Translating Ensemble Weather Forecasts into Probabilistic User-Relevant Information

Transcription

1 Translating Ensemble Weather Forecasts into Probabilistic User-Relevant Information Matthias Steiner with contributions from Robert Sharman, Thomas Hopson, Yubao Liu, Mike Chapman, and Mary Hayden NUOPC User Workshop NOAA Sand Point Conference Center in Seattle, Washington Wednesday, 1 June 2011

2 Motivation Today s Paradigm: Human in the Loop Weather User Decision Maker Forecast Products Decision Support Tool tailored to user needs ready for integration Paradigm of Future: End-to-end Integration with Human over the Loop

3 Weather Translation & Integration Concept Focus of Talk Weather Information Weather Translation Impact Estimation Response Scenarios Weather analysis & forecast data Extraction of relevant information Placing into situational context Mitigation strategies Weather information provider Weather-impacted user Some examples: Airport operation Ceiling & visibility (flight categories) Reduced capacity (arrival rates) Ground delay programs Dam operation Precipitation & runoff (water level) Overflow or breaking, minimal discharge Controlled release of water Power plant operation Winds below/above critical thresholds Reduced power generation Balancing grid with other power sources

4 Translation Aviation Example Weather hazard Old way... New way... ensemble member #1 member #2 member #3 ensemble of weather information 2 lanes 2 lanes 2 lanes ensemble mean How many air lanes? ensemble of user-relevant information Most likely 2 air lanes...

5 Example #1: Aviation Capacity Prediction User: Air Traffic Planners 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 Observed traffic reduction compared to clear weather Predicted chance of 30% capacity loss in E-W direction 9 h ahead

6 Discharge 10^3 m^3 / s Example #2: Flood Prediction User: Emergency Managers Probability of overbank flow & flooding? Bangladesh has little upstream information for Brahmaputra & Ganges rivers Focus on amount of precipitation in neighboring countries Translation of ECMWF precipitation ensemble into probabilistic discharge, flow depth, overbank flow, inundated area, etc. 10 Day Forecasts Bankfull Observed Discharge 6/25 7/29 8/26 9/23 10/12

7 Example #3: Hurricane Track Prediction User: Emergency Managers Probability of landfall, need for evacuation, damage, etc.? Hurricanes can cause huge societal impacts Focus on storm track, timing, & intensity (both precipitation & wind) Translation of hurricane track, size & intensity ensemble into probabilistic evacuation area, storm surge, damage, disruption of services, economic impact, etc. Ensemble of Hurricane Tracks

8 Example #4: Noise Propagation Prediction Probability of significant noise impact to populated areas? Sound propagation models for test range neighborhood annoyance mitigation & avoiding window shattering are sensitive to atmospheric conditions (e.g., temperature inversion & wind shear) Translation of spatial ensemble of model soundings into probabilistic sound propagation User: Test Range Operators Ensemble of Noise Propagation from a Detonation

9 Example #5: Pollution Dispersion Prediction Probability of concentration exceeding point threshold? Atmospheric diffusion models to predict concentration of smoke or chemical/biological agents require spatially & temporally varying wind, temperature, surface heat flux, & PBL depth forecasts Translation of ensemble weather forecasts into probabilistic dispersion of pollutants User: Homeland Security Managers

10 Example #6: Missile Trajectory Prediction Probability of missile to hit designated area? Missile testing is sensitive to atmospheric conditions (e.g., winds) Translation of spatial ensemble of model soundings into probabilistic missile trajectories spatial ensemble of 23-h model wind forecast User: Test Range Operators no winds actual impact rawinsonde winds rawinsonde & tower winds

11 Turbine Nacelle wind speeds (m/s) Example #7: Wind Energy Prediction User: Power Plant Operators Probability of too little or too much wind, & ramp events? Managing power grids based on different energy sources is significant challenge, especially for wind & solar energy harvesting Wind-generated power is non-linear function of wind speed Translation of LES down-scaled (ensemble) forecasts into probabilistic wind speed & ramp events timing Ramp Event 13/00 13/06 13/12 13/18 14/00 14/06 14/12 14/18

12 Example #8: Winter Road Maintenance When to run plows & apply treatments? Effective winter road maintenance requires knowledge of weather (e.g., temperature, relative humidity, wind & precipitation), road surface information (temperature) & treatment (type, amount & location) Translation of (ensemble) weather, road conditions, & treatment instructions into probabilistic road plowing & chemical treatment applications User: Road Maintenance Operators

13 Example #9: Meningitis Epidemics in Sahel User: World Health Organizations When & where to administer vaccinations? Meningitis is endemic in Sahel, occurs February June (dry, dusty season) & ends abruptly with onset of monsoon season Epidemic in resulted in 250,000 [%] cases & 25,000 fatalities Probability of epidemic (10 cases per 100,000) depends strongly on relative humidity Decision support system for WHO based on with THORPEX TIGGE 1-14 day ensemble weather forecasts Probability [%] weather-conditioned risk unconditioned risk Predicted probability of epidemic to occur 3 weeks in advance (2 week incubation time) Relative humidity prediction across Meningitis Belt 1 week in advance Probability

14 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 communicate 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 For further details about projects see: