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AvMet Applications, Inc.1800 Alexander Bell Dr., Ste. 130

Reston, VA 20191

Applying FAA Aviation Weather Metrics Program Research to Operational Benefits Evaluations

NBAA Orlando, FLFPAW - 2012

Mike RobinsonAvMet Applications, Inc

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Challenges to Quantifying Operational Benefits Attributed to Enhanced Weather Forecasts

• Normalizing for similar weather events

• Normalizing for similar operations

• Defining pertinent baselines; How much of impact was “unavoidable”

• Attributing improved decisions to(a) improved forecasts, (b) USE of

improved forecasts

• Providing objective, data-driven, quantified benefits estimates

“Of course delays were down this July compared to last…..there was 60% less

convection!”

“Of course delays are down,ORD demand was down 20%!”

“What do you mean you have noway to apply this new forecast?”

(Said during solid ME to Gulf line)

“Weren’t those delay improvementsAssociated with that new procedure / TMI?

you say delays were saved during all 200days of convection in ZMA Center….how

often were improved decisions derived fromthis new forecast?

“We are in a tight fiscal environment….I am not going to just take your word

that this new forecast increasesoperational efficiency!”

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FAA ANG-C6 Aviation Weather Metrics Program

Emphasis on:

• Weather impacts in context of operations (specifically, operational decision-making)• Weather event normalization and baselining• Objective, relational data analysis (and simulation support)

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Queuing Delay Modeled by Wx Index Software, PHL, Dec 14, 2006

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30

60

90

120

150

0600Z Dec 14 - 0600Z Dec 15, Hourly Wx Observations

Hour

ly D

eman

d, E

stim

ated

Cap

acity

, and

Del

ay

Dep+Arr demandDep+Arr capacityQueuing Delay, Hrs

Actual queuing delays at PHL on 12/14/06 were significant (223 flights delayed > 15 min) and there were approx. 100 cancellations (so the resulting queuing delay was less than it could have been if there were no cancellations)

Optimum capacity in good Wx

WITI is a weighted sum of three components:

– En-route Component: hourly frequency on major flows X amount of convective Wx that these flows cross

– Terminal Component

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Used by the FAA and NWS on a regular basis:• Macroscopic system performance measure in an objective manner – weekly reports • Compare different seasons’ Wx/traffic impact with outcomes (e.g. delays)

– Linear part: capacity degradation due to terminal weather impact, proportional to number of ops

– Non-linear (Queuing Delay) part reflecting excess traffic demand vs.

capacity

Weather Impact Traffic Index (WITI)Weather Weighted by Traffic

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Historical Scalable Record of WITI / WITI – Forecast Accuracy (FA)

NAS - Daily

Region - Hourly

Airport – Hourly(IAH)

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Need to give preference to departures

Accidents, outages, VIP flights, security, etc0

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10

15

20

25

30

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40

45

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5

AAR

Hour, Z

LGA, Apr 3, 2009 Arrival Rates

Actual arrivals

Arr rate based on METAR

Arr rate based on 4hr TAF

Scheduled arrivals

UnavoidableReasonable risk mgmt

Network effects

Inaccurate forecast

Overly conservative TMI

TMI execution (in excess of risk mgmt)

Deficit

Bad Weather

En-route to Terminal to Final Transition

Airlines’ business practices (e.g. related to cancellations)

Opportunities

Airspace design, procedures

Impact caused by en-route airspace

Opportunities?

GDP rate

Natural “Inertia” (time to ramp up) Operationally Beneficial Delay

(slight arrival oversupply)

Opportunities?

Assessing Components of Unavoidable Weather Impacts

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Estimating Unavoidable Arrival Delay

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200

400

600

800

1000

1200

1400

1600

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211

Daily Delay Total, Hours

Day #

Model-Estimated Overall and Unavoidable Arrival Queuing Delay Factors, Jan-Jul 2011, OEP34, Sorted from Highest to Lowest Overall Delay

Overall arrival queuing delayBad Wx at destination, Risk margins, etcNetwork EffectsOperationally-Beneficial Delay

0

10

20

30

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60

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90

Freq

uenc

y

Bin

Unavoidable Delay Percentage Distribution

Mean: 44%STD: 10%

• Modeled estimated unavoidable arrival delay 40-55% of

overall delay

• Results similar to independent estimates of unavoidable delay (e.g., REDAC report)

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Similar Weather Impact Events (WX & Demand)

Example: ORD – Varying impacts throughout the day, both convective and non-convective

ORD, 20 August 2009 - Airport Weather Impact Matrix Wx may have been the same but there was

less traffic in the morning

WITI Values

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Finding Similar Weather Impact Days Similar Weather Event/Impact Evaluation Portal (SWEEP)• Identify, rank, and inspect similar NAS wx-impact days• Identify, rank, and inspect similar REGIONAL wx-impact days

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Dynamic Airspace Routing Tool

Weather-Aware Superfast-Time NAS/ATM Simulation Model• Full ETMS flight plans• Terminal Wx (convective and non-convective), TRACON and En-route convective Wx: actual

and forecasts; permeability• Airport RWY configurations and capacity (may be Wx-degraded) but no physical RWYs• Airspace (sector, Center) capacity (may be Wx-degraded)• TMIs (Playbook, GDP, GS, AFP, MIT)

- Can blend historically enforced TMIs and simulated TMIs• Reroutes, delays, Cnx, simulated airborne holding & diversions• User-definable rules, risk factors, equipage profiles, etc• Randomized Wx, airport/airspace capacity, Wx forecast, traffic

A “superfast-time” NAS simulation tool• Day-in-the-NAS (50,000+ flights and all the above detail) in 2-3 min

Abundant output on various aspects of NAS operations • Impact (cost) metrics include delay (airborne, ground, etc.) AND cancellations and diversions• Validation of DART includes all three primary impact metrics

What is DART?

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Examining Alternative TMIs via DART Simulations

Optimized solution: Airway J29 open to relieve traffic on VUZ playbook reroute; reduced MIT, less delay

Non-optimal solution: VUZ playbook reroute traffic uses standard route; J29 closed; heavier MIT, longer delays

Only the traffic using NAS Playbook reroutes is shown; Color-coding by delay: 0-15, 15-20, 30-60, 60-120, >120 min arrival delay

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Weather-ATM Analysis and Visualization Environment (WAVE)

• “Weather-Centric” air traffic / ATM analysis tool and research platform

• Utilizes 1-min ETMS traffic data (flight information, flight plans, amendments, lat/lon/altitude positioning)

• Can ingest and display any gridded or polygon-based weather product (diagnostic or forecast)

• Engine for extensive analysis, utilizing multiple data types; output results in CSV format

• Generate standard output reports, targeted for specific performance assessments accounting for weather / forecasts

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WAVE