Network of Networks: A Private-Sector Perspective

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

2. 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. Range of Scales Fig. 2.1 Time and space scales of ‘high-impact’ weather (source: NoN, 2008)

4. Table 3.1 Spatial and temporal scales of several meteorological phenomena of consequence for the power-generation industry, and the required measurement resolution Event Space Time Measurement Resolution Heat wave (temp) 500-1500 km 2 days-1 week 0.5°C, 10 km, 1 hr Winda 1-2000 km 1 min-4 days 1 m s−1, 1 km, 1 min Wind (for wind power) 100 m-1000 km; to 1 kmb 10 min-1 week 0.5 m s−1, 100 m, 10 min; (1 m s−1, 30 m, 10min)b Snow and ice storms 50-1000 km minutes-2 days 1 mm snow water equiv. 1 cm snow, 1 km, 30 min Lightning region minutes to hours location to 0.5 km Precipitationc basin to regional Hours-days, 1 mm, 1 km, 1 hr. seasonal to interannual Cloudinessc local to regional daytime hourly to monthly 0.1 sky, 10 km, 20 min Waste heat impact 10 km, lakes and rivers 1 hour-4 days 0.5°C, 100 m, 1 h Normal weather urban (2 km); rural (30 km) 20 min-climate aCould be associated with a Nor’easter (4 days), icing conditions, hurricanes or tornadoes (1 min), straight-line winds, or fire weather. bMeasurements in the vertical direction. cCould be from short-term (management) or long-term (planning) for hydropower production. SOURCE: Derived from Schlatter et al. (2005). (source: NoN, 2008)

5. Table 3.2 Key capabilities of key meteorological observations to meet public health and safety applications ParameterMeasurement Resolution Issue Horizontal Vertical Temporal Air Quality SurfaceFair n/a Good AloftPoorPoorPoor PBL Depth NBL Poor Poor Poor CBL Fair FairPoor MBL PoorPoorPoor Winds SurfaceGood n/a Good AloftFair FairPoor Temperature SurfaceGood n/a Good AloftFair FairPoor Relative Humidity SurfaceGood n/a Good AloftFair GoodPoor CloudsGoodGoodGood PrecipitationGoodn/aGood Pressure SurfaceGood n/a Good 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. 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. 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. 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 Some Example Applications of the Enterprise

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

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

13. 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. 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. 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. The NoN Recommendation

17. 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. CASA's Concept of a distributed adaptive network

19. CASA’s R2O Transition Plan 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 IAB Members Non-IAB Members private sector; public sector Create and operate a quasi-operational multi-functional network the enterprise IAB + Univs. Suppliers Non-IAB Members IAB Members & university partners service

20. CASA’s R2O Transition Plan 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 IAB Members Non-IAB Members private sector; public sector ‘testbed’ = a quasi-operational multi-functional network the enterprise Suppliers Non-IAB Members IAB Members & university partners service

21. The End mailto: walter.dabberdt@vaisala.com