1. Science Meeting-1 Lin 12/17/09 MIT Lincoln Laboratory Prediction of Weather Impacts on Air Traffic Through Flow Constrained Areas AMS Seattle Yi-Hsin Lin 25 January 2011

2. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Outline Forecast capacity model –Motivation –Algorithm Example: 4 August 2011 case study Results and verification Summary and further work

3. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 0 – 2 Hours “Local & Dynamic” 2 – 6 Hours “National & Planned” Tactical Decision-Making - Managing pilot deviations - Safe management of airborne holding - Dynamic, locally-coordinated reroutes - Implementing local airspace restrictions - Balancing airport arrival / departure fixes Strategic Decision-Making -Airspace Flow Programs -Playbook reroutes - Ground Delay Programs Airspace Flow Programs“Playbook” Reroutes Weather PHL NY Ground Delay Programs Good Strategic PlanningManageable Tactical Environment Good Tactical Planning Contributes to Successful Strategic Plan Strategic and Tactical Planning Synergism

4. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 AFP Capacity Matrix: 4 August 2010, FCAA05 Valid Time (UTC) Issuance Time (UTC) Capacity Truth Low Medium High A05 Primary Decision Period

5. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Estimating Capacity: Process Overview CoSPA Forecast Weather Avoidance Field Blockage Algorithm Resource Capacity

6. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Weather Avoidance Fields Flight Altitude – Echo Tops (16 km) % VIL Coverage ≥ Level 3 (60 km) Echo Tops VIL Historically, what kind of weather do pilots tend to deviate around? WAF: Probability of deviation 0 5 25 50 75 95 100 As of summer 2009, WAFs have been integrated into the CoSPA shadow system. 0 10 20 30 40 50 60 70 80 90 100 -22 -18 -14 -10 -6 -2 2 6 10 14 18 22 100 90 80 70 60 50 40 30 20 10 0

7. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Severe Wx Blockage Algorithm Typical maneuverability ( 40km ) Time to coordinate deviation ( 4min* or 55km ) Least Significantly Impacted Path through Wx Preferred Route Blockage = where Weight = 1 / Normal Distance from Path Route Segment Weighted average centered on least significantly impacted path: Sum ( Weight ) Sum ( Weight * Precip>=Threshold ) Threshold = Maximum Precip along Path Center of Jet Route *based upon 825km/hr cruise speed Distance-weighted average of unavoidable WAF Takes into account maneuverability along a route Takes into account orientation of route Takes into account unavoidable weather

8. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Resource Capacity FCAs A05 and A08 chosen because they are the most frequently used AFPs. –Delays in the northeast can cause delays throughout the CONUS Route capacity = minimum capacity along each route AFP capacity = average of route capacities FCAA05 FCAA08

9. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Outline Forecast capacity model Example: 4 August 2011 case study –AFP vs. route capacities –Analysis of forecast error Results and verification Summary and further work

10. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 999999- XYZ 12/30/10 Weather and Traffic on 4 August 2010 17Z 18Z 19Z 20Z 21Z 22Z

11. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 4 August 2010: CoSPA Forecasts Reflected in Matrix Capacity Forecasts Truth A05 Primary Decision Period Valid Time (UTC) Issuance Time (UTC) Capacity Truth Low Medium High Forecasts Valid at 19Z, 4 August 2010 6-hr 5-hr 4-hr

12. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Route vs. AFP Blockage Uncertainty 5-hour forecast4-hour forecast CoSPA forecasts valid at 22Z All routes J191 AFP aggregate Forecasts are highly volatile at the route scale Errors are averaged out at the AFP scale Matrix Capacity Forecasts Valid at 22Z

13. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Outline Forecast capacity model Example: 4 August 2011 case study Results and verification –Overall statistics –Error model Summary and further work

14. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Overall Statistics Forecast Time Forecast - Truth Dataset: Summer 2010, except when CoSPA was down High capacity most of the time Forecast Error Distribution

15. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Statistics by Impact and Time of Day 50-80 80-99 100 11-15Z 15-3Z 3-11Z

16. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Observed CoSPA-based AFP Route Blockage Forecast Error Modes Analysis of both 2009 and 2010 CoSPA route blockage forecasts AFP route blockage forecast error did not always decrease monotonically with shorter look-ahead times Uncertainty modeling needs to focus both on predicting route blockage error and error behavior –Presenting model to predict route blockage error only “Free and Clear” “The Dip” “The Climb” “The Fall” Truth value Forecast value Forecast AFP Blockage Forecast Look-ahead (minutes)

17. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Factors, Predictors, and Results of AFP Blockage Error Modeling Route Blockage (0 – 1.0) Regression Tree Model Error Predictors Forecast issue time Forecast look-ahead AFP blockage at issue time Degree of convection already initiated Std. deviation of route blockages at issue time Proxy for current storm scale, organization, weather type Forecast AFP blockageScale and scope of predicted convection Std. deviation of forecast route blockages Proxy for predicted storm scale, organization, weather type Blockages affected by severity, scale, and organization of storms throughout the domain Fraction Correctmean{Interval}min{Interval}max{Interval} 0.872622.9575083.7638 AFP Blockage Error Prediction Results from 2010 CoSPA Regression tree may also be used to improve the blockage prediction

18. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Outline Forecast capacity model Example: 4 August 2011 case study Results and verification Summary and further work

19. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Summary Airspace Flow Programs are used to mitigate delays on strategic timescales CoSPA forecast → statements of resource capacity –Forecast scoring method –Tool for air traffic managers Capacities cannot be estimated at the scale of routes AFP capacities can be broadly estimated More work needs to be done on scoring and quantifying forecast uncertainty

20. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Further work: AFP Forecast Scoring Relate capacity forecasts to actual traffic counts Incorporate error analysis to improve the forecast Scaling vs. uncertainty – transition from strategic to tactical

21. MIT Lincoln Laboratory AMS Seattle 2011 Lin – 1/25/2011 Thanks Josh Sulkin, Rich DeLaura Bill Dupree Mike Robinson Joe Venuti Marilyn Wolfson Questions?