ATCA Journal - Spring 2017

Spring 2017 | VOLUME 59, NO. 1

Using Data to Better Predict Weather Impacts

Plus • Achieving a Performance Based Navigation-Centric NAS • Incorporating Avian Radar Information in the Airport Traffic Control Tower

• Using ADS-B Big Data Analysis to Create Stable Approach Models • Will Drones Change Personal Mobility?

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Spring 2017 | Vol. 59, No. 1 Published for:

Contents

ATCA members and subscribers have access to the online edition of The Journal of Air Traffic Control. Visit www.lesterfiles.com/pubs/ATCA Password: ATCAPubs (case sensitive).

Air Traffic Control Association 1101 King Street, Suite 300 Alexandria, VA 22314 Phone: 703-299-2430 Fax: 703-299-2437 info@atca.org www.atca.org Published by:

140 Broadway, 46th Floor New York, NY 10005 Toll-free phone: 866-953-2189 Toll-free fax: 877-565-8557 www.lesterpublications.com President, Jeff Lester Publisher, Jill Harris EDITORIAL Editor, Andrew Harris

Articles 12

Achieving a Performance Based Navigation-Centric NAS

18

Incorporating Avian Radar Information in the Airport Traffic Control Tower

By James Eck and Elizabeth Lynn Ray, FAA

DESIGN & LAYOUT Art Director, Myles O’Reilly Senior Graphic Designer, John Lyttle DIGITAL MEDIA Digital Media Manager, Gayl Punzalan Junior Web Designer, Mark Aquino Social Media, Jenina Bondoc

ADVERTISING Quinn Bogusky | 888-953-2198 Jason Cumming | 877-953-2197 Tim Hothi | 866-953-2190 Walter Lytwyn | 866-953-2196 Louise Peterson | 866-953-2183 Darryl Sawchuk | 866-953-2193

By Mark R. Hale, CSSI, Inc.

24 Using Emerging Data-Driven Analytics to Better Predict and Mitigate Weather Impacts

By Rafael Kicinger, Tim Myers, and Jeff Nalevanko, Metron Aviation, Inc.

28

Using ADS-B Big Data Analysis to Create Stable Approach Models

By Matthew Thompson, FAA

36 Will Drones Change Personal Mobility?

By Thomas Spencer, Antonio Trani, Nick Hinze, Virginia Polytechnic Institute and State University, and Frederick Wieland, Intelligent Automation, Inc.

Office Manager Nikki Manalo | 866-953-2189

© 2017 Air Traffic Control Association, Inc. All rights reserved. The contents of this publication may not be reproduced by any means, in whole or in part, without the prior written consent of ATCA. Disclaimer: The opinions expressed by the authors of the editorial articles contained in this publication are those of the respective authors and do not necessarily represent the opinion of ATCA. Printed in Canada. Please recycle where facilities exist.

Cover image: Davide Calabresi/Shutterstock.com Top of this page: Andrey VP/Shutterstock.com

46 A Skill Development Workshop for ATC On-the-Job Training Instructors

By Lauren J. Thomas, Evans Incorporated, and Stephanie Kreseen, FAA

54 MH370: Three Years Later

By Steve Winter, Independent Aviation Consultant

Departments 5 From the President 8 From the Editor’s Desk 59 Directory of Member Organizations

2017 ATM GUIDE 41 The Journal of Air Traffic Control

3

FROM THE PRESIDENT

By Peter F. Dumont, President & CEO, ATCA

Training the

f you are reading this Journal, you probably know the basic requirements to be an FAA air traffic controller: you must be a U.S. citizen under the age of 31; pass a medical and security screening; speak English clearly; have three years of experience or college or a combination of the two; have the ability to relocate; and pass FAA’s pre-employment tests. You might not know that even after passing all those qualifications, only 65-75 percent of the newly hired employees make it out of basic FAA Academy training. Once in the field, on-thejob (OTJ) training can take 1.5 years at a tower, two years at a terminal radar approach control facility (TRACON), and up to 3.5 years at an en route facility. The investment in air traffic controller training is significant but well worth it.

Approximately 14,000 FAA controllers manage 70,000 flights a day in the world’s busiest airspace. Of these 14,000 controllers, only around 10,800 are fully certified professional controllers (CPCs). The CPC staffing level has fallen nearly 10 percent since 2011.

I do realize that some of those lacking full certification are controllers who have moved from one facility to another and are walking through the certification process again. But regardless, FAA is facing controller training and staffing challenges. In addition to these challenges, around a third of the workforce is eligble to retire. It was great to read about the FAA and Evans Incorporated’s strides to develop a workshop for OTJ training instructors on page 46.

There are many phases of training and placement from the start of a controller’s career, through development, and onto retirement. The FAA is managing several working groups to look at the various issues surrounding controller training. In fact, the FAA turned to one of its best tools in their toolbox – the Aviation Rulemaking Advisory Committee (ARAC) – and established the Air Traffic Controller Basic Qualification Training Working Group in late 2015. The FAA established this working group because, according to FAA’s Federal Register’s notice, the FAA wants the ARAC to explore alter-

native options to utilize external training provider capabilities that will expose prospective air traffic controllers. In addition, the Agency wanted to shift the focus from basic government air traffic controller training to training on advanced NextGen tools and procedures.

ATCA was asked to participate on this working group, and I have been learning and providing advice to their proceedings.

The working group will provide its final report to the ARAC in February 2017. Since FAA is looking at capability in the marketplace for a safe training environment, I have been able to bring decades of experience as both a controller and an executive to the team and the insight that many aviation companies already exist that provide successful and thorough aviation training. For instance, Boeing, the airlines, Part 141 and 142 schools, and other companies provide pilot training that is just as safety-critical as air traffic controller training.

In addition, we have asked many of our international air navigation service provider (ANSP) members to brief the working The Journal of Air Traffic Control

Cylonphoto / Shutterstock, Inc.

I

NEXT GENERATION

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group on how air traffic controller training is accomplished in other countries. As we have seen in our discussion regarding FAA reform, NAV CANADA is a very open and welcoming neighbor regarding its best practices, including their best air traffic controller training practices. In addition, NATS UK has also provided the working group with insight into their training practices. I am pleased that the international partnerships that ATCA has developed over the past years have been part of a larger cooperative environment where ANSPs are sharing their best practices to improve the aviation industry.

Since the working group is still finalizing their overarching recommendations, I want to share one interesting issue that came up several times during our meetings: the idea that controllers should receive resiliency training. Some ANSPs felt that their new recruits could face a challenge at some point in their training, fail at it, and become nearly paralyzed at the prospect of moving forward.

The old saying, “If first you don’t succeed, 6

Spring 2017

try, try again,” didn’t seem to be part of their experience. The ANSPs said they didn’t want small failures to get in the way of learning and progressing through the training. At first, I thought this may be overstated or playing into some of the stereotypes of millennials. But this issue was repeated several times globally. I later read an article about the air traffic controller training for the military including stress relief training specifically to improve resiliency. That said, we will certainly consider whether or not resiliency training is properly embedded in FAA’s basic controller qualification training.

I understand that most improvements must be made in steps; improvements and reforms in air traffic control (ATC) are not akin to simply flipping a switch. We have certainly seen this process when it comes to improvements brought about through NextGen investments. Now that the backbone NextGen programs are in place and are nearing completion, we will be seeing more comprehensive changes in ATC.

In this issue, several of the articles’ underlying themes would not be possible without the basic NextGen investments that will lead to tangible benefits. Jim Eck and Lynn Ray’s article, “Achieving a Performance Based Navigation-Centric NAS,” is about moving from theory to reality through our investments. Turning large data streams from raw information into knowledge to improve ATC is also an underlying theme in several articles in this issue.

As always, I want to thank all of our Journal contributors and invite our members to continue submitting your fantastic articles that push improvements in air traffic management (ATM). We look forward to some of these same topics being discussed in even more depth at World ATM Congress 2017 (www.worldatmcongress.org) in Madrid in March, and the FAA Technical Symposium (www.atca.org/techsymposium) in May in Atlantic City, N.J. See you at the next ATCA event!

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FROM THE EDITOR’S DESK

By Steve Carver Editor-in-Chief, The Journal of Air Traffic Control

Spring 2017 | Vol. 59, No. 1 Air Traffic Control Association 1101 King Street, Suite 300 Alexandria, VA 22314 Phone: 703-299-2430 Fax: 703-299-2437 info@atca.org www.atca.org

Data, Data, Everywhere

D

ata is paramount in the National Airspace System (NAS). From composite flight, navigation, flow management, and avionics, data touches every aspect of aviation and air traffic control/air traffic management. There’s a treasure trove of possibilities with data that when analyzed will enhance flow control and increase safety and efficiency.

Data’s relationship to weather is intrinsic. It affects every aspect of airport operations and the NAS’s front line workforce, air traffic controllers, pilots, and airlines, to the benefit of the passenger and everyday consumer. The effects of one big storm can reverberate across the NAS. Big data – one of last year’s biggest buzz terms – is key to the future of aviation. Our ability to harness data will set us up – and set us apart – for the

Formed in 1956 as a non-profit, professional membership association, ATCA represents the interests of all professionals in the air traffic control industry. Dedicated to the advancement of professionalism and technology of air traffic control, ATCA has grown to represent several thousand individuals and organizations managing and providing ATC services and equipment around the world.

Editor-in-Chief: Steve Carver Publisher: Lester Publications, LLC

Officers and Board of Directors

Data’s relationship to weather is intrinsic. It affects every aspect of airport operations and the NAS’s front line workforce, air traffic controllers, pilots, and airlines, to the benefit of the passenger and everyday consumer.

