Science Meeting-1Lin 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
MIT Lincoln LaboratoryAMS 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
MIT Lincoln LaboratoryAMS 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
Wea
ther
PHL
NY
Ground Delay Programs
Good Strategic Planning Manageable Tactical Environment
Good Tactical PlanningContributes to SuccessfulStrategic Plan
Strategic and Tactical PlanningSynergism
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
AFP Capacity Matrix: 4 August 2010, FCAA05
Valid Time (UTC)
Issu
an
ce T
ime (
UTC
)
Capacity Truth Low Medium High
A05 PrimaryDecision Period
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Estimating Capacity: Process Overview
CoSPA Forecast
Weather Avoidance
Field
Blockage Algorithm
Resource Capacity
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Weather Avoidance Fields
Flig
ht A
ltitu
de –
Ech
o To
ps (
16 k
m)
% 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
-1
8 -
14
-1
0
-6
-2
2
6
10
1
4 1
8
22
1009080706050403020100
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Severe Wx
Blockage Algorithm
Typ
ical
man
euve
rab
ility
( 4
0km
)
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
MIT Lincoln LaboratoryAMS 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
MIT Lincoln LaboratoryAMS 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
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011999999-XYZ 12/30/10
Weather and Traffic on 4 August 2010
17Z
18Z
19Z
20Z
21Z
22Z
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
4 August 2010: CoSPA Forecasts Reflected in Matrix Capacity Forecasts
TruthTruth
A05 PrimaryDecision Period
Valid Time (UTC)
Issu
an
ce T
ime (
UTC
)
Capacity Truth Low Medium High
Forecasts Valid at 19Z, 4 August 2010
6-hr 6-hr
5-hr 5-hr
4-hr 4-hr
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Route vs. AFP Blockage Uncertainty
5-hour forecast 4-hour forecast
CoSPA forecasts valid at 22Z
All routesJ191AFP aggregate
• Forecasts are highly volatile at the route scale
• Errors are averaged out at the AFP scale
Matrix Capacity Forecasts Valid at 22Z
MIT Lincoln LaboratoryAMS 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
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Overall Statistics
Forecast Time
For
ecas
t - T
ruth
Dataset: Summer 2010, except when CoSPA was down
High capacity most of the time
Forecast Error Distribution
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Statistics by Impact and Time of Day
50-80
80-99
100
11-15Z 15-3Z 3-11Z
MIT Lincoln LaboratoryAMS 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 valueForecast value
Fo
rec
as
t A
FP
B
loc
ka
ge
Forecast Look-ahead (minutes)
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Factors, Predictors, and Results of AFP Blockage Error Modeling
Ro
ute B
lockag
e (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 blockage Scale 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 Correct mean{Interval} min{Interval} max{Interval}
0.8726 22.9575 0 83.7638
AFP Blockage Error Prediction Results from 2010 CoSPA
Regression tree may also be used to improve the blockage prediction
MIT Lincoln LaboratoryAMS Seattle 2011
Lin – 1/25/2011
Outline
• Forecast capacity model
• Example: 4 August 2011 case study
• Results and verification
• Summary and further work
MIT Lincoln LaboratoryAMS 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
MIT Lincoln LaboratoryAMS 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