Embed Size (px)
Citation preview
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
Series1Day 0
Ac
cu
rac
y/C
on
fid
en
ce
Lead Time
Low
High
2
Challenging Forecasts
“Normal” Weather
Extreme Weather
Extreme Weather
High impact, low probabilityevents are the most difficult
3
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
5
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
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
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
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
10
Superstorm Sandy – 5 Day Forecast
11
Additional Parameters Are Being Tested Using GEFS
• Wind Speed
• Temperature
• Precipitation
12
Dec 2010 Blizzard – 120 hours
13
Dec 2010 Blizzard – 72 hours
14
Superstorm Sandy – 120 hours
15
Superstorm Sandy – 72 hours
16
Tropical Storm Irene – 120 hours
17
Tropical Storm Irene – 72 hours
18
February 2013 Blizzard – 120 hours
19
February 2013 Blizzard – 72 hours
20
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