Chairman, Charles Keegan Chair-Elect, Cynthia Castillo President & CEO, Peter F. Dumont East Area Director, Susan Chodakewitz Pacific Area, Asia, Australia Director, Peter Fiegehen South Central Area Director, William Cotton Northeast Area Director, Mike Ball Southeast Area Director, Jack McAuley North Central Area Director, Bill Ellis West Area Director and Secretary, Chip Meserole Canada, Caribbean, Central and South America, Mexico Area Director, Rudy Kellar Europe, Africa, Middle East Area Director, Jonathan Astill Director-at-Large, Rick Day Director-at-Large, Vinny Capezzuto Director-at-Large, Michael Headley Director-at-Large, Fran Hill

Staff The Journal of Air Traffic Control (ISSN 0021-8650) is published quarterly by the Air Traffic Control Association, Inc. Periodical postage paid at Alexandria, VA and additional entries.

EDITORIAL, SUBSCRIPTION & ADVERTISING OFFICES at ATCA Headquarters: 1101 King Street, Suite 300, Alexandria, Virginia 22314. Telephone: (703) 299-2430, Fax: (703) 299-2437, Email: info@atca.org, Website: www.atca.org.

POSTMASTER: Send address changes to The Journal of Air Traffic Control, 1101 King Street, Suite 300, Alexandria, Virginia 22314. © Air Traffic Control Association, Inc., 2017 Membership in the Air Traffic Control Association including subscriptions to the Journal and ATCA Bulletin: Professional, $130 a year; Professional Military Senior Enlisted (E6–E9) Officer, $130 a year; Professional Military Junior Enlisted (E1–E5), $26 a year; Retired fee $60 a year applies to those who are ATCA Members at the time of retirement; Corporate Member, $500–5,000 a year, depending on category. Journal subscription rates to non-members: U.S., its territories, and possessions—$78 a year; other countries, including Canada and Mexico—$88 a year (via air mail). Back issue single copy $10, other countries, including Canada and Mexico, $15 (via air mail).

Contributors express their personal points of view and opinions that are not necessarily those of their employers or the Air Traffic Control Association. Therefore The Journal of Air Traffic Control does not assume responsibility for statements made and opinions expressed. It does accept responsibility for giving contributors an opportunity to express such views and opinions. Articles may be edited as necessary without changing their meaning.

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Marion Brophy , Communications Specialist Abigail Glenn-Chase, Director, Communications Ken Carlisle, Director, Meetings and Expositions Theresa Clair, Associate Director, Meetings and Expositions Glenn Cudaback, Manager, Digital Media and Marketing Ashley Haskins, Office Manager Kristen Knott, Writer and Editor Christine Oster, Chief Financial Officer Paul Planzer, Manager, ATC Programs Rugger Smith, International Development Liason Sandra Strickland, Events and Exhibits Coordinator Tim Wagner, Manager, Membership

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FROM THE EDITOR’S DESK

next era of aviation. It will be instrumental in utilizing Automatic Dependent Surveillance-Broadcast (ADS-B) and Performance Based Navigation (PBN) and safely integrating unmanned aerial systems (UAS) and commercial space operations into the NAS. Data is also crucial to if not preventing more catastrophes like Malaysia Airlines Flight 370 (MH370) then at least ensuring that we will never again lose an aircraft.

This issue of the Journal delves into all that and more. In this Journal, you will read about the FAA’s efforts to form a more PBNcentric NAS with goals for the near and long term. To be successful,

data will need to flow between compatible applications in ground NAS and avionics systems. The article on ADS-B big data analyzes the possibilities of creating a more stable model of approaches.

We initially didn’t mean to focus this Journal issue on data; it just happened. Much like our industry, we realized that it was the common thread in this issue. It’s a topic we’ll continue to explore in the Journal. If you have a paper on the subject of data (or any other topic), please send it to Kristen Knott, managing editor, at Kristen.knott@atca.org. I hope you enjoy the issue.

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Achieving a Performance Based Navigation-Centric NAS

By James Eck, Assistant Administrator for NextGen, FAA and Elizabeth Lynn Ray, Vice President, Mission Support Services, FAA

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Spring 2017

PBN STRATEGY

O

ne of the FAA’s most noteworthy NextGen accomplishments is the creation of a nationwide network of Performance Based Navigation (PBN) procedures for departures, approaches, and arrivals, and new high- and low-altitude routes. As the FAA continues to publish more procedures, the agency and the aviation community are closing in on a major air navigation objective: a PBN-centric National Airspace System (NAS).

With the FAA introducing time-based management to make the best use of PBN as the primary means of air navigation, the NAS will deliver more predictable aircraft operations. More predictable flight schedules mean more on-time arrivals for the flying public and greater efficiency for aircraft operators.

Ten years ago, the FAA and aviation stakeholders developed and published a “Roadmap for Performance Based Navigation,” setting the stage for the ongoing transformation of the NAS to run on PBN. Since then, the agency has worked with stakeholders to design and publish thousands of area navigation (RNAV ) and RNAV-required navigation performance (RNP) procedures to provide a reliable structure for getting aircraft in and out of terminal airspace to and from runways and across the United States. Our PBN accomplishments build off of NextGen research that informed the work led by the FAA’s Air Traffic Organization. As of November

2016, the FAA has published more than 6,400 RNAV (GPS) and 380 RNP approach procedures, plus more than 1,070 RNAV departures and 800 RNAV arrivals.

The NextGen Advisory Committee and the Performance Based Operations Aviation Rulemaking Committee have helped the FAA set its PBN priorities, as well as refine the detailed operation of PBN. That collaboration has prompted the FAA to provide PBN first where it can have the most benefit. For example, the agency has focused on designing and publishing PBN departure, approach, and arrival procedures at 11 metroplexes – metropolitan areas with multiple airports and complex air traffic. So far, more than 400 PBN procedures have been completed at metroplexes and are already reducing fuel use and aircraft exhaust emissions. Beyond metroplexes, the FAA has already published RNP approaches at all Core 30 airports.

The NextGen team is building on this progress to further refine the agency’s priorities and related milestones for a smooth transition to a PBN-centric NAS. This vision is outlined in the PBN NAS Navigation Strategy 2016 (available at www.faa.gov/nextgen/media/ PBN_NAS_NAV.pdf ), a new report we plan to update every two years to ensure the FAA continues to meet the navigation needs of a changing world. We urge you to read it. The FAA seeks feedback from all interested parties, including ATCA members.

bluebay/Shutterstock.com

Since World War II, navigation in the NAS has relied on ground-based navigation aids (navaids). Only at the end of the last century did PBN begin to unlock new possibilities.

The Journal of Air Traffic Control

13

PBN STRATEGY

Digital navigation charts, Data Comm capability, and PBN tools will make it possible for pilots and controllers to work more efficiently to accommodate shifting demand.

A PBN Culture Since World War II, navigation in the NAS has relied on groundbased navigation aids (navaids). Only at the end of the last century did PBN begin to unlock new possibilities. By relying on satellite-enabled positioning of aircraft, the FAA’s procedure designers could draw a 3D path just about anywhere in the sky.

However, it’s a challenge to get an entire community to embrace the PBN vision and to rely primarily on a new method of air navigation when commercial aircraft operators need a business justification to equip aircraft and train pilots. Affordability is always a key concern, especially for general aviation where many aircraft are used for recreation.

A successful transition to a PBN-centric NAS requires the agency to closely collaborate with all types of aircraft users and manufacturers, as well as airport operators, and with residential communities located around airports. The FAA has been actively engaged in this process for decades and aims to reach new levels of success through 2030 and beyond. Achieving the benefits of a PBN-centric NAS requires a shared commitment to foster improvements by equipping, training, and using these new navigation techniques.

The FAA is encouraging air carriers and other operators to upgrade equipment and train flight crews to use all types of PBN procedures wherever and whenever possible. Upgrading cockpit equipment will allow operators to derive PBN benefits more often.

The FAA aims to provide the right type of PBN procedures for the particular needs of airports divided into navigation service groups (NSG). For example, NSG 1 includes the 15 busiest US hubs that will require aircraft to have some minimum navigation performance capability. These airports conduct about 30 percent of all instrument flight rule operations and 45 percent of enplanements in the country.

RNAV (GPS) approach procedures with various minimums, as low as a 200-foot decision height, will be provided at these 15 facilities, as will RNAV RNP approaches. RNP is being used for approaches and for a variety of routes and may be applied to standard terminal arrivals (STAR) and standard instrument departures (SID) in the future.

Legacy ground-based navaids will be kept in service for the resiliency of the overall air navigation system. The FAA will even add some distance measuring equipment (DME) sites where needed in en route or terminal airspace to improve the triangulation needed to 14

Spring 2017

continue operations if GPS is not available. What’s no longer needed in this “new” PBN NAS will be removed.

A successful transition to a PBN-centric NAS also requires the agency to enhance its community involvement. As the FAA continues to implement NextGen, there has been an increasing amount of community and congressional interest about potential environmental impacts associated with implementing PBN procedures.

Communities’ views are important to the FAA as we take the next steps to improve the NAS. As the FAA makes aviation decisions that affect citizens, the agency is committed to informing and involving the public, and giving meaningful consideration to community concerns and views.

As part of the FAA’s response to improving engagement with congressional and community interests, the FAA is working to develop and implement practices that facilitate community involvement and partnerships, both earlier in the process for proposed actions and on an ongoing basis.

PBN Marches On The PBN NAS Navigation Strategy 2016 includes near-, mid-, and long-term objectives through 2030 and beyond: • Near-term objectives focus on using more RNAV and RNP procedures, as well as developing new criteria, policies, and standards to allow for more advanced application of PBN. • Mid-term objectives will build on newly available PBN operations to increase access, efficiency, and resiliency across the NAS. The focus will be to expedite the delivery, use, and subsequent maintenance of PBN services.

• Long-term objectives will focus on leveraging time- and speed-based air traffic management to further increase predictability. Time-based management will facilitate the best use of PBN so that airlines and other aircraft operators can conduct more predictable operations.

