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ams2009scm-03-Dabberdt

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ams2009scm-03-Dabberdt

  1. 1. Network of Networks: A Private-Sector Perspective 10 August 2009 AMS Summer Community Meeting Norman, OK Walter Dabberdt Vaisala CSO Boulder, CO
  2. 2. Some Observations on NoN <ul><li>Important follow-on to “ Fair Weather ” </li></ul><ul><ul><li>Partnerships are crucial </li></ul></ul><ul><li>Frames the problem(s) well </li></ul><ul><li>Impedance mismatch: mesoscale meteorology and synoptic observations </li></ul><ul><li>Offers important network design and architecture criteria </li></ul><ul><li>(but not a network design per se) </li></ul><ul><li>Articulates the importance and challenges w/r/t observations of the PBL, humidity, air quality, soil moisture </li></ul><ul><li>Makes a strong case for comprehensive metadata & QA/QC </li></ul><ul><li>Need for and importance of ‘quasi-operational’ network testbeds </li></ul><ul><li>Frames the importance of stakeholders and their specific needs </li></ul><ul><li>Proposes a ‘soft’ model for a working relationship among the sectors </li></ul>
  3. 3. Fig. 2.1 Time and space scales of ‘high-impact’ weather (source: NoN, 2008) Range of Scales
  4. 4. <ul><li>Table 3.1 Spatial and temporal scales of several meteorological phenomena of consequence for the power-generation industry, and the required measurement resolution </li></ul><ul><li>Event Space Time Measurement Resolution </li></ul><ul><li>Heat wave (temp) 500-1500 km 2 days-1 week 0.5°C, 10 km, 1 hr </li></ul><ul><li>Wind a 1-2000 km 1 min-4 days 1 m s −1 , 1 km, 1 min </li></ul><ul><li>Wind (for wind power) 100 m-1000 km; to 1 km b 10 min-1 week 0.5 m s −1 , 100 m, 10 min; (1 m s −1 , 30 m, 10min) b </li></ul><ul><li>Snow and ice storms 50-1000 km minutes-2 days 1 mm snow water equiv. </li></ul><ul><li>1 cm snow, 1 km, 30 min </li></ul><ul><li>Lightning region minutes to hours location to 0.5 km </li></ul><ul><li>Precipitation c basin to regional Hours-days, 1 mm, 1 km, 1 hr. seasonal to interannual </li></ul><ul><li>Cloudiness c local to regional daytime hourly to monthly 0.1 sky, 10 km, 20 min </li></ul><ul><li>Waste heat impact 10 km, lakes and rivers 1 hour-4 days 0.5°C, 100 m, 1 h </li></ul><ul><li>Normal weather urban (2 km); rural (30 km) 20 min-climate </li></ul><ul><li>a Could be associated with a Nor’easter (4 days), icing conditions, hurricanes or tornadoes (1 min), straight-line winds, or fire weather. </li></ul><ul><li>b Measurements in the vertical direction. </li></ul><ul><li>c Could be from short-term (management) or long-term (planning) for hydropower production. </li></ul><ul><li>SOURCE: Derived from Schlatter et al. (2005). </li></ul>(source: NoN, 2008)
  5. 5. <ul><li>Table 3.2 Key capabilities of key meteorological observations to meet </li></ul><ul><li>public health and safety applications </li></ul><ul><li>Parameter Measurement Resolution Issue Horizontal Vertical Temporal </li></ul><ul><li>Air Quality </li></ul><ul><li>Surface Fair n/a Good </li></ul><ul><li>Aloft Poor Poor Poor </li></ul><ul><li>PBL Depth </li></ul><ul><li>NBL Poor Poor Poor </li></ul><ul><li>CBL Fair Fair Poor </li></ul><ul><li>MBL Poor Poor Poor </li></ul><ul><li>Winds </li></ul><ul><li>Surface Good n/a Good </li></ul><ul><li>Aloft Fair Fair Poor </li></ul><ul><li>Temperature </li></ul><ul><li>Surface Good n/a Good </li></ul><ul><li>Aloft Fair Fair Poor </li></ul><ul><li>Relative Humidity </li></ul><ul><li>Surface Good n/a Good </li></ul><ul><li>Aloft Fair Good Poor </li></ul><ul><li>Clouds Good Good Good </li></ul><ul><li>Precipitation Good n/a Good </li></ul><ul><li>Pressure </li></ul><ul><li>Surface Good n/a Good </li></ul><ul><li>Aloft Good Good Good </li></ul><ul><li>NOTE: NBL, CBL, and MBL refer to the nocturnal, continental and marine boundary layers, respectively. </li></ul><ul><li>SOURCE: Tim Dye, Sonoma Technologies, Air Quality Community’s Meteorological Data Needs. </li></ul>(source: NoN, 2008)
  6. 6. Some Issues in Creating a Public-Private-Academic Enterprise <ul><li>Who provides what functions? </li></ul><ul><li>What sectors are engaged? Public? Private? Academia? </li></ul><ul><li>How are the parties selected? Entry criteria? Exit criteria? </li></ul><ul><li>How do they work together? </li></ul><ul><li>What is the business model? </li></ul><ul><li>What is the governance? </li></ul><ul><li>Who are the customers? </li></ul><ul><li>IP rights and issues? </li></ul>
