Societal Benefits of Winds Mission Ken Miller Mitretek

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

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

Control. Number 2 National Research Council Vision Statement 1 “A healthy, secure, prosperous and

Control. Number 2 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)

Control. Number 3 Societal Benefits from Improved Weather Forecasts Using Lidar Winds Civilian Improved

Control. Number 3 Societal Benefits from Improved Weather Forecasts Using Lidar Winds Civilian Improved Operational Weather Forecasts Military Hurricane Track Forecast Agriculture Transportation Energy Homeland Security Air Quality Forecast Recreation Ground, air & sea operations Weapons Delivery Satellite launch Aerial Refueling Dispersion Forecasts for Nuclear, Biological, & Chemical Release Science Climate Change Issues Circulation H 20, trace gases, aerosol, heat transport Carbon cycle Energy cycle

Control. Number 4 Quantifiable Economic Benefits l Recent estimates for Decadal Survey, ESTO study,

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

Control. Number 5 Findings l Benefits greater than in 1995 – Fuel costs –

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

Control. Number 6 US Economy Affected by Weather l 2005 GDP ~ $12. 5

Control. Number 6 US Economy Affected by Weather l 2005 GDP ~ $12. 5 Trillion l Percent of GDP affected by weather 17 – Nearly 30% directly or indirectly ($3. 75 Trillion) – About 10% directly ($1. 25 Trillion) l Mission benefits large vs. cost

Control. Number 7 Benefits Reviewed l Cordes Study 2, 3 – Quantified $ •

Control. Number 7 Benefits Reviewed l 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 l Considine et al Study 12 – Off-shore drilling rig decision optimization

Control. Number 8 Hurricanes: Loss of Life and Limb 11 l Not quantified here

Control. Number 8 Hurricanes: Loss of Life and Limb 11 l Not quantified here l Before Katrina, Red Cross estimated 25 K to 100 K deaths in a New Orleans worst case l Death rate for hurricanes with > $1 B property damage (20 yr avg to 2005) – All 128 / yr – Excluding Katrina, Andrew 34 / yr l “…late 20 th century forecasting prevents 90% of hurricane-related mortality that would occur with techniques used in the 1950 s”

Control. Number 9 Hurricane Over-Warning Savings 10, 11 l Evacuation cost: popular estimate $1

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

Control. Number 10 Reduce Hurricane Overwarning l Statistics – Typical warning – Affected coast

Control. Number 10 Reduce Hurricane Overwarning l Statistics – Typical warning – Affected coast – Overwarning l Benefits – Cost / mile – Over-warn cost / landfall – Reduce over-warning/landfall – x 2 landfalls / yr – Or scale 1995 to $1 M / mile 1995 2 341 miles 124 miles 217 miles 2005 11 300 - 400 100 200 - 300 $145 K $ 32 M $ 5. 4 M (17%*) $ 11 M $ 1 M $50 M (50 miles) 100 M** $ 75 M 2005 Evacuation Avoidance: $75 to 100 M/yr * Storm climatology and simulations for global 3 D winds in NWP ** Ref 11, better forecasts, not necessarily wind measurement alone

Control. Number 11 Direct Hurricane Property Damage 11 l Much not preventable l Hard

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

Control. Number 12 Direct Property Damage Savings (1995)2 l 13 yr to 1995 avg

Control. Number 12 Direct Property Damage Savings (1995)2 l 13 yr to 1995 avg damage – Selected “typical” storms w/o Andrew = $1. 2 B / yr l Assumed – 15% preventable with sufficient warning – 17% forecast improvement with winds vs. 1995 24 hr – Total 15% x 17% = 2. 5% l Reduction, typical hurricanes = $30 M / yr

Control. Number 13 Update Direct Damage l For > $B storms l 20 yr

Control. Number 13 Update Direct Damage l For > $B storms l 20 yr avg to 2005 (2005 $) – About 1 landfall / yr > $1 B – $7. 1 B / yr less Andrew, Katrina – $15. 7 B / yr counting Andrew, Katrina

Control. Number 14 Update Direct Damage (concluded) l Using the lower number – $7.

Control. Number 14 Update Direct Damage (concluded) l Using the lower number – $7. 1 B / yr without Andrew, Katrina – Account for lesser hurricanes 22 • Divide by. 83 • Total = $8. 5 B / yr l Reduce preventable losses 2. 5% 2 l 2005 Savings Estimate = $212 M / yr

Control. Number 15 Off-shore Drilling Rigs l Not in Cordes study l Gulf Rigs

Control. Number 15 Off-shore Drilling Rigs l Not in Cordes study l 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 l Assume 17% improvement 2 x ($239 M-$10 M) l Benefit = $39 M / yr

Control. Number 16 General Forecasting l Winds improve accuracy and lead times l Forecasts

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

Control. Number 17 Some Industries Affected by Weather 16, 18

Control. Number 17 Some Industries Affected by Weather 16, 18

Control. Number 18 General Forecasting (1995)2 l Chapman study 20 – Estimated gains from

Control. Number 18 General Forecasting (1995)2 l Chapman study 20 – Estimated gains from maximum forecast improvement from NWS modernization – $1. 2 B / year in 1992 l Cordes 2 – Assumed winds provide 5% of max – $60 M / yr ($1992)