Near-Term (2016–2020) At larger airports, the near-term focus includes optimizing and increasing the use of existing PBN procedures, continuing implementation efforts at metroplexes, and ensuring the navigation infrastructure is resilient. At smaller airports, the focus will be on enhancing safety with PBN approaches with vertical guidance. As part of Continued on Page 16

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PBN STRATEGY

Photos courtesy of the FAA

Elizabeth Lynn Ray, Vice President, Mission Support Services, FAA

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James Eck, Assistant Administrator for NextGen, FAA

NextGen, the FAA has already published more than 3,700 localizer performance with vertical guidance (LPV) procedures. The procedures use the wide area augmentation system (WAAS), which was developed before the FAA began NextGen. But these procedures are an integral part of a PBN NAS. Using some LPV approaches, a pilot can get as low as 200 feet above the ground before deciding whether to land or go around – the same as a Category 1 instrument landing system (ILS). But LPV requires no ILS equipment at an airport. As of February 2017, 3,767 WAAS LPV approach procedures now serve 1,832 airports, 1,074 of which are non-ILS airports. These procedures help pilots fly more safely in instrument and visual meteorological conditions to thousands of runways.

Currently, 622 localizer performance (LP) approach procedures serve 466 US airports. LP uses WAAS for lateral guidance and barometric altimeter for vertical guidance.

NextGen’s near-term plans also include improving low-visibility access to airports with the enhanced flight vision system (EFVS).

The FAA plans to issue updated regulations and guidance materials to enable aircraft operators who have the required technology and pilot training to conduct EFVS operations through the entire visual segment.

The FAA is also working with stakeholders to reduce the number of circling approaches. Unlike approaches that guide an aircraft straight in to a runway, these procedures involve making an approach and flying in a circular pattern at low altitude to land, most often without electronic navigation aids guiding a descent from minimums.

Circling approach procedures will be maintained only as needed to meet pilot training requirements, to provide airport access, and for NAS resiliency.

As we move toward a PBN-centric NAS, the FAA plans to deactivate about 70 VHF omnidirectional radio ranges (VOR) in coming years and assess whether smaller airports still need ILSs. The agency has no plans to publish more non-directional beacon (NDB) Spring 2017

approaches, but the Department of Defense will continue to use some NDBs for training.

Mid-Term (2021–2025) The FAA is updating its software used in procedure development to handle new PBN criteria, data, and designs. This is expected to reduce how long it takes to introduce new PBN procedures. An automated tool is also being developed to help with the periodic review of procedures to reduce maintenance workload.

The FAA is encouraging operators to equip aircraft to fly radiusto-fix (RF) turns – where an aircraft flies a circular path with a constant radius around one point before the path terminates at a fix. It is important that aircraft arriving at busy airports can fly RF turns so they can take advantage of RNP procedures. The RF turn is the primary curved path segment for these procedures, which include RNP approaches now and may include RNP STARs and SIDs in the future.

At Denver, many carriers’ properly equipped aircraft are cleared on an RNP approach on downwind to make an RF turn to final approach close to the airport. Conventional procedures at Denver, including radar vectors to an ILS, usually involve flying much farther from the airport before turning inbound for final approach. The RF turn capability saves time and fuel while reducing emissions.

The FAA will also continue replacing jet routes and victor airways with Q-routes and T-routes, respectively, as PBN continues to guide more traffic flow in en route airspace. The agency will work to provide more DME/DME coverage where needed as backup navaids in case of a GPS outage. Meanwhile, the FAA will discontinue most NDB approaches in the contiguous United States.

Long-Term (2026–2030) By 2030, PBN procedures will be the standard method of navigation through the NAS during normal operating conditions. The FAA will continue to reduce the number of VORs and ILSs where they are no longer needed.

PBN STRATEGY

navigation charts, Data Comm capability, and PBN tools will make it possible for pilots and controllers to work more efficiently to accommodate shifting demand.

“The Future of the NAS” report also addresses how NextGen modernization will facilitate the operation of new entrants in the NAS, such as unmanned aircraft systems (UAS) and commercial space vehicles. New automation capabilities will assist with space vehicle launch and re-entry operations including the display of active special activity airspace volumes for controllers. The FAA is also researching the development of separation standards between aircraft and space vehicles, focusing on captive-carry operations and unpowered glide flights. UAS operations are leading to new rules, policies, and procedures to support anticipated active operations at all altitudes.

Rewards and Revisions PBN is already proving to be a boon to aircraft operators, who will save time and fuel. As it becomes the primary form of air navigation procedures, PBN will provide more benefits to more users. Everyone can benefit from the new world of PBN operations, but only if they start to learn about the possibilities, equip their aircraft accordingly, and fly these procedures.

We look forward to working with the aviation community, including members of ATCA, to perfect these methods. We ask for everyone’s help revising the new “PBN NAS Navigation Strategy” plan – which we plan to do every two years – as we move together toward a PBN-centric NAS. The Journal of Air Traffic Control

ra2studio/Shutterstock.com

By then, the FAA expects to make major strides in implementing trajectory-based operations (TBO). Enhanced time-based metering capabilities will complement the PBN infrastructure development to support higher throughput and more efficient terminal and en route traffic flows.

The FAA is transitioning the NAS from a largely tactical surveillance and separation structure – based on where an aircraft is – to a more strategic NextGen time-based management system.

The new system will be based on knowledge of when an aircraft will be at designated points along its projected flight path. The move to time-based management is described more fully in “The Future of the NAS,” available at www.faa.gov/nextgen/media/futureOfTheNAS.pdf. The 2016 report includes a summary of the FAA’s plans to implement time-based management and TBO. Controllers are already using automation tools designed to help them with time- and speed-based management of flights. Time-based flow management (TBFM) is one tool that prompts en route controllers to speed up or slow down a flight while it is still hundreds of miles from terminal airspace so that it can arrive at the ideal time to begin a PBN procedure. Flying into Atlanta, this means commencing a STAR with an optimized profile descent (OPD) without delay by avoiding conflict with other arriving aircraft. An OPD allows a pilot to fly a continuous descent with throttles near idle, reducing fuel burn and emissions.

Controllers will have the ability to provide aircraft operators with PBN-based point-to-point navigation capability with strategically located RNAV waypoints to traverse high-altitude airspace. Digital

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Lipsett Photography Group/Shutterstock.com

AVIAN RADAR INFORMATION

Incorporating Avian Radar Information in the Airport Traffic Control Tower

By Mark R. Hale, CSSI, Inc.

H

Figure 1. Passengers rescued following water landing on Hudson River.

ave you seen Sully? The motion picture features Tom Hanks as Captain Chesley Sullenberger, who, on an otherwise routine day in January 2009, heroically piloted his Airbus A320-214 to rest on the Hudson River.

That afternoon, all 155 passengers and crew were safely evacuated before the airframe slowly sank in the chilly 36-degree river to settle in the mud at the bottom (Figure 1). We have always been aware of the danger that bird strikes pose to aviation, but seeing the “Miracle on the Hudson” play out in front of our eyes focused the national spotlight on how serious a threat these incidents can be. While critical incidents like this focus our nation’s attention, the FAA, airports, and industry have been systematically researching and mitigating avian threats for decades. For air traffic controllers, dealing with the management and dissemination of bird threat information is challenging – occupying time, communication, and attention. On top of that, current ATC operational practices and procedures rely primarily on pilot reports, which yield limited useful information. The Wildlife Surveillance Concept (WiSC) aims to decrease the reliance on pilot reporting and streamline ATC operational procedures so controllers are able to provide timely, precise bird threat information to pilots where and when it can be acted upon.

Photo: cnn.com/2009/OPINION/11/18/langewiesche.miracle.hudson.flight

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AVIAN RADAR INFORMATION

Figure 2. Reported bird strikes from 1990-2014.

Background Bird strikes lead to serious damage and can have deadly consequences.

The threat that birds pose to aviation can be traced back to the earliest days of flying. Did you know that bird strikes have resulted in more than 258 fatalities and the destruction of 245 aircraft since 1988,[1] and that costs exceed $1.2 billion per year for commercial aircraft carriers?[2] The FAA’s National Wildlife Strike Database confirms the risks, with total reported bird strikes increasing steadily over the last 15 years (Figure 2).

The FAA, US Department of Agriculture (USDA), and other government and industry partners are actively researching ways to mitigate wildlife strikes and have already leveraged strategic, tactical, and regulatory measures. In 2005, the FAA’s Airport Technology Research and Development (R&D) branch initiated the Bird Radar Performance Assessment Research Program. This program began investigating the performance and feasibility of commercially available avian radars in an experimental capacity. These systems are intended for use by researchers, airport wildlife biologists, and other airport operations personnel.

The research yielded two important outcomes. It informed FAA Advisory Circular 150/5220-25, “Airport Avian Radar Systems,” which provides guidance on the selection and use of avian radar systems to supplement an airport’s wildlife hazard management plan (WHMP). It also confirmed that existing commercial avian radars are suitable for detecting and tracking birds on and around the airport while providing nearly real-time alerts of bird activity in operational areas where they represent a high collision risk.[3] The WiSC effort extends this information to the airport traffic control tower (ATCT) where it can be efficiently disseminated in near real-time to pilots.

The Wildlife Surveillance Concept (WiSC) The FAA’s Airport Technology Research and Development Branch initiated a task in September 2014 to develop a concept for introducing avian radar-derived information to the ATC environment. This research, led by the FAA’s NextGen Advanced Concept branch, led to WiSC’s development. WiSC enables the distribution of more precise bird threat information to air traffic controllers and eases the burden of managing and disseminating bird threat information. Figure 3 shows the flow of bird threat information in the current operational environment and how the information flow will be enhanced to provide this information to the ATCT.

Figure 3. Bird threat information flow.

WiSC Research WiSC is currently in phase three of a four-phased research approach.

The research team has identified how the detection and dissemination of bird threat information is managed in the ATCT, how it might be improved in the future with radar-derived bird threat information, and some potential benefits of providing this new information to the

It is not possible for controllers, and very difficult for pilots, to visually detect birds during nighttime operations or in low visibility operations.

