1. NCAR Research Applications Laboratory
Completing the Forecast—Bridging Weather Predictions to User Applications
Matthias Steiner
NCAR Research Applications Laboratory
Everybody stole thunder by now – so maybe a summary of various discussion points so far
WMO WWRP Workshop on Use of NWP for Nowcasting NCAR Center Green in Boulder, Colorado
Wednesday, 26 October 2011

2. Weather Forecasting & its Uncertainty
NRC Report 2006
Uncertainty is a fundamental characteristic of weather, seasonal climate, & hydrological prediction, & no forecast is complete without a description of its uncertainty.
WMO Report 2008
Effective communication of forecast uncertainty manages user expectations, builds confidence, & enables better decision making.
AMS Report 2011
Plan to define a vision, strategic goals, roles & responsibilities, & an implementation roadmap that will guide the weather & climate enterprise toward routinely providing the Nation with comprehensive, skillful, reliable, & useful information about the uncertainty of weather, water, & climate forecasts.
Uncertainty information probably as important as forecast in first place for decision makers 2

3. Wide Range of Decisions based on Weather Forecasts
Air Traffic Manager:
How many airplanes can airport handle?
Risk of delays & diversions
high
It’s about sensitivity of a user or sector to aspects of weather, assessment of risks, & readiness to cope with weather impacts!
Public User:
Do I take umbrella
today – yes/no?
Risk of getting soaked
Consequences of Bad Decision
medium
Broad range of weather information users, with various degrees of sensitivity to weather, requirements regarding space & time scales, what weather aspects matter, forecast lead time need to be able to respond, etc.
Even for similar users, readiness to cope with impacts vary significantly (e.g., 1 inch of snow for DIA is no big deal but would be shutting airport down for ATL)
Other factors that affect aviation may be runway construction, president flying into that location, etc.
CAUTION: It’s not just about weather, there are many other factors that influence a user’s decision!
low
low
medium
high
Degree of User Requirements & Sophistication 3

4. Collaboration instead of Throwing over Fence
Weather
User
Public Safety
Recreation
Transportation
Utilities
Construction
Agriculture
Emergency
etc.
Forecast Products
Decision to be made
Effective forecasts have to be tailored to specific user needs
One solution fits all doesn’t exist, need to tailor forecasts for each user application (i.e., extract what matters to decision making process)
Throwing forecast over fence (or putting it on website) isn’t solution 4

5. Tailoring of Forecasts – Translation & Integration
Weather
Information
Weather analyses & forecast data
Weather
Translation
Extraction of relevant information
Impact
Estimation
Placing into situational context
Response
Scenarios
Mitigation strategies
Weather Information Provider
Weather-impacted User
It is not about doing each other’s job, but understanding enough to help . . .
Building effective bridges between providers & users of weather information requires:
understanding information needs as well as communicating capabilities & limitations (it’s a two-way street)
providing weather information relevant to user’s decision making
training in products & building of trust 5

6. Example #1: Air Traffic Management
Weather
Information
Weather analyses & forecast data
Storm intensity & echo tops
Weather
Translation
Extraction of relevant information
Aviation constraints or threshold events
Sector capacity & workload impact
Impact
Estimation
Placing into situational context
Strategic traffic flow management initiatives & tactical programs
Response Scenarios
Mitigation strategies 6

7. Example #2: Coping with Hurricanes
Weather
Information
Weather analyses
& forecast data
Hurricane track, size, & intensity
Storm surge, flooding, inundated areas
Weather
Translation
Extraction of relevant information
Affected population & infrastructure, disruption of services, damages due to wind & water, etc.
Impact
Estimation
Placing into situational context
Implementation of evacuation & recovery plans
Response Scenarios
Mitigation strategies 7

8. Example #3: Water Resources Management
Weather
Information
Weather analyses
& forecast data
Rainfall (or lack thereof)
Runoff & flow into reservoir, water levels behind dam
Weather
Translation
Extraction of relevant information
Dam overflow, water rights, or minimal streamflow for fish
Impact
Estimation
Placing into situational context
Controlled release of water & timing thereof
Response Scenarios
Mitigation strategies 8

9. Example #4: Wind Energy Harvesting
Weather
Information
Weather analyses
& forecast data
Wind & variability
Wind at hub height, min/max thresholds, & ramp events
Weather
Translation
Extraction of relevant information
Energy generated by windfarms
Impact
Estimation
Placing into situational context
Balancing power grid using different energy sources
Response Scenarios
Mitigation strategies 9

10. Probabilistic Forecasting using Ensembles
Look at every ensemble member from a user perspective
& ensemble “user relevant information” instead of weather 10

11. Example #5: Aviation Capacity Prediction
User: Air Traffic Planners
Example #5: Aviation Capacity Prediction
Probability of losing fraction of capacity due to weather?
Translation
Impacting weather reduces usable air space
Extraction of capacity reduction based on each member of ensemble forecast
Focus on storm hazard & its organization (permeability of pattern)
Observed traffic reduction compared to clear weather
Predicted chance of 30% capacity loss in E-W direction 9 h ahead 11

12. Completing the Forecast – Take Home Message
Making forecasts most valuable to users requires . . .
close collaboration between weather forecast providers & end users / decision makers
understanding of information needs, but also communicating capabilities & limitations
translation of weather into user-relevant information (extraction of relevant information from each ensemble member)
integration of weather into user’s decision making process (impact estimation & response scenarios utilizing decision support tools)
calibration of probabilities & including some measure of confidence
training for understanding & utilizing probabilistic forecasts
development of trust in translated forecasts & decision support tools
embracing change & possibly adjusting operational procedures 12

13. Food for additional Thought
role of national weather services versus private sector
- private sector’s role may be tailoring forecasts to commercial users/sectors, while weather services focus on public
role of human in increasingly automated work environment
- human over loop rather than in loop (let automation take care of repetitive tasks)
- focus on what matters (e.g., areas of high sensitivity or impact)
human factors aspects in communicating weather & impact information
- carefully choose words, graphics & colors (e.g., avoid “met speak”)
assessment of forecast performance
- not only look at skill in forecasting weather aspects, but also assess how much value was added to user’s decision making process
- how close is performance to predictability limit
integration of weather into decision making process
- enables important feedback on how good forecasts have to be in order to be meaningful to user application 13

14. References
American Meteorological Society (AMS), 2011: A Weather and Climate Enterprise strategic Implementation Plan for generating and communicating Forecast Uncertainty Information. Commission on Weather and Climate Enterprise Board on Enterprise Communication, 99 pp.
Lazo et al., 2011: U.S. economic Sensitivity to Weather Variability. Bull. Amer. Meteor. Soc., 92, 709 – 720.
National Research Council (NRC), 2006: Completing the Forecast: Characterizing and communicating Uncertainty for better Decisions using Weather and Climate Forecasts. Committee on Estimating and Communicating Uncertainty in Weather and Climate Forecasts, 124 pp.
Sharman et al., 2008: The operational mesogamma-scale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part III: Forecasting with secondary-applications models. J. Appl. Meteor. Clim., 47, 1105 – 1122.
Steiner et al., 2010: Translation of ensemble weather forecasts into probabilistic air traffic capacity impact. Air Traffic Control Quarterly, 18, 229 – 254.
World Meteorological Organization (WMO), 2008: Guidelines on Communicating Forecast Uncertainty. WMO/TD 1422, 25 pp. 14