Challenges in Urban Meteorology:

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Challenges in Urban Meteorology A Forum for
Users and Providers (September 21-23,
2004) Panel 4 Research and Development for
Urban Weather and Climate Applications Lloyd
Treinish IBM Thomas J. Watson Research
Center Yorktown Heights, NY lloydt_at_us.ibm.com http
//www.research.ibm.com/weather http//www.researc
h.ibm.com/people/l/lloydt
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Panel 4 Research and Development for Urban
Weather and Climate Applications

• We are a small weather modelling group at IBM
  Research focusing on understanding of mesoscale
  weather as it relates to weather-sensitive
  operations and decision support, and developing
  solutions for such applications
• We have developed an operational, prototype
  NWP-based system, which has provided regular
  forecasts for the NYC-metropolitan area at 1 km
  resolution as a testbed for over 3 years (Deep
  Thunder)
• To evaluate both meteorological and business
  value beyond physical realism
• To evaluate the level of practicality and
  usability at reasonable cost
• To develop an operational end-to-end
  infrastructure and automation with focus on
  high-performance computing, visualization and
  system integration
• To prototype business applications with actual
  end users
• Additional testbeds at 2 km resolution for
  Chicago and Kansas City established earlier this
  year
• Although work is on-going and capabilities have
  limitations, it is sufficiently evolved for some
  practical urban applications and to assess
  additional needs

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Current Focus and Issues

• Several specific urban (and suburban)
  applications with short-term (three to 18 hours)
  weather sensitivity
• Emergency management, homeland security, energy
  (distribution, operations and generation), road
  maintenance and operations, traffic management
  and airport terminal operations
• Results to date are sufficiently compelling to
  enable discussions with local government agencies
  and private companies for each of these
  applications in several urban areas in the US
• Unfortunately, these interactions are ad hoc and
  duplicative because
• No formal mechanisms exist to engage such groups
  or to transfer technology or services
• There is no central clearing house of information
  for urban decision makers to become aware of such
  potential capabilities
• Methods are needed to properly validate the
  utility of such forecasting systems for these
  applications
• Traditional meteorological (statistical)
  verification is not appropriate for many urban
  applications
• True end-user metrics are often ill-defined or
  simply qualitative at present
• What level of capability is good enough for an
  effective deployment ?
• Need for a heads-up for severe weather event,
  even with opportunity for phase errors vs.
  standard zone forecasts, which may be too vague
• More focused dissemination since standard
  meteorological products are a clear mismatch with
  end-user decision makers, whose expertise is in
  applications and understanding the impact of
  weather as opposed to meteorology

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An Example (Among Many) NYC Metropolitan Area
Heavy Rainfall Event -- 8 September 2004

• Remnants of Hurricane Frances moved into the NYC
  metropolitan area early in the morning of
  September 8
• The heaviest rainfall occurred in an area
  stretching from northeastern New Jersey through
  central Westchester County, NY with amounts in
  excess of 5" in some areas
• There was widespread disruption of transportation
  systems (e.g., road closures, flooded subways,
  airport delays) and significant flooding in
  several regions
• Evening NWS zone forecast (2130 EDT, 7 September)
  for the next day "showers and a slight chance
  of thunderstorms, rain may be heavy at time in
  the morning"
• Revised NWS zone forecast (0440 EDT, 8
  September), adding "locally heavy rain
  possible"
• NWS issued a flash flood watch at 0748 EDT

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Forecast Results for Other Applications

• Interest in surface and upper air winds dictates
  entirely different presentation
• Virtual wind profilers at two locations within
  1 km nest enhanced with trajectories to show
  forecasted propagation

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Needs and Future Directions

• Despite promising results to date, improvements
  are clearly needed in several key areas
• Improved model representations of boundary layer
  physics and cloud microphysics
• Better observing (sampling) strategies coupled
  with data assimilation to reduce errors in
  initial conditions
• Continued advances in overall system
  cost-effectiveness (performance, throughput and
  usability)
• Further prototyping and development of systems
  that can be used with confidence is required
• End-to-end tailoring for specific application
  focus (throughput, physics and dissemination)
  forecast products when they are needed in the way
  that they are needed
• While RD continues, deploy now even with
  limitations, to enable earlier understanding of
  operational constraints and issues as well as to
  develop user-oriented metrics
• Establish additional testbeds for specific urban
  weather issues or application sensitivity
• Emphasize further two-way education and
  collaboration with potential beneficiaries

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Needs and Future Directions

• Appropriate visualization is critical to enable
  meteorological data to be usable for urban
  applications methods are available but not
  widely utilized
• Understanding of how weather data need to be used
  and why (e.g., human factors concerning how users
  work and interact)
• Understanding of how users perceive and interpret
  weather visualizations
• Data must be made relevant for different classes
  of users using their terminology, and thus,
  expressible in terms that can be readily
  understood in real-time without expert
  interpretation
• Effective coupling to derived modelling and
  analysis for proactive planning is necessary but
  much more work needs to be done
• Meteorology needs to be reasonably correct (good
  enough) first, which has been the primary focus
  for RD
• Direct match to relevant physical problem (e.g.,
  pavement, dispersion)
• Direct match to relevant operational problem
  (e.g., crew and equipment optimization
  scheduling and routing that is impacted by
  weather)