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AVIAN RADAR INFORMATION ATCT. The team interviewed more than 20 certified professional controllers and 25 front line managers at ATCT facilities with a high incidence of bird strikes. Research activities included laboratory exercises, visits to avian radar research installations at Dallas Fort Worth International Airport (DFW) and Seattle Tacoma International Airport (SEA), and a human-in-the-loop experimental simulation in a virtual tower cab environment at the William J. Hughes Technical Center in Atlantic City, N.J. (Figure 4).

ond-hand reported data in the form of bird advisories. As a result, air traffic controllers are typically forced to reactively – rather than proactively – share bird threat information.

Current procedures (FAA Order JO 7110.65W) require air traffic controllers to disseminate bird threat advisories to affected aircraft for 15 minutes or until they can confirm that there is no longer a threat. Unfortunately, bird position information immediately begins to decay, and if it is not updated in a timely fashion, will be of little use.

In actuality, outdated bird threat information is likely more of a hindrance to operations than a help. To combat this, air traffic controllers query subsequent aircraft in an attempt to update bird threat position information, thereby increasing bird threat information communications. If an air traffic controller is unable to obtain updated bird position information, they must continue to issue the “last known location of birds” for 15 minutes. As Figure 6 illustrates, the location of a bird threat can change substantially in 15 minutes, resulting in bird advisories that are neither accurate nor usable for pilots.

Figure 4. Tower Cab simulator in Atlantic City, N.J.

Current Operational Environment In today’s ATC environment, birds are usually not detected until seen by pilots. Relying on humans to detect birds is problematic for several reasons. First, exposure to birds is fleeting because pilots are moving at a high rate of speed and birds are small targets. In addition, most strikes occur during approach and departure, which are critical phases of flight when pilots are especially focused on navigating their aircraft (Figure 5). Therefore, competing duties related to the phase of flight may impair a pilot’s perception of the location, direction, size, and number of birds encountered. Finally, it is not possible for controllers, and very difficult for pilots, to visually detect birds during nighttime operations or in low visibility operations. This is unfortunate because birds are active at night, as confirmed by the National Wildlife Strike Database, with nocturnal strikes accounting for 30 percent of all strike Figure 6. Distance traveled by two bird species in 15 minutes. reports.[4] Pilots also obtain information on birds (and other airport conditions) on the automatic terminal information service (ATIS) recorded update. Air traffic controllers and supervisors update the ATIS at prescribed intervals or on an as-needed basis. These broadcasts include important weather and operational conditions and may include bird activity near an airport. When the airport is experiencing high levels of bird activity, as happens in seasonal bird migration, the recorded ATIS message will typically report “birds in the vicinity” continuously for days or weeks at a time. Generic ATIS information like this does not provide pilots with actionable information to avoid bird strikes.

Benefits of WiSC WiSC has several key benefits for aviation stakeholders. Most benefits Figure 5. Percentage of bird strikes by phase of flight. are broadly related to three factors: Once a pilot detects birds they report their observations to air • Improved bird detection. traffic controllers along with any discernable information on species, • More precise bird threat information. altitude, and heading. Air traffic controllers then disseminate this sec- • Improved information management and procedures.

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AVIAN RADAR INFORMATION

provide more precise position, altitude, and threat size information.

This information is continuously updated through the WiSC display, allowing the controller to communicate, in near real time, the proximity of birds to aircraft. Providing more precise avian radar-derived information to air traffic controllers not only increases the quality of information communicated but also relieves the controller of the mandatory 15-minute reporting period. This allows air traffic controllers to disseminate bird advisories to only affected aircraft rather than issuing blanket broadcast advisories. WiSC will reduce bird-related communications, More precise bird threat information The quality of bird threat information from avian radar is far superi- thereby reducing frequency congestion, and will also reduce controller or to relying on human perceptual abilities. Avian radar systems can and pilot workload related to bird threat information management.

Improved bird detection The integration of radar-derived bird threat information provides air traffic controllers with a more precise detection method so that they can convey more meaningful bird advisories to pilots. Avian radar also allows bird detection during nighttime and low-visibility operations.

Last, with avian radar, controllers will receive bird threat information sooner, allowing them to be more proactive with bird advisories.

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muratart/Shutterstock.com

Bird position information immediately begins to decay, and if it is not updated in a timely fashion, will be of little use.

AVIAN RADAR INFORMATION

If an air traffic controller is unable to obtain updated bird position information they must continue to issue the “last known location of birds” for 15 minutes.

Improved information management and procedures WiSC will include new procedures for handling radar-derived bird threat information. WiSC will display only those threats that present a potential risk to operations, based on factors like current location, altitude, number of birds (flock), and direction of flight. With WiSC, birds that previously would have caused the air traffic controller to issue an advisory may now be disregarded based on more sophisticated, machine-based processing of threat level and probability of bird-aircraft collision. This will eliminate some of the communications and coordination related to birds, and free up the controller’s cognitive resources. For example, if the processing algorithm in WiSC determines that a specific bird threat does not meet some minimum threshold (e.g., size of threat) or other characteristic (e.g., direction of flight, altitude, etc.), the system does not display it to the air traffic controller. If a pilot reports bird activity that is not currently displayed through WiSC, the air traffic controller simply acknowledges the pilot but does not have to track the threat.

Conclusion and Next Steps WiSC research has confirmed that air traffic controllers spend significant time and effort managing and disseminating bird threat information. This is particularly true when considering air traffic controllers at facilities with known bird strike problems. Moreover, we learned that the information conveyed through current practices and procedures (e.g., bird advisories, ATIS updates, etc.) is not timely and often does not provide actionable information for pilots. The air traffic controllers surveyed confirmed that, as envisioned, WiSC will support better bird safety advisories for pilots, reduce the workload associated with managing bird threat information, and promote higher quality reports.

They were overwhelmingly in favor of integrating more precise and current avian radar-derived bird threat information into the ATCT. With support from the user community, the WiSC research team has formulated preliminary bird threat information requirements, identified preliminary display system requirements, and developed draft procedural guidance in support of integrating and managing avian radar-derived information in the ATCT. In 2017 the WiSC research team plans to conduct site visits to further validate these findings and to conduct a formal benefit analysis.

Mark R. Hale is a human factors specialist at CSSI, Inc., supporting the FAA’s Advanced Concepts Group, and an adjunct professor of psychology at Rowan University. Hale holds an M.S. in experimental psychology – human factors from the University of Idaho, and a B.A. in psychology from Rowan University. He has over 15 years of experience supporting the FAA with an emphasis in cognitive engineering research methods, human-in-the-loop simulation, and concept development and validation research.

References

[1.] Federal Aviation Administration (2015). Wildlife strikes to civil aircraft in the United States, 1990-2014. US Department of Transportation, Federal Aviation Administration, Office of Airport Safety and Standards, Serial Report No. 21, Washington, DC., USA [2.] Allan, J. R. (2002). The costs of bird strikes and bird strike prevention. Human conflicts with wildlife: economic considerations. National Wildlife Research Center, Fort Collins, Colorado, USA. [3.] King, R. (2013). Research on Bird-Detecting Radar, (FAA Report DOT/ FAA/TC-13/3). US Department of Transportation, Federal Aviation Administration.

[4.] Federal Aviation Administration (2016). FAA National Wildlife Strike Database. http://wildlife.faa.gov/

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Using Emerging Data-Driven Analytics to Better Predict and Mitigate Weather Impacts

By Rafal Kicinger, Tim Myers, Jeff Nalevanko, Metron Aviation, Inc.

W

eather causes 60 percent of all air travel delays in the United States. According to the FAA’s Operations Network (OPSNET) statistics, weather is the leading cause of flight delays (see Figure 1). Likewise, weather interrupts aviation operations throughout the world. Recent research shows that many delays, diversions, and cancellations can be avoided through improved integration of weather information into ATM decision support tools, such as data-driven analytics.[1] A root cause of persistent operational inefficiencies is the lack of shared situational awareness in anticipated weather impacts to aviation. Several factors contribute to this shortfall, including the fact that pilots, dispatchers, air traffic controllers, and traffic managers each have access to different weather observation and forecast products.

As a result, decisions are based on inconsistent sources of information. An additional impediment to shared situational awareness is that in order to estimate weather’s effects on stakeholders’ specific operational context, each stakeholder subjectively interprets meteorological information based on their experience. There is no single, agreed upon process for estimating how and when weather is likely to block routes, the degree to which airport arrival and departure rates will be reduced, or the time an airport will take to return to full capacity following a weather event.

Emerging weather translation technologies driven by big data analytics and machine learning offer new opportunities to provide objective, consistent, and more accurate depictions of weather impacts on flight operations. These technologies strive to provide a common understanding of weather’s constraints related to aviation, rather than focusing on the details of the weather itself. Examples of outputs provided by such technologies are illustrated in Figure 2, and include en route avoidance regions that pilots are likely to avoid, route blockages, and airspace and airport constraints caused by predicted demand-capacity imbalances. These types of guidance products do not require subjective interpretation and can be easily integrated into decision support tools to facilitate proactive and efficient mitigation of weather impacts.

Figure 1. Annual flight delays by cause. Source: OPSNET

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COVER STORY

Weather Translation Efforts In the early 2000s, NASA and the FAA sponsored the initial research on weather translation techniques. These efforts were coupled with the Joint Planning and Development Office’s ( JPDO) work on defining the FAA NextGen Weather Concept of Operations[2] and ATMWeather Integration Plan.[3-5] Several research organizations, including MIT Lincoln Laboratory, National Center for Atmospheric Research (NCAR), Metron Aviation, and others made significant contributions to

the development of early weather translation techniques.[6-10] This period of rapid development yielded more than 40 weather translation techniques to address various types of aviation weather hazards, including convection, turbulence, ceiling and visibility (C&V), and winter weather.[4] Much of this research focused on convective weather, which is the most disruptive type of weather hazard to US flight operations. Few of these initial techniques achieved the necessary maturity level to transition to operational decision support prior to a cut in research funding. In The Journal of Air Traffic Control

DAE Photo/Shutterstock.com

As we see more air traffic traversing our skies, the need to minimize weather impact becomes increasingly important.

