Post on 11-May-2020
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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, FL FPAW - 2012
Mike Robinson AvMet 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 no way to apply this new forecast?”
(Said during solid ME to Gulf line)
“Weren’t those delay improvements Associated with that new procedure / TMI?
you say delays were saved during all 200 days of convection in ZMA Center….how
often were improved decisions derived from this new forecast?
“We are in a tight fiscal environment…. I am not going to just take your word
that this new forecast increases operational 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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RAINNONE
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0600Z Dec 14 - 0600Z Dec 15, Hourly Wx Observations
Hou
rly
Dem
and,
Est
imat
ed C
apac
ity,
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, etc 0
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Hour, Z
LGA, Apr 3, 2009 Arrival Rates
Actual arrivals
Arr rate based on METAR
Arr rate based on 4hr TAF
Scheduled arrivals
Reasonable 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)
Airspace design, procedures
Impact caused by en-route airspace
GDP rate
Natural “Inertia” (time to ramp up) Operationally Beneficial Delay
(slight arrival oversupply)
Assessing Components of Unavoidable Weather Impacts
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Estimating Unavoidable Arrival Delay
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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
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y
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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