertainty in P –People use it more –Better economic decisions Evacuation decisions will be conservative since loss of life is a factor