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COVER STORY

Figure 2. Examples of guidance products provided by weather translation technologies.

fact, the Convective Weather Avoidance Model (CWAM), developed

by MIT Lincoln Laboratory, is currently the only weather translation technique considered for deployment as part of the NextGen Weather Processor (NWP) program.[7, 11] With funding significantly reduced, progress towards maturing weather translation techniques has been largely hindered and limited to specific application needs. This approach lacks a cohesive strategy for using and transitioning the most promising weather translation techniques for operational deployment. In the meantime, the overall data, computing, and IT landscapes are constantly evolving. Emerging approaches for big data analysis and machine learning tools can be leveraged to define a new class of weather translation techniques that exploit data-driven predictive analytics and real-time model adaptation to changing operational conditions. Several examples of ongoing research projects using big data analysis to improve predictive weather translation capabilities are introduced in the following sections.

Flow Constrained Area Airspace Capacity Estimator Flow Constrained Area (FCA) Airspace Capacity Estimator (FACE) is a weather translation and impact assessment technique that provides traffic managers with guidance on determining FCA location, timing, and rate profile. FCAs are commonly used in today’s air traffic flow management (ATFM) to control aircraft flows through constrained airspace resources. The locations of today’s FCAs are largely static, often located along the boundary joining adjacent Air Route Traffic Control Centers (ARTCCs), and are not well aligned with dynamically changing convective weather hazards. The rate-setting guidance is typically limited to lookup tables that are derived based on historical throughput analysis. This is due to the difficulty in dynamically estimating weather impacts on airspace capacity with changing weather forecasts.

FACE combines convective weather translation modeling, data mining, and network flow modeling to provide timing, location, and rate suggestions for FCAs that account for current weather forecasts and predicted traffic flow patterns (see Figure 3). Rate validation is provided by enabling users to dynamically analyze the distribution of historic throughput for any FCA geometry. Initially developed in 2010, Metron Aviation is currently enhancing FACE for an ongoing

Figure 3. Location, timing, and rate setting guidance provided by FACE.

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NASA-sponsored effort that develops decision support capabilities for the Collaborative Trajectory Options Program (CTOP). MIT Lincoln Lab is conducting a related effort on modeling airspace permeability and flow rate prediction that also has potential to improve flow rate guidance for CTOP programs.[12] Turbulence Avoidance Modeling Metron Aviation’s recent effort, using data mining techniques and predictive analytics, focused on Turbulence Avoidance Modeling (TAM) products that provide aviation stakeholders with a predictive assessment of operational impacts and depictions of turbulence hazards. This FAAsponsored project extends prior analysis of turbulence avoidance using a limited number of scenario data sets to a broader, big data approach.

[13,14] In this effort, months of turbulence forecast and observation data (e.g., Graphical Turbulence Guidance [GTG] forecast and nowcasts, aircraft-derived in-situ Eddy Dissipation Rate [EDR] observations, and pilot reports [PIREPs]) are mined and fused to determine turbulence intensities across the NAS at varying altitude ranges. Figure 4 shows the results for the first three months of 2015.

TAM uses data-driven analytics to translate meteorological forecast information into depictions of predicted turbulence avoidance regions. The model can be configured to account for specific operational policies and tolerance for turbulence by different airlines, and even to specific aircraft types (weight classes) to account for varying sensitivities to the same turbulence intensity.

Network Resilience Tool Automated weather translation techniques strive to provide accurate, objective, and consistent interpretation of weather impacts (e.g., avoidance regions, capacity, and flow reductions) that can be shared among all stakeholders. The application of automation overcomes the need for manual interpretation; therefore, it can be more quickly updated as new weather information becomes available. Graphical representations of weather translation guidance can be easily shared among stakeholders to achieve common situational awareness and the associated risks to enable proactive weather mitigation and planning.

The Network Resilience Tool (NRT), developed by Metron Aviation for EUROCONTROL Network Manager in Brussels, is an example of an operational weather translation service providing all European stakeholders with up to a two-day, look-ahead prediction of anticipated weather impact risks due to various types of weather hazards (i.e., C&V, surface winds, convection, and winter weather) and other natural hazards (e.g., space weather, flooding, fires, and volcanic ash). The NRT ingests ensemble weather forecasts for the entire European domain that are provided by the Danish Meteorological Institute (DMI). The NRT then uses a suite of weather translation techniques to convert meteorological information into predicted operational capacity reduction impacts and combines this information with weather uncertainty to predict risk to airports and airspace sectors. To

COVER STORY

Figure 4. Frequencies of pilots encountering turbulence intensity values exceeding GTG>0.1 as a fraction of total cruise time in the NAS during January – March 2015.

ing transformation to data-based services and cloud infrastructure, other industrial vendors (e.g., flight planning solutions) are likely to integrate such predictive capabilities to improve the accuracy and reliability of their products.

References

Figure 5. Network Weather Resilience tool provides operational weather translation services to EUROCONTROL with two-day weather impact predictions.

promote proactive collaboration and development of mitigation strategies, the tool presents a web-based graphical user interface (see Figure 5) that provides network-wide and customizable local alerts regarding the anticipated weather impacts.

The Future of Weather Impact Capabilities Discussions with traffic managers, controllers, and airlines show the need for automated, consistent, and objective translation of weather data into anticipated operational impacts to enable proactive mitigation and planning. In addition, ongoing FAA efforts, such as the Plan, Execute, Review, Train, and Improve (PERTI) initiative, demonstrate why providing this type of information to establish common situational awareness among all stakeholders and advanced planning is critical to future flight operations.

As we see more air traffic traversing our skies, the need to minimize weather impact becomes increasingly important. With government research funding expected to remain scarce in the foreseeable future, commercial applications are likely to be the main driving force in maturing these technologies and transitioning the most promising ones to operational readiness levels.

At the same time, expanding the user base may provide additional incentive to enhance, mature, and adapt weather translation capabilities.

As illustrated with the NRT example, strong interest from international air navigation service providers may lead to adaptations of many of these techniques in other regions of the world. Also, with the industry’s ongo-

[1.] Klein, A., Kavoussi, S., and Lee, S.R., 2009, Weather forecast accuracy: Study of impact on airport capacity and estimation of avoidable costs. 8th USA/Europe Air Traffic Management Research and Development Seminar (ATM2009), June 29-July 2, 2009. Napa, CA. [2.] Joint Planning & Development Office, 2006, Weather concept of operations for the Next Generation Air Transportation System, US Department of Transportation: Washington, DC. [3.] Joint Planning and Development Office, 2009, ATM-weather integration plan. Version 1.0. September 17, 2009, US Department of Transportation: Washington, DC. [4.] Joint Planning and Development Office, 2010, ATM-weather integration plan: Where we are and where we are going. Version 2.0. September 24, 2010, US Department of Transportation: Washington, DC. [5.] Bradford, S., Pace, D.J., Fronzak, M., Huberdeau, M., McKnight, C., and Wilhelm, G., 2011, ATM-weather integration and translation model. 91st American Meteorological Society Annual Meeting, January 23-27, 2011.

Seattle, WA. [6.] DeLaura, R. and Evans, J.E., 2006, An exploratory study of modeling enroute pilot convective storm flight deviation behavior. 12th Conference on Aviation, Range and Aerospace Meteorology, January 30-February 2, 2006.

Atlanta,GA. [7.] DeLaura, R., Robinson, M., Pawlak, M.L., and Evans, J.E., 2008, Modeling convective weather avoidance in enroute airspace. 13th Conference on Aviation, Range and Aerospace Meteorology, January 20-24, 2008. New Orleans, LA. [8.] Steiner, M., Bateman, R.E., Megenhardt, D.L., Liu, Y., Xu, M., Pocernich, M.J., and Krozel, J., 2010, Translation of ensemble weather forecasts into probabilistic air traffic capacity impact. Air Traffic Control Quarterly, 18(3): p. 229-254.

[9.] Song, L., Wanke, C.R., Greenbaum, D.P., and Callner, D.A., 2007, Predicting sector capacity under severe weather impact for traffic flow management. 7th AIAA Aviation Technology, Integration and Operations Conference (ATIO), September 18-20, 2007. Belfast, Northern Ireland: p. AIAA 2007-7887.

[10.] Krozel, J., Mitchell, J.S.B., Polishchuk, V., and Prete, J.M., 2007, Capacity estimation for airspaces with convective weather constraints. AIAA Guidance, Navigation and Control Conference and Exhibit, August 20-23, 2007. Hilton Head, SC. [11.] Matthews, M.P. and DeLaura, R., 2010, Evaluation of enroute convective weather avoidance models based on planned and observed flight. 14th Conference on Aviation, Range, and Aerospace Meteorology, January 17-20, 2010. Atlanta, GA. [12.] Matthews, M.P., DeLaura, R., Veillette, M., Venuti, J., and Kuchar, J.K., 2015, Airspace flow rate forecast algorithms, validation, and implementation.

[13.] Klimenko, V. and Krozel, J., 2009, Impact analysis of clear air turbulence hazards. AIAA Guidance, Navigation, and Control Conference, August 10-13, 2009. Chicago, IL. [14.] Sharman, R., Krozel, J., and Klimenko, V., 2011, Probabilistic pilot-behavior models for clear-air turbulence avoidance maneuvers. Aviation, Range and Aerospace Meteorology Special Symposium on Weather–Air Traffic Management Integration, January 24-27, 2011. Seattle, WA.

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Using ADS-B Big Data Analysis to Create Stable Approach Models

scyther5/Shutterstock.com

By Matthew Thompson, FAA

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ADS-B BIG DATA ANALYSIS

A

ircraft on approach to an airport may become unstable for myriad reasons, such as late ATC clearances, improper piloting technique, or weather. Regardless of the reason, the flight crew’s decision to execute a go-around due to an unstable approach is typically the safest option, in addition to being dictated by company procedures. However, the go-around maneuver often takes ATC by surprise and may interfere with other, normal operations. As the NTSB highlighted in Safety Recommendation A-13-024: “The NTSB has recently investigated or reviewed numerous events in which air carrier aircraft that were executing a go-around came within hazardous proximity of other landing or departing aircraft… and have resulted in flight crews having to execute evasive maneuvers at low altitude to avoid collisions”

selected the airports in the observed data for their varied weather conditions, elevation, and traffic density. All runways in the observed data utilize a 3.0-degree glideslope for precision approaches and, where available, visual approach path indicators. Table 1 provides the total number of approaches observed for each individual runway.