  7. 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. 8. Some Example Applications of the Enterprise <ul><li>Transportation </li></ul><ul><ul><li>Roads & railroads </li></ul></ul><ul><ul><li>Airports </li></ul></ul><ul><ul><li>Marine terminals and harbors </li></ul></ul><ul><li>Energy industry </li></ul><ul><ul><li>Demand and supply forecasting </li></ul></ul><ul><ul><li>Wind & solar power management </li></ul></ul><ul><ul><li>Distribution </li></ul></ul><ul><ul><li>Maintenance </li></ul></ul><ul><li>Emergency management </li></ul><ul><ul><li>Flooding </li></ul></ul><ul><ul><li>Toxic releases – accidental & deliberate </li></ul></ul><ul><li>Public health and Safety </li></ul><ul><ul><li>Forecasts </li></ul></ul><ul><ul><li>Watches and warnings </li></ul></ul><ul><ul><li>Air quality alerts </li></ul></ul><ul><ul><li>Heat stress and severe cold outbreaks </li></ul></ul><ul><li>Construction management </li></ul><ul><ul><li>High winds – e.g. tall crane ops </li></ul></ul><ul><ul><li>Lightning </li></ul></ul><ul><ul><li>Precipitation </li></ul></ul><ul><li>Entertainment and Recreation </li></ul><ul><ul><li>Outdoor entertainment & sporting venues </li></ul></ul><ul><li>Agriculture </li></ul><ul><ul><li>Freezes </li></ul></ul><ul><ul><li>Irrigation </li></ul></ul><ul><ul><li>Commodities exchange </li></ul></ul><ul><li>Insurance industry </li></ul>
  9. 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. 10. 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? Other? AWS Soil moisture Sensor & Other Suppliers Other Radar Profil- ers
  11. 11. Some Rules of the Road <ul><li>The value of testbeds </li></ul><ul><ul><li>Learn during the demo phase </li></ul></ul><ul><ul><li>Test network designs </li></ul></ul><ul><ul><li>Establish relationships: B2B; B2G; G2B; G2G; B2G2A; etc. </li></ul></ul><ul><li>Keep it simple </li></ul><ul><li>Play to the strengths of the different sectors </li></ul><ul><li>Make sure the goals are clearly defined and pursued </li></ul><ul><li>Address the needs of all levels of the value chain </li></ul>
  12. 12. Primary strengths of the sectors Source: USWRP Mesoscale Workshop, Boulder, CO (2003) <ul><ul><li>Public interest </li></ul></ul><ul><ul><li>Policy justification </li></ul></ul><ul><ul><li>Infrastructure </li></ul></ul><ul><ul><li>Stable environment (incl. research) </li></ul></ul><ul><ul><li>Standards (data, metadata, interface) </li></ul></ul><ul><ul><li>Innovation </li></ul></ul><ul><ul><li>Value-added products </li></ul></ul><ul><ul><li>Entrepreneurship </li></ul></ul><ul><ul><li>Agility </li></ul></ul><ul><ul><li>Risk taking </li></ul></ul><ul><ul><li>Efficiencies </li></ul></ul><ul><ul><li>Operational capabilities </li></ul></ul><ul><ul><li>Market expertise </li></ul></ul><ul><ul><li>Science </li></ul></ul><ul><ul><li>People (technical resource base) </li></ul></ul><ul><ul><li>Research risk- taking </li></ul></ul><ul><ul><li>Research centers </li></ul></ul><ul><ul><li>Neutral ground </li></ul></ul>Public Private Academic
  13. 13. Strawman #1 <ul><li>Business as in the past </li></ul><ul><li>Government leads and pays </li></ul><ul><li>Industry is a contractual supplier of government-dictated products and services </li></ul><ul><li>Academia does the R&D </li></ul>
  14. 14. Strawman #2 <ul><li>An emerging (though still limited) approach </li></ul><ul><li>Industry leads and takes financial risks and rewards </li></ul><ul><li>Government is a core customer among many customers </li></ul><ul><li>Academia does directed R&D for industry and government </li></ul>
  15. 15. Other Strawmen <ul><li>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? </li></ul><ul><li>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) </li></ul><ul><li>All parties do their own thing, collaborating where there is mutual benefit? </li></ul>
  16. 16. The NoN Recommendation
  17. 17. The CASA Approach <ul><li>Vision: to enable vastly improved detection and prediction of adverse weather, and mitigate the associated societal and economic impacts </li></ul><ul><li>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 </li></ul><ul><li>Strategy: </li></ul><ul><ul><li>Throughout the remaining lifetime of the CASA ERC, develop, improve and test sensing, modeling, and decision-support tools </li></ul></ul><ul><ul><li>Deploy and test one or more advanced, quasi-operational networks to demonstrate the benefits and viability of the concept, which provide the justification for </li></ul></ul><ul><ul><li>Ultimately: Implement a nationwide capability </li></ul></ul>
  18. 18. CASA's Concept of a distributed adaptive network
  19. 19. CASA, Sector Attributes & Partnering
  20. 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 University Partners: U. Mass. U. Oklahoma CSU UPR-Mayaguez private sector; public sector service 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. 21. 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 private sector; public sector service Non-IAB Members IAB Members Non-IAB Members IAB Members & university partners Suppliers ‘ testbed’ = a quasi-operational multi-functional network the enterprise
  22. 22. The End mailto: [email_address]

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