Using Ensemble Models to Develop a Long-Range Forecast and Decision Making Tool Brandon Hertell, CCM...

Using Ensemble Models to Develop a Long-Range Forecast and

Decision Making Tool

Brandon Hertell, CCMCon Edison of New York

Brian A. Colle, Mike Erickson, Nathan KorfeStony Brook University

Motivation

• Actionable weather forecasts required at longer lead times

• Conveying certainty/uncertainty in weather forecasts at any time length challenging

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Lead Time

Low

High

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Challenging Forecasts

“Normal” Weather

Extreme Weather

Extreme Weather

High impact, low probabilityevents are the most difficult

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Timeline

> 5 Days

MonitoringDay 5

Monitoring

Notifications

Day 3

Preparation

Resource Decisions

Mobilization

Day 0 – Storm Impact

Ride out storm

What if we could make decisions earlier?4

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

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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?

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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

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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

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

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

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Superstorm Sandy – 5 Day Forecast

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Additional Parameters Are Being Tested Using GEFS

• Wind Speed

• Temperature

• Precipitation

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Dec 2010 Blizzard – 120 hours

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Dec 2010 Blizzard – 72 hours

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Superstorm Sandy – 120 hours

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Superstorm Sandy – 72 hours

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Tropical Storm Irene – 120 hours

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Tropical Storm Irene – 72 hours

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February 2013 Blizzard – 120 hours

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February 2013 Blizzard – 72 hours

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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

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Questions?

Brandon Hertell, CCMMeteorologist

Con Edison Emergency Managementhertellb@coned.com

212-460-3129

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