According to FAA Comprehensive Electronic Data Analysis and Reporting (CEDAR) data, there were 125 turbojet go-arounds in June 2016 and 97 in July 2016, all of which cite unstable approach as the reason. The intent of this article is to demonstrate that Automatic Dependent Surveillance-Broadcast (ADS-B) and big data analysis can be leveraged to develop a model of stabilized go-around approaches as well as show a low probability of such.. Utilizing positional and performance figures from ADS-B data, augmented with real-time weather observations, it is possible to identify the ideal approach for each individual aircraft type and operator. With a stable approach model compiled, approaches can be observed and compared to the model in real time, and the probability of the approach resulting in a go-around due to instability can be calculated.

The end goal is to provide more advanced warning to air traffic controllers of a possible go-around and to minimize false positives.

Of the 320 approaches analyzed for this paper, the average advanced warning was more than one minute (0:55 and 1:13), while the instance of false positives was less than 0.002 percent.

Mapping of the ADS-B approach data was conducted to augment the available information with the elements necessary to accurately assess the ADS-B point in relation to the runway to which the approach was being conducted. When mapped, the approach corridor for specific runways became quite clear, allowing for the addition of runway metadata to the ADS-B squitter information. Where appropriate, distance to runway end, or runway displaced threshold, were added to the ADS-B positional and vector data.

Model Creation In an effort to create an accurate approach model to compare actual approaches in real time, a number of criteria must be considered, including: aircraft model, aircraft operator, runway specific parameters, and weather. Analyzing large volumes of data, while compensating for multiple variables, quickly highlighted the significant, monitored characteristics of a stable approach. Upon review of the available data, we decided to conduct analysis on the Boeing 737-900 series operated

by Delta Air Lines utilizing ADS-B data.

According to registry records, Delta Air Lines owns 41 737-900s, 39 of which were used in the data collection. In total, we analyzed 320 individual approaches across five Universal Time Coordinated (UTC) days at five airports where Delta Air Lines operates the 737-900.[1] We

Table 1. Number of approaches to individual runways.

Figure 1. ADS-B approach data to SLC highlights the approach corridors for individual runways.

Meteorological Terminal Aviation Routine Weather Report (METAR) information was also collected to augment the ADS-B squitter data based on prevailing weather conditions at the time of approach. Utilizing all available data, estimated indicated airspeed was derived from ADS-B ground speed by first correcting for estimated Continued on Page 32

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IT’S TIME FOR A NEW APPROACH TO ATM

boeing.com/commercial

With the ever-growing amount of traffic in the sky, air traffic management (ATM) is a critical priority that requires continuous progress. Working together with industry and government organizations, Boeing is committed to an ATM transformation that improves safety, efficiency and the environment for all. At the core of Boeing’s ATM solutions are secure network-centric operations that will incorporate the capabilities of modern airplanes, as well as ensure global interoperability and real-time access to critical information. The time is now, and Boeing is ready to help.

ADS-B BIG DATA ANALYSIS

The initial evaluation results were surprising and indicated that vertical speed and height above runway were better indicators of the quality of approach than speed (Figure 3).

Indeed, the variability in speed was reflected at all distances from runway end, while vertical speed and height above runway followed a sharp bell curve. Figure 4 shows the result of 737 data points occurring between 2.04 nm and 1.95 nm from runway end.[2]

Figure 2. Comparison of ADS-B reported ground speed at distance from runway end to IAS estimated speed at distance to runway end.

winds aloft and then for pressure altitude. Using weather information to augment ADS-B reported ground speed proved to be the lynch- Figure 4. Vertical speed, height above runway end, and estimated pin in compiling a stable approach model. Figure 2 demonstrates the indicated airspeed at 2 nm from runway end. importance of correcting for weather conditions.

Further, utilizing METAR pressure, runway end elevation was Figure 5 shows a similar scenario using 738 data points between adjusted to conform with ADS-B squitter-provided pressure altitude.

3.04 nm and 2.95 nm from runway end.

With this correction in place, it was now possible to observe aircraft height above physical runway/displace threshold elevation. Weather conditions during the observed period were varied and provided a good cross-section of elements, as shown in Table 2.

Table 2. Observed METAR weather extremes.

The Model of a Stable Approach We conducted data analysis across 43,667 points along the observed 320 approaches. Distance ranged from runway end to 6.5 nautical miles (nm) from runway end. There were no discernable patterns beyond 6.5 nm from runway end. The maximum observed height above runway elevation was 2,975 feet. The maximum observed pressure altitude was 6,700 feet. The maximum descent rate observed was 3,008 feet per minute. Three major factors were evaluated: • Height above runway at distance from runway end.

• Vertical speed at distance from runway end.

• Estimate indicated airspeed (IAS) at distance from runway end.

Figure 5. Vertical speed, height above runway end, and estimated indicated airspeed at 3 nm from runway end.

Given the model’s corrections for elevation and weather, its applicability remains valid across multiple scenarios.[3] A brief look at the 3.04 nm to 2.95 nm distance to runway end at Atlanta[4] and Salt Lake City[5] shows consistency across vastly different scenarios, as shown in table 3.

Figure 3. Display of the three major factors.

Figure 3a. Vertical speed at distance to runway end.

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Figure 3b. Height above runway at distance to runway end.

Figure 3c. Estimated IAS at distance to runway end.

ADS-B BIG DATA ANALYSIS

However, the go-around maneuver often takes ATC by surprise and may interfere with other, normal operations.

The model’s data suggests that vertical speed, while moderately consistent, varies along any given approach and is rarely a consistent descent rate. To a greater extent, airspeed can also vary along a given approach, likely due to aircraft weight, weather, and ATC instructions.

However, height above runway remains consistent across all approaches. This suggests that the most important factors in determining the stability of an approach are the angle to the runway, followed by descent rate, and, finally, airspeed.

Figure 6 depicts the three major factors for approach stability at all distances from runway end to 6.4 nm from runway end. Each factor Figure 6. Depiction of the three major factors, with σ, across the has been buffered above and below average by one standard deviation entire approach. (σ) to illustrate the delta. Of significant note is the narrow window in which the height above runway end exists.

Probability of Go-Around Calculations The probability determination is based upon the approximate variability in the three major factors previously identified. Each factor is measured against the model average and differences measured in the number of standard deviations from average. Each factor is then weighted based on its apparent value in order to provide a percentage probability of a go-around. The maximum probability of this model

is 90 percent to avoid the impression of certainty. An aircraft with a probability above 80 percent is considered a very high likelihood of going around.

Instances of false positives utilizing this model are very low. Of the model’s 43,667 data points, only 199 have a go-around probability greater than or equal to 80 percent.[6] This number shrinks to 87 points when a runway end distance of less than .05 nm is removed.[7] The Journal of Air Traffic Control

ESB Professional/Shutterstock.com

Table 3. Comparison of vertical speed, height above runway end, and estimated IAS at 3 nm from runway end for ATL and SLC.

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ADS-B provides a level of data fidelity far superior to any existing beacon radars and allows for advanced data analysis.

Comparison of Actual Go-Arounds Due to Instability on Approach On July 22, 2016, Atlanta Tower entered a go-around Mandatory Occurrence Report (MOR) in CEDAR for DAL1991, B737-900.

The aircraft stated the go-around was due to an unstable approach.

METAR reports wind was 200 at four knots, visibility at nine miles, the lowest reported layer was scattered at 300 feet, temperature 22°C, and pressure 30.14 in/Hg. The aircraft flew a left downwind for runway 8L and intercepted the final approach course approximately 6.7 nm from the runway end. The aircraft became unstable on the approach and initiated a go-around, establishing a positive climb rate approximately 1.86 nm from the runway end.

DAL1991 with Model Estimated IAS one σ Above and Below

DAL1991 with Model Vertical Speed one σ Above and Below

DAL1991 with Model Height Above Runway one σ Above and Below

σ = standard deviation

Figure 9. DAL1991’s unstable approach compared to the model.

On August 25, 2016, Baltimore Tower entered a go-around MOR in CEDAR for DAL1271, B737-900. According to the aircraft, the go-around was due to an unstable approach. METAR reports wind was 190 at six knots, visibility at 10 miles, the lowest reported layer at 3900 feet, temperature 31°C, and pressure 30.02 in/Hg. The Figure 7. DAL1991’s course to Atlanta Runway 8L. aircraft entered a right base for runway 10 and intercepted the final Comparing the approach of DAL1991 to the model, the aircraft approach course approximately 5.2 nm from the runway end. The demonstrated a 90 percent probability of go-around (the maximum) aircraft became unstable on the approach and initiated a go-around, at 3.8 nm from runway end, and was above 80 percent at 4.5 nm from establishing a positive climb rate 0.72 nm from the runway end. runway end. The rate of climb did not begin to trend upward (a likely indication of the decision to go-around) until 1.8 nm from runway end.

Figure 10. DAL1271’s course to BWI Runway 10.

Comparing the approach of DAL1271 to the model, the aircraft demonstrated a 90 percent probability of go-around (the maximum) at 4.8 nm from runway end and was above 80 percent at 5 nm from runway Figure 8. Model probability of DAL1991 go-around due to instability. end. The climb rate did not begin to trend upward (a likely indication of Timewise, DAL1991 entered 80 percent go-around probability the decision to go-around) until 1.2 nm from runway end. at 12:27:55 and entered 90 percent go-around probability at 12:28:10.

The aircraft’s vertical speed began trending upwards from 1,664 ft/ min (a likely indication of the decision to go-around) at 12:28:50. The model provided a total of 55 seconds warning, indicating the high probability of a go-around.

Figure 11. Model probability of DAL1271 go-around due to instability.

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ADS-B BIG DATA ANALYSIS Timewise, DAL1271 entered 80 percent go-around probability at 20:43:30 and entered 90 percent go-around probability at 20:43:34.

