1. Network of Networks: A Private-Sector Perspective 10 August 2009 AMS Summer Community Meeting Norman, OK Walter Dabberdt Vaisala CSO Boulder, CO

2. Page 2 / date / name / ©Vaisala Some Observations on NoN  Important follow-on to “Fair Weather”  Partnerships are crucial  Frames the problem(s) well  Impedance mismatch: mesoscale meteorology and synoptic observations  Offers important network design and architecture criteria (but not a network design per se)  Articulates the importance and challenges w/r/t observations of the PBL, humidity, air quality, soil moisture  Makes a strong case for comprehensive metadata & QA/QC  Need for and importance of ‘quasi-operational’ network testbeds  Frames the importance of stakeholders and their specific needs  Proposes a ‘soft’ model for a working relationship among the sectors

3. Page 3 / date / name / ©Vaisala Fig. 2.1 Time and space scales of ‘high-impact’ weather (source: NoN, 2008) Range of Scales

4. Page 4 / date / name / ©Vaisala Table 3.1 Spatial and temporal scales of several meteorological phenomena of consequence for the power-generation industry, and the required measurement resolution EventSpaceTimeMeasurement Resolution Heat wave (temp)500-1500 km2 days-1 week0.5°C, 10 km, 1 hr Wind a 1-2000 km1 min-4 days1 m s −1, 1 km, 1 min a Wind (for wind power)100 m-1000 km; to 1 km b 10 min-1 week0.5 m s −1, 100 m, 10 min; (1 m s −1, 30 m, 10min) b b Snow and ice storms50-1000 kmminutes-2 days1 mm snow water equiv. 1 cm snow, 1 km, 30 min Lightningregionminutes to hourslocation to 0.5 km Precipitation c basin to regionalHours-days, 1 mm, 1 km, 1 hr. seasonal to interannual c Cloudiness c local to regionaldaytime hourly to monthly0.1 sky, 10 km, 20 min c Waste heat impact10 km, lakes and rivers1 hour-4 days0.5°C, 100 m, 1 h Normal weatherurban (2 km); rural (30 km)20 min-climate a Could be associated with a Nor’easter (4 days), icing conditions, hurricanes or tornadoes (1 min), straight-line winds, or fire weather. b Measurements in the vertical direction. c Could be from short-term (management) or long-term (planning) for hydropower production. SOURCE: Derived from Schlatter et al. (2005). (source: NoN, 2008)

5. Page 5 / date / name / ©Vaisala Table 3.2 Key capabilities of key meteorological observations to meet public health and safety applications ParameterMeasurement Resolution Issue Horizontal Vertical Temporal Air Quality SurfaceFairn/aGood AloftPoorPoorPoor PBL Depth NBLPoorPoorPoor CBLFairFairPoor MBLPoorPoorPoor Winds SurfaceGoodn/aGood AloftFairFairPoor Temperature SurfaceGoodn/aGood AloftFairFairPoor Relative Humidity SurfaceGoodn/aGood AloftFairGoodPoor CloudsGoodGoodGood PrecipitationGoodn/aGood Pressure SurfaceGoodn/aGood AloftGoodGoodGood NOTE: NBL, CBL, and MBL refer to the nocturnal, continental and marine boundary layers, respectively. SOURCE: Tim Dye, Sonoma Technologies, Air Quality Community’s Meteorological Data Needs. (source: NoN, 2008)

6. Page 6 / date / name / ©Vaisala Some Issues in Creating a Public-Private-Academic Enterprise  Who provides what functions?  What sectors are engaged? Public? Private? Academia?  How are the parties selected? Entry criteria? Exit criteria?  How do they work together?  What is the business model?  What is the governance?  Who are the customers?  IP rights and issues?

7. Page 7 / date / name / ©Vaisala The Value Chain Decision Support Prediction Analyses Observations  Data Technology/Sensors/Systems To be successful, the “Enterprise” must participate throughout the value chain. But, who does what?

8. Page 8 / date / name / ©Vaisala Some Example Applications of the Enterprise  Transportation  Roads & railroads  Airports  Marine terminals and harbors  Energy industry  Demand and supply forecasting  Wind & solar power management  Distribution  Maintenance  Emergency management  Flooding  Toxic releases – accidental & deliberate  Public health and Safety  Forecasts  Watches and warnings  Air quality alerts  Heat stress and severe cold outbreaks  Construction management  High winds – e.g. tall crane ops  Lightning  Precipitation  Entertainment and Recreation  Outdoor entertainment & sporting venues  Agriculture  Freezes  Irrigation  Commodities exchange  Insurance industry

9. Page 9 / date / name / ©Vaisala The Value Chain Decision Support Prediction Analyses Observations  Data Technology/Sensors/Systems To be successful, the “Enterprise” must participate throughout the value chain. But, who does what?

