1. Using Ensemble Models to Develop a Long-Range Forecast and Decision Making Tool Brandon Hertell, CCM Con Edison of New York Brian A. Colle, Mike Erickson, Nathan Korfe Stony Brook University

2. Motivation Actionable weather forecasts required at longer lead times Conveying certainty/uncertainty in weather forecasts at any time length challenging Day 0 Accuracy/Confidence Lead Time Low High 2

3. Challenging Forecasts “Normal” Weather Extreme Weather High impact, low probability events are the most difficult 3

4. Timeline > 5 Days Monitoring Day 5 Monitoring Notifications Day 3 Preparation Resource Decisions Mobilization Day 0 – Storm Impact Ride out storm 4

5. Decisions Being Made Sooner Do Nothing Better be 100% correct $$ cost of being wrong is high Unhappy customers Bad publicity Regulation Do Something Pre-mobilization Scheduling Planning Resources Mutual Assistance Decision 5

6. Current Methodology Meteorologist experience and knowledge of the current weather, combined with the model forecasts and other data dictates confidence level in the weather forecast Hypothesis- An ensemble weather model may provide an engineered solution to quantifying forecast probabilities at any time scale …how would decision making change if this were the case? 6

7. Ensemble Decision Tool 2014 Phase 1 – Develop visualizations that show the probability of incoming weather – based on company weather triggers Storm track – testing phase Coming soon – – High winds, heavy precipitation, freezing line – Attempt to classify the probability of weather solutions by a “most probable”, “best case”, “worst-case” scenario 7

8. Ensemble Datasets Operational cyclone tracking website uses 4 ensembles – 21 member GEFS: Global Ensemble Forecast System – 21 member CMC: Canadian Meteorological Center Ensemble – 21 member SREF: Short Range Ensemble Forecast System – 10 member FNMOC: Fleet Numerical Meteorology and Oceanography Center (NOGAPS Ensemble) Forecasts update when data is available GEFS and SREF 4 times daily CMC and FNMOC 2 times daily 8

9. http://wavy.somas.stonybrook.edu/cyclonetracks/ 9

10. Significant Cyclone Track Archive Track historical cyclone cases using Hodges (1995) surface cyclone tracking scheme – Cyclone conditions: 24 h lifetime and 1000 km distance – TIGGE: THORPEX Interactive Grand Global Ensemble ECMWF, CMC, and NCEP ensembles utilized 00Z and 12Z MSLP data with 1˚x1˚ resolution Download and Convert Data Preprocess Data: Bandpass Filter Hodges Cyclone Tracking Calculate Cyclone Intensity Box Method Tracks Probability Shading Instantaneous Probability 10

11. Superstorm Sandy – 5 Day Forecast 11

12. Additional Parameters Are Being Tested Using GEFS Wind Speed Temperature Precipitation 12

13. Dec 2010 Blizzard – 120 hours 13

14. Dec 2010 Blizzard – 72 hours 14

15. Superstorm Sandy – 120 hours 15

16. Superstorm Sandy – 72 hours 16

17. Tropical Storm Irene – 120 hours 17

18. Tropical Storm Irene – 72 hours 18

19. February 2013 Blizzard – 120 hours 19

20. February 2013 Blizzard – 72 hours 20

21. Ensemble Decision Tool 2015 Phase 2 – Incorporate Historical Data – By using a historical data set of storms, the ensemble model can be “trained” toward pattern recognition – Probability estimates can be improved by comparing the forecast to past events 21

22. Questions? Brandon Hertell, CCM Meteorologist Con Edison Emergency Management hertellb@coned.com 212-460-3129 22