The aircraft’s vertical speed began trending upwards from 1,472 ft/ min (a likely indication of the decision to go-around) at 20:44:43. The model provided a total of one minute and 13 seconds (1:13) warning, indicating the high probability of a go-around.

DAL1271 with Model Estimated IAS One

DAL1271 with Model Vertical Speed One

Above and Below

Above and Below

weather information, air traffic controllers or managers can monitor instability of an aircraft’s approach in real-time and identify aircraft with a high probability of executing a go-around. The probability output can be consumed in any fashion deemed appropriate. Immediate applications are: • To alert tower controllers that an aircraft has a high probability of going around.

• To alert TRACON controllers that an aircraft may be going around and require re-sequencing.

• To aid in training of air traffic controllers, allowing them to observe how their technique contributes to approach stability.

Matthew Thompson is an ATC specialist with the FAA’s Air Traffic Organization. He is currently the automation lead for the System Operations Security directorate, and the data architect of multiple air traffic programs. Thompson is a graduate of Embry-Riddle Aeronautical University, a certified flight instructor, an FAA certified en route controller, and an ICAO-certified ACC controller.

References

DAL1271 with Model Height Above Runway One

Above and Below

[1.] August 25, 2016; August 30, 2016; September 4, 2016; September 9, 2016; and September 14, 2016.

[2.] For modeling and data handling purposes, distances were rounded to the nearest tenth of a nautical mile; hence the 2.0 nm range is comprised of distances between 2.04 nm and 1.95 nm.

[3.] SLC field elevation – 4,227 ft MSL; ATL field elevation – 1,026 ft MSL. [4.] 426 data points.

[5.] 113 data points.

[6.] Or .004 percent.

[7.] Or .0019 percent.

Figure 12. DAL1271’s unstable approach compared to the model.

The Possibility of a Real-time Approach Stability Monitoring Tool ADS-B provides a level of data fidelity far superior to any existing beacon radars and allows for advanced data analysis. ADS-B positional information is provided every second, six times more frequent than airport surveillance radar; altitude information is provided in 25-foot altitude increments, four times the precision of a Mode C beacon.

Additionally, ground speed is reported directly from the aircraft’s on board GPS, opposed to distance over time calculations currently done by traditional radars. Further, ADS-B provides unique flight identification directly in the squitter, virtually eliminating the possibility of target swapping or target jumping sometimes seen in traditional radar platforms. The increased volume and higher fidelity of ADS-B data will surely allow for new and unique applications to increase safety.

Calculating go-around probability due to observed instability begins with geospatially identifying a track entering a geometrically defined area designated as the approach corridor to a specific runway.

PASS Members Are Systems Specialists that ensure functionality of communications systems, computer systems, navigational aids and power systems vital to safe air travel and the mission of pilots and controllers.

Flight Standards and Manufacturing Aviation Safety Inspectors that

oversee every aspect of the aviation industry to ensure adherence to FAA regulations and safety standards.

Figure 13. Depiction of approach corridors at BWI.

Once a track has entered the corridor, the unique Mode S code provided by ADS-B can be cross-referenced with the aircraft registry to obtain specific type and operator. With this data, along with

Professional Aviation Safety Specialists, AFL-CIO www.passnational.org The Journal of Air Traffic Control

35

Will Drones Change Personal Mobility?

By Thomas Spencer, Antonio Trani, and Nick Hinze, Virginia Polytechnic Institute and State University, and Frederick Wieland, Intelligent Automation, Inc.

W

hen the NAS was designed more than 50 years ago, its creators used the US transportation model as its backbone; they could not have foreseen the volume and type of unmanned aerial systems (UAS) missions infiltrating our airspace today. Some of the uses of UAS are transportation related. One of the areas where UAS technology might spur a large increase in demand is the air taxi industry. In this article, we take a detailed look into UAS technology for air taxi services and ask if the demand for air taxi services will increase with the introduction of UAS technology. If so, by how much?

Under a two-year NASA study, Intelligent Automation, Inc. (IAI) and Virginia Polytechnic Institute and State University (Virginia Tech) investigated future uses of UAS technology by interviewing over 50 subject matter experts from over 29 civil/government and industrial organizations. Using the data, the project produced data sets of likely 36

Spring 2017

future UAS flights, including all the data that would normally be part of a flight plan – the UAS flight’s origin, destination, departure and arrival time, flight route, and even type of UAS flown for the mission.

In terms of implications for the NAS, consider the number of air taxi flights today. The FAA currently classifies air taxi flights for statistical reporting purposes as any aircraft with a maximum seating capacity of 60 passengers or less, or having a payload capacity of more than 60,000 pounds.[12] There are about 7,000 such flights on a typical day, of which we estimate 3,000 of them are for personal mobility.

Air taxi operations today typically consist of four- to six-seat aircraft that are scheduled on-demand from small airports. The passengers are transported to a small airport near their destination, and at their scheduled time of return, the air taxi operator ensures that an aircraft and crew will be available. A typical use case, which arises frequently in the Florida air taxi market, involves lawyers

The survey revealed that 76 percent of 605 air travelers would utilize small turbofan-powered aircraft such as the Cessna Mustang for longdistance travel with a single pilot. who need to brief in the state capitol, Tallahassee. To better maximize their time, lawyers could use an air taxi service to fly direct to Tallahassee, attend the hearing, and fly an air taxi back to their origin later the same day.

Air taxi today is replete with costs that drive its price beyond what most people can afford. The costs typically arise from the pilots: small aircraft have between four and six seats, meaning they can carry three to five passengers (plus the pilot). Therefore, the pilot occupies about 17-25 percent of the available seats. If UAS technology is used to replace the on board pilot with a remote pilot, then costs will decrease and, presumably, demand will increase, especially once the technology has a proven safety record – but by how much? To answer this question, Virginia Tech used their Transportation System Analysis Model (TSAM) to estimate the number of air taxi flights using a pilotless UAS aircraft.

What is TSAM? TSAM is an integrated model that predicts annual county-to-county roundtrip demand for automobile, commercial airline, rail, and air taxi, otherwise known as small aircraft transportation systems (SATS), in the US. The TSAM model utilizes several databases, including socio-economic data (the census, American Travel Survey, and Woods and Poole), airline schedules and travel times (Official Airline Guide), airline fares (Department of Transportation), auto travel times and routes (MapPoint), airports and their characteristics (FAA database), and aircraft technology and their corresponding travel time information. TSAM was developed as part of a NASA-sponsored research effort to investigate the potential for developing a SATS that would utilize existing general aviation airports nationwide. Using the fourstep urban transportation planning process, TSAM is capable of predicting the number of trips generated, the trip distribution, and the The Journal of Air Traffic Control

Drone: iSam iSmile/Shutterstock, Inc.; Cirrus SR-22: Simon_g/Shutterstock, Inc.

DRONES AND PERSONAL MOBILITY

37

DRONES AND PERSONAL MOBILITY The trip distribution module in TSAM assigns the produced mode choice for each possible county-to-county origin-destination trips to each destination county using the gravity module equation.[2] (OD) pair until 2040.

TSAM is comprised of four key computational modules, outlined Where T ij represents trips from county i to county j, P i repin Figure 1, and each module simulates one of four transportation resents trips produced from county i, A j represents trips attracted planning steps.[9] to county j, F ij is a friction factor between county i and county j, and Kij is a socioeconomic adjustment factor for interchange ij.

This module is calibrated at the state level using the American Travel Survey and also distributes the trips by two trip purposes (business and non-business) and the five household income group brackets.[3] The mode choice module applies a family of logit models to estimate the automobile, commercial airline, rail, and SATS demand between the 3,091 counties.[5] This module is also calibrated using the American Travel Survey.[3] The network assignment module includes supporting models that perform commercial airline network assignment, air cargo network assignment, air taxi network assignment, and rail network assignment.

Figure 1. TSAM model flowchart.[5]

The trip generation module estimates annual county-to-county demand produced by and attracted to all 3,091 counties in the United States.[2] This module considers two trip purposes, business and non-business, and five household annual income group brackets: less than $30,000; $30,000 – $60,000; $60,000 – $100,000; $100,000 – $150,000; and above $150,000.

How TSAM Estimates Air Taxi Flight Volume In estimating the expected demand for air taxi services, we used two general aviation aircraft represented by the Cessna Mustang and the Cirrus SR-22T as the assumed operating aircraft for the air taxi missions. Both aircraft are utilized in on-demand private air taxi services.

We first employed a system dynamics (SD) lifecycle cost model to estimate aircraft operating cost using the following cost categories.[10] • Facilities cost: hanger and office space.

• Periodic cost: engine, paint, refurbishing, avionics, mid-life inspection, etc.

• Variable cost: fuel, parts, maintenance, etc.

• Fixed cost: hull insurance, liability, maintenance software, property tax, etc.

• Personnel cost: pilot and crew salaries.

• Training cost: initial, maintenance, recurrent training, etc.

• Capital and amortization cost: resale value, interest rate, purchase cost, etc.

Each cost category is modeled as a state variable in systems dynamics. The rate categories feeding state variables were various yearly costs required to keep the vehicle flying. Data sourced from Business & Commercial Aviation and the Aviation Research Group/ US, Inc. are used to populate the model.[4] The model is then used to

Gravity Module Equation Table 1. Performance and cost parameters for UAS on-demand air taxi mission.[1]

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Parameter

Cessna Mustang

Cirrus SR-22T

Acquisition cost

$3.2 million

$0.712 million

Seating capacity

4 seats

3 seats

Typical cruise speed

340 knots @ 30,000 feet

180 knots @ 10,000 feet

Runway length requirement

3,200 feet @ ISA sea level conditions

2,900 feet @ ISA sea level conditions

Typical annual utilization

800 hours/year

800 hours/year

Range

1,035 nm with 3 passengers

615 nm with 2 passengers

Automation cost

$500,000.00

$150,000.00

Fuel cost

$6.10 per gallon

$6.70 per gallon

Typical hourly cost

$1,522 per hour

$550 per hour

Cost per mile

$5 per nautical mile

$3.50 per nautical mile

DRONES AND PERSONAL MOBILITY accrue all lifecycle costs and then estimate the hourly operating cost for a user-defined lifecycle.