10. Page 10 / date / name / ©Vaisala Component Functions of the Enterprise Civil Works Decision Support Archival Modeling Operations & Command & Control Analysis Infra Installation & Maintenance QA & QC Commun- ications Decision- Making & Actions Sales & Marketing R&D Governance Other? AWS Soil moisture Sensor & Other Suppliers Other Radar Profil- ers

11. Page 11 / date / name / ©Vaisala Some Rules of the Road  The value of testbeds  Learn during the demo phase  Test network designs  Establish relationships: B2B; B2G; G2B; G2G; B2G2A; etc.  Keep it simple  Play to the strengths of the different sectors  Make sure the goals are clearly defined and pursued  Address the needs of all levels of the value chain

12. Page 12 / date / name / ©Vaisala Primary strengths of the sectors  Public interest  Policy justification  Infrastructure  Stable environment (incl. research)  Standards (data, metadata, interface)  Innovation  Value-added products  Entrepreneurship  Agility  Risk taking  Efficiencies  Operational capabilities  Market expertise  Science  People (technical resource base)  Research risk- taking  Research centers  Neutral ground PublicPrivateAcademic Source: USWRP Mesoscale Workshop, Boulder, CO (2003)

13. Page 13 / date / name / ©Vaisala Strawman #1  Business as in the past  Government leads and pays  Industry is a contractual supplier of government-dictated products and services  Academia does the R&D

14. Page 14 / date / name / ©Vaisala Strawman #2  An emerging (though still limited) approach  Industry leads and takes financial risks and rewards  Government is a core customer among many customers  Academia does directed R&D for industry and government

15. Page 15 / date / name / ©Vaisala Other Strawmen  Industry, academia and government form a new joint venture? Isn’t this happening today with the banks and auto industry (govt. + industry) but also CPB, Amtrak, USPS?  Or, government creates a GOCO (Government-Owned, Contractor Operated facility that is owned by the Government and operated under contract by a non-governmental, private firm)  All parties do their own thing, collaborating where there is mutual benefit?

16. Page 16 / date / name / ©Vaisala The NoN Recommendation

17. Page 17 / date / name / ©Vaisala The CASA Approach  Vision: to enable vastly improved detection and prediction of adverse weather, and mitigate the associated societal and economic impacts  Goals: Implement, in an operational context, CASA- developed remote sensing and DCAS (together with other) technologies that will enable marked improvements in decision-making for a variety of applications  Strategy:  Throughout the remaining lifetime of the CASA ERC, develop, improve and test sensing, modeling, and decision-support tools  Deploy and test one or more advanced, quasi-operational networks to demonstrate the benefits and viability of the concept, which provide the justification for  Ultimately: Implement a nationwide capability

18. Page 18 / date / name / ©Vaisala CASA's Concept of a distributed adaptive network

19. Page 19 / date / name / ©Vaisala CASA, Sector Attributes & Partnering

20. Page 20 / date / name / ©Vaisala Industrial Advisory Board (IAB): Public Sector Members: NOAA-NWS DOE EC Private Sector Members: Vaisala Inc. Raytheon Co. EWR Weather Radar WeatherNews International ITT Electronic Systems-Gilfillan OneNet DeTect Inc. IBM Natl. Res. Institute for Earth Science and Disaster Prevention (NIED) News 9 Oklahoma State Board of Regents for Education University Partners: U. Mass. U. Oklahoma CSU UPR-Mayaguez CASA’s R2O Transition Plan private sector; public sector servic e Non-IAB Members IAB Members Non-IAB Members IAB Members & university partners Suppliers Create and operate a quasi- operational multi- functional network the enterprise IAB + Univs.

21. Page 21 / date / name / ©Vaisala Industrial Advisory Board (IAB): Public Sector Members: NOAA-NWS DOE EC Private Sector Members: Vaisala Inc. Raytheon Co. EWR Weather Radar WeatherNews International ITT Electronic Systems-Gilfillan OneNet DeTect Inc. IBM Natl. Res. Institute for Earth Science and Disaster Prevention (NIED) News 9 Oklahoma State Board of Regents for Education CASA’s R2O Transition Plan private sector; public sector servic e Non-IAB Members IAB Members Non-IAB Members IAB Members & university partners Suppliers ‘testbed’ = a quasi-operational multi-functional network the enterprise

22. Page 22 / date / name / ©Vaisala The End mailto: walter.dabberdt@vaisala.com