Control. Number 19 Scale 1995 General Forecasting Estimate by GDP l 1992 – GDP

Control. Number 19 Scale 1995 General Forecasting Estimate by GDP l 1992 – GDP = 5. 6 Trillion 1992$ – Benefit was $60 M – 0. 0011 % of GDP l 2005 GDP estimate = $12. 5 Trillion l 2005 benefit scales to > $137 M / yr

Control. Number 20 Forecast Improvements Example 1: Households l Household benefit estimates for better

Control. Number 20 Forecast Improvements Example 1: Households l Household benefit estimates for better forecasts • $1. 7 B / yr (2003) 13 • $1. 87 B / yr (2005) 14 l This doesn’t include industry benefits

Control. Number 21 Forecast Benefits Example 2: ENSO 17 l El Nino Southern Oscillation

Control. Number 21 Forecast Benefits Example 2: ENSO 17 l 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 l Total $211 -326 M l Can winds help?

Control. Number 22 Forecast Benefits Example 3: Est. Marginal GOES-R Benefits 15 $M /

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

Control. Number 23 Conclusions from General Forecasting Examples l Big benefits: examples = $1191

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

Control. Number 24 U. S. Airlines Fuel Savings l User preferred routing – Critical

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

Control. Number 25 U. S. Airlines Fuel: 1995 Estimate 2 l Background – Fuel

Control. Number 25 U. S. Airlines Fuel: 1995 Estimate 2 l Background – Fuel consumption effects • 50 knot wind ~ 11% fuel impact (FAA, early 1980 s) • Haul extra fuel for unknown wind conditions – Real time vs. NWS forecast winds cut flight time 4. 2% (simulation early 1980 s) l Cordes 2 lidar fuel savings estimates – 0. 5% domestic – 1. 0% international, less wind information available 2006 Savings = $107 M / yr (1994$)

Control. Number 26 Update U. S. Airlines Fuel l 2006 (annualized Jan - Nov

Control. Number 26 Update U. S. Airlines Fuel l 2006 (annualized Jan - Nov data)5 – 19. 3 billion gallons @ average $1. 972 / gal = $38 B – 72% for domestic flight, 28% international l Estimated savings with wind data – Domestic $137 M – International $106 M 2006 US Airlines Savings Estimate= $243 M

Control. Number 27 U. S. Military Aviation Fuel for 2006 (Cordes 1998)3 Military Aviation

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

Control. Number 28 Military Aviation – More Recent Numbers 21 l AF jet fuel

Control. Number 28 Military Aviation – More Recent Numbers 21 l AF jet fuel usage 2. 6 B gallons / year l 53% over continental US l Cost – ~ $2. 40 / gal – vs. $0. 63 in 1995 study – Transport to plane $1. 30 / gal

Control. Number 29 Military Aviation Update* * Basis is 2. 6 B gallons /

Control. Number 29 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.

Control. Number 30 2006 Annual Benefits Estimates ($M)

Control. Number 30 2006 Annual Benefits Estimates ($M)

Control. Number 31 Conclusions l Dollar benefit estimates have increased – Fuel costs –

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

Control. Number 32 References 1. National Academy of Science, Earth Science and Applications from

Control. Number 32 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 2. Cordes, J. J. , “Economic Benefits and Costs of Developing and Deploying A Space. Based Wind Lidar, ” GWU, NOAA Contract 43 AANW 400233, 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/Monthly. Jet. Fuel. htm 6. http: //fermat. nap. edu/books/0309087155/html/9. html reference to Zeiger and Smith, 1998 7. http: //www. oilendgame. com/pdfs/Media. Kit/Media. Wt. OEg_Mil. Facts. pdf 8. NOAA National Climatic Data Center, www. ncdc. noaa. gov/oa/reports/billionz. html 9. Weather. Zine No. 18, October 1999, http: //sciencepolicy. colorado. edu/zine/archives/129/txt/zine 18. txt 10. UCAR Quarterly, Spring 1999, http: //www. ucar. edu/communications/quarterly/spring 99/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 the. Art. 1. pdf

Control. Number 33 References 12. T. Considine et al, “The value of hurricane forecasts

Control. Number 33 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 13. “The Economic Value of Current and Improved Weather Forecasts to U. S. Households”, NOAA Magazine, 2003 http: //www. magazine. noaa. gov/stories/mag 99. 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. Economic. Benefits. Final. REPORT 2. 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 17. Weiher et al, “Valuing Weather Forecasts”, 2003 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 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

Control. Number 34 References 20. R. Chapman, “Benefit-Cost analysis for the modernization and associated

Control. Number 34 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: 19251995, ” Weather and Forecasting, 13: 621 -631 http: //www. aoml. noaa. gov/hrd/Landsea/USdmg/

Control. Number 35 Backup Charts

Control. Number 35 Backup Charts

Control. Number 36 Weather in Economic Decision Making 2 Simple Decision Model P= Probability

Control. Number 36 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

Control. Number 37 Better Forecasts Make Better Decisions l P is the weather forecast

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