Completing the Forecast—Bridging Weather Predictions to User Applications

1. Completing the Forecast—BridgingWeather Predictions to User Applications Matthias Steiner NCAR Research Applications Laboratory Email: msteiner@ucar.edu WMO WWRP Workshop on Use of NWP for NowcastingNCAR Center Green in Boulder, Colorado Wednesday, 26 October 2011

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

3. Wide Range of Decisions based on Weather Forecasts high It’s about sensitivity of a user or sectorto aspects of weather, assessment of risks,& readiness to cope with weather impacts! Air Traffic Manager: How many airplanescan airport handle? Risk of delays &diversions Consequences of Bad Decision medium Public User: Do I take umbrella today – yes/no? Risk of gettingsoaked CAUTION: It’s not just about weather,there are many other factors thatinfluence a user’s decision! low low medium high Degree of User Requirements & Sophistication 3

4. Weather User • Public Safety • Recreation • Transportation • Utilities • Construction • Agriculture • Emergency • etc. Decision to be made Forecast Products Collaboration instead of Throwing over Fence Effective forecasts have to be tailored to specific user needs 4

5. Tailoring of Forecasts – Translation & Integration Weather Information Weather Translation Response Scenarios Impact Estimation Placing intosituational context Extraction ofrelevant information Weather analyses& forecast data Mitigation strategies Weather-impacted User Weather Information Provider • Building effective bridges between providers & users of weatherinformation 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 Translation Weather Information ResponseScenarios Impact Estimation Extraction ofrelevant information Weather analyses& forecast data Placing intosituational context Mitigation strategies Strategic traffic flowmanagement initiatives& tactical programs Aviation constraintsor threshold events Storm intensity& echo tops Sector capacity &workload impact 6

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

8. Example #3: Water Resources Management Weather Translation Weather Information ResponseScenarios Impact Estimation Placing intosituational context Extraction ofrelevant information Weather analyses & forecast data Mitigation strategies Runoff & flow intoreservoir, waterlevels behind dam Rainfall (or lack thereof) Controlled release of water& timing thereof Dam overflow, waterrights, or minimalstreamflow for fish 8

9. Example #4: Wind Energy Harvesting Weather Translation Weather Information ResponseScenarios Impact Estimation Placing intosituational context Extraction ofrelevant information Weather analyses & forecast data Mitigation strategies Wind at hub height,min/max thresholds,& ramp events Energy generatedby windfarms Balancing power grid usingdifferent energy sources Wind & variability 9

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

11. User: Air Traffic Planners Example #5: Aviation Capacity Prediction Probability of losing fraction of capacity due to weather? • 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) Translation Predicted chance of30% capacity loss inE-W direction 9 h ahead Observed traffic reductioncompared to clear weather 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