1. FAA Air Traffic Organization (ATO)
Weather and Operational Performance
Date:
2 August 2016
Presented to:
FPAW 2016 Summer Meeting
Presented by:
John Gulding

2. Management Objectives
Can FAA identify/prioritize the constraints in the system?
Air Traffic Flow Management Delay, Taxi-Out Delay
Is FAA making the most efficient use of capacity?
Capacity, Throughput, Capacity Efficiency
Is FAA providing efficient flight trajectories to operators?
En-Route Additional Distance, Level Flight
How will FAA respond to questions of Airline schedule delay, on-time performance?
On Time Performance, Change in Block Time

3. Metric Inter-Dependencies
ANSP Investment
Traffic Management Initiatives
Airspace Design
Airport Maintenance
Airport Expansion
ATC/ATM performance
Capacity & Airport Infrastructure
Schedule Delay
New Technology
Other Drivers (SAA)
Operator Schedules & Flight Preferences
Weather
Schedule Peaks
Crew Scheduling
Equip problems, etc.
Low Visibility
High wind
Convective weather

4. Data Sources Archived Trajectory and Flight Plan Data
Aviation System Performance Metrics (ASPM)
Key Event Times: Scheduled Filed Actual,
Basic METAR
Air Traffic Flow Management Delay (OPSNET)
National Traffic Management Log (NTML)
Weather Sources
METAR
NCAR Wind Data at 6-hour intervals

5. ATFM Delay by Category - FY2016
For comparison, here is FY2015 to May:
Visibility 29.4%
Wind 20.7%
T-Storms/Severe 15.1%
Volume 14.8%
Snow/Ice/Winter Ops 7.2%
Equipment 6.1%
Runway/Taxi 4.6%
Other 2.1%
Rain 0.0%
GREATEST CHANGES IN PERCENT SINCE LAST COO BRIEF:
The greatest increase has been in t-storms/severe (+5.1%) and the greatest decrease has been in wind (-2.5%).

6. ATFM Delay by Region - FY2016
Northeast
-BOS, EWR, HPN, JFK, LGA, N90, PHL, TEB, ZBW, ZNY
Chicago
-MDW, ORD, ZAU
DC Area
-DCA, BWI, IAD, ZDC
GREATEST INCREASES IN PERCENT SINCE LAST COO BRIEF
Chicago- t-storms/severe (+1.08%)
NE- t-storms/severe (+0.91%)
DFW- t-storms/severe (+0.64%)
GREATEST DECREASES IN PERCENT SINCE LAST COO BRIEF
SFO- wind (-0.93%)
Chicago- snow (-0.90%)
Chicago- wind (-0.66%)

7. ATFM Delay by Facility- FY2016
Largest Increases in Delay
EWR-Wind & Visibility
ZMA-Volume
MSP-Visibility & Wind
DCA-Visibility
Largest Decreases in Delay
ORD-Equipment
PHL-Visibility & Snow/Ice
JFK-Runway/Taxi
BOS-Wind & Visibility
Other Important Changes
SFO
-Increase in Wind delay
-Decrease in Visibility delay
LARGEST INCREASES IN DELAY
-EWR 1) Wind- February, March, and April were worse this year compared to last year. 2) Visibility- February and May of this year were worse when compared to last year.
-ZMA Volume- 12/26/2015 was a particularly bad day. Both December and January had increase delay for FY16 compared to FY15.
-MSP 1) Visibility- December was worse this FY. So far, all months for MSP in FY16 have had higher visibility delays than their corresponding FY15 months. 2) Wind- December was worse this FY.
-DCA Visibility- December and February worse in this FY.
LARGEST DECREASES IN DELAY
-ORD Equipment- fire greatly impacted the beginning of FY15
-PHL 1) Visibility- November and January main drivers of improvement. 2) Snow/Ice- January and March see improvement.
-JFK Runway/Taxi- March, April, and May were better this year
-BOS Wind & Visibility- improvement in several months, but mostly in October.
SFO DELAY CANCELS OUT
It is important to note that although SFO does not show a large change in the second graph, it had a significant shift in its charged delay. Comparing FY16 to May with FY15 to May, we see a large increase in wind delay (251,134 minutes) as well as a large decrease in visibility delay
(-216,235 minutes).
GREATEST INCREASES SINCE LAST COO BRIEF
(1) LGA increased by 320,834 minutes, (2) SFO increased by 265,528 minutes, and (3) EWR increased by 177,812 minutes.

8. Total TMI Wind Delays FY2016
The top 5 airports highlighted in red constitute 89% of total TMI wind delays.

9. TMI Wind Delays - JFK
Most wind delays occurred in October, December, March, and April

10. Linking Wind Conditions to Delay
OPSNET and METAR data are showing similar patterns. However, not exactly matching. To be further examined by looking at
different days
Trajectory characteristics, arrival fix, runway used… etc.

11. Capacity Efficiency Calculating Demand
Demand based on Filed Times or Empirically by a Best Achieved Trajectory
Flight Demand is from Benchmark Arrival Time (un-impeded time) until Actual Arrival Time

12. Arrivals into JFK April 15, 2016 HR 19:00 -1959
40nm
40nm
13L, 22L - 30 Arrivals, TAER 100
22L, 22R - 39 Arrivals, TAER 88.64

13. Flight Efficiency KPI – EnRoute
Actual vs. Flight Plan vs. Great Circle
vs Best Achieved vs. Wind Optimal
40nm
Actual
Flt Plan
100nm

14. Impact of Special Activity Airspace

15. Impact of Weather March – 481 Flights 8.3 nm Excess Dist.
June – 363 Flights
32.6 nm Excess Dist.

16. Performance Metrics Reporting Is the metric/process useful?
Does it lead to improvements in the system?
Data mining identifies specific scenarios for mitigation.
Will decision makers trust what is presented?
Weather, Airline Schedules, Airport Capacity
What are similar days?
Capabilities beyond local METAR
ASPM like tables for Terminal/EnRoute
ASPM like tables for Forecast Weather