IAI and Virginia Tech estimated demand for the air taxi mission using a two-step approach. In the first step, we estimated potential demand for the service. We used TSAM and employed the performance and cost parameters listed in Table 1. Autonomous operations results using the Mustang are shown in Figure 2.

For the second step, the estimated demand is corrected based on public perceptions and weather reliability of the service. To gauge public perception, Virginia Tech conducted a survey of potential air travelers at five airports on the US East Coast. The survey revealed that 76 percent of 605 air travelers would utilize small turbofan-powered aircraft such as the Mustang for long-distance travel with a single pilot.[10] For smaller single-engine, piston-powered aircraft such as the Cirrus, the acceptance rate dropped to 40 percent. For both aircraft, passenger apprehension primarily centered around the aircraft size and the ride quality compared to larger commercial aircraft.

Virginia Tech also considered the mission reliability of the Cirrus and similar aircraft. Through analyzing METAR data, Virginia Tech found that under normal conditions, only 67 percent of single engine aircraft flights flew in winter due to icing events.[6] Convective weather during the summer months made possible only 70 percent of single engine aircraft flights.

Figure 2 shows the resulting distribution of UAS-enabled air taxi flights.

Figure 2. TSAM flight demand results using autonomous Cessna Mustang very light jet aircraft for on-demand air taxi service.

Will Passengers Accept a Pilotless Small Aircraft?

As unmanned aircraft slowly become more integrated into the NAS, we can expect increased consideration of the use of UAS technology for commercial aviation, including air taxi services. Potential benefits to commercial unmanned aerial vehicles (UAV) include reduced operating costs and enhanced safety. However, despite any possible advantages, considerable hurdles remain that must be surmounted, especially in regards to pilotless passenger aircraft. An exploratory study of UAV public perception supports this assertion as it found little support for pilotless aircraft. As illustrated in Figure 3, respondents soundly rejected UAV, except in the case when a pilot was on board to monitor the operation.

Figure 3. Survey responses regarding the use of UAV airlines for passenger transportation.[8]

Perhaps the greatest challenge to pilotless aircraft is the general safety concern. Aircraft designers will need to put forth a multifaceted response that effectively defends against safety concerns. Fault tolerance is another major concern and aircraft designers will need to ensure that the machine flying the aircraft has built-in redundancy and resiliency. As an electronic system, the autopilot will need to be immune to lightning strikes or other events that may cause power loss.

Also important are safeguards against hacking and other methods for unsavory actors to gain control of the aircraft.

Aircraft designers must also demonstrate a situational awareness on par with human pilots. More importantly, designers must somehow demonstrate that an autopilot can process and respond to emergency situations with the same efficacy as human pilots. Taken to the extreme, designers must convince the flying public that an unmanned aircraft could perform US Airways Captain Sullenberger’s “Miracle on the Hudson,” or replicate the chain of events that safely landed Air Canada 143, “The Gimli Glider.” In each of these situations, human pilots were able to quickly assess the relevant facts and develop appropriate responses that may not be immediately recognizable but nevertheless led to successful outcomes.

Designers may argue that removing human pilots may result in considerable improvements to safety. It’s estimated that as many as 85 percent of aircraft accidents are the result of human error.[7] Removing human error, while also not substituting human error for machine error, would form a strong case in favor of unmanned passenger aircraft.

Pilotless aircraft may not necessarily operate with total independence from the human, and societal acceptance of these aircraft may hinge on the degree of autonomy allowed. Autonomy implies that an aircraft can execute a “decision cycle” in the same manner as a human operator. One model of a decision cycle is illustrated in Figure 4, in which one observes, orients, decides, and then acts.

Figure 4. The classic OODA Loop.[11]

Therefore, a fully autonomous aircraft would perform all elements of this decision cycle without human intervention. However, human intervention at any point in this decision cycle remains possible without a pilot in the aircraft cockpit. For example, much in the same way that military drones are operated, a certified pilot may still remotely fly a pilotless aircraft. One might imagine a scenario where The Journal of Air Traffic Control

39

DRONES AND PERSONAL MOBILITY

For city pairs that are a bit too far to drive, and for which there is no commercial air service, air taxi will become a viable alternative for more people in the future.

The largest impact of this technology is in personal mobility. For a pilot will remotely taxi and takeoff an aircraft and then initiate the autopilot to control the remainder of the flight, including the land- city pairs that are a bit too far to drive, and for which there is no coming. While not a fully autonomous operation, such a scenario may mercial air service, air taxi will become a viable alternative for more represent a publically acceptable level of automation, despite being a people in the future.

As for implications to the NAS, these flights are transportationpilotless aircraft. oriented, and the current NAS architecture is able to handle such flights. These flights are itinerant in nature, and they do add Conclusion This study concentrated on four-seat aircraft that represent on-de- significant volume to the NAS. On a clear-weather day with average mand air taxi. We estimate that there are approximately 3,000 such demand, we can expect up to 14,300 such on-demand air taxi flights, flights each day currently. Our estimate of the number of autonomous which represents an increase of about 11,000 air taxi flights/day, or an Cessna Mustang flights in the future is 3,792 flights per day, or a overall increase in NAS volume of about 22 percent. This increase is bit more than the number of air taxi flights that currently exist. The significant and will require additional controller staffing and possible estimate for autonomous Cirrus on-demand flights is much higher, re-sectorization to handle the workload, but it is achievable within at 10,508 flights per day. This higher number results from the fact the current NAS architecture. The real implementation challenge for that Cirrus on-demand flights are much less expensive than Mustang, UAS-enabled air taxi is public acceptance of a pilotless flight. which induces additional demand. Both of these numbers assume visual metrological conditions with no icing and winds that are with- References in the performance envelope of both aircraft. As noted earlier, icing [1.] Ayyalasomayajula, S., Sharma, R., Wieland, F., Trani, A. A., Hinze, N., and Spencer, T. (2015). “UAS Demand Generation Using Subject Matter in winter and convective weather in summer reduce the number of Expert Interviews and Socio-economic Analysis.” Proceedings of the 15th such flights.

AIAA Aviation Technology, Integration, and Operations Conference, Dallas,

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[2.] Baik, H., Trani, A. A., Hinze, N., Swingle, H., Ashiabor, S., and Seshadri, A. (2008). “Forecasting Model for Air Taxi, Commercial Airline, and Automobile Demand in the United States.” Transportation Research Record: Journal of the Transportation Research Board, 2052, 9-20.

[3.] Bureau of Transportation Statistics (BTS) (1995). “American Travel Survey: Technical Documentation.” Bureau of Transportation Statistics.

[4.] Business & Commercial Aviation. (2012). “2012 Operations Planning Guide.” Business & Commercial Aviation.

[5.] Chirania, S. R. (2012). “Forecasting Model for High-Speed Rail in the United States.” M.S. thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States (October).

[6.] Gates, M. (2004). “The Effect of Icing on the Dispatch Reliability of Small Aircraft.” M.S. thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States (October).

[7.] MacSween-George, S. (2003). “Will The Public Accept UAVs for Cargo and Passenger Transportation?” 2003 IEEE Aerospace Conference Proceedings, Big Sky, MT, 357-369.

[8.] Tam, A. (2011). “Public Perception of Unmanned Aerial Vehicles.” Aviation Technology Graduate Student Publications, Purdue University <http://docs. lib.purdue.edu/cgi/viewcontent.cgi?article=1002&context=atgrads> (accessed Dec. 3, 2016).

[9.] Trani, A. A., Baik, H., Swingle, H. S., and Ashiabor, S. (2004). “Integrated Model to Study the Small Aircraft Transportation System.” Transportation Research Record: Journal of the Transportation Research Board, 1850, 1-10.

[10.] Trani, A. A., Baik, H., Hinze, N., Ashiabor, S., Swingle, H., and Seshadri, A. (2007) “Transportation Systems Analysis of the Small Aircraft Transportation Systems.” Report to NASA Langley Research Center, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States.

[11.] Transportation Research Board and National Research Council. (2014).

Autonomy Research for Civil Aviation: Toward a New Era of Flight. The National Academies Press.

[12.] Federal Aviation Administration, “OPSNET Reports: Definition of Variables,” 16 October 2014. [Online]. Available: http://aspmhelp.faa. gov/index.php/OPSNET_Reports:_Definitions_of_Variables. [Accessed November 2016].

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1800 Alexander Bell Drive, Suite 130 Reston, VA 20191 Phone: (703) 351-5646 Email: kavoussi@avmet.com Website: www.avmet.com

The Aims Community College Air Traffic Control Program holds the FAA AT-CTI designation. We prepare students for air traffic control careers, and our graduates have a 95 percent success rate at the FAA training academy in Oklahoma City.

With small class sizes, one-on-one simulator instruction, and experienced faculty, Aims is the perfect place to train for your career as an air traffic controller!

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45

A Skill Development Workshop for ATC On-the-Job Training Instructors

By Lauren J. Thomas, Evans Incorporated, and Stephanie Kreseen, FAA

g-stockstudio/Shutterstock.com

T

46

he path to becoming an air traffic controller requires considerable investment from both the individual and the FAA. Newly hired candidates must learn basic air traffic control concepts at the FAA Academy before being assigned to a specific control facility. Upon arrival, trainees undergo additional classroom and simulator training to master facility-specific items, including airspace and operating procedures. Trainees are then assigned to training teams for on-the-job (OTJ) training, all with the goal of achieving Certified Professional Controller (CPC) status.

While this process normally takes between 2.5 and three years, it is unclear whether the check-out could be sped up with ideal staffing levels, scheduling, and training resources. Even for experienced controllers, who may transfer to highly complex facilities, the training process takes considerable time. Training time continues to rise despite efforts to reduce the certification time.[8] The lengthy training process, combined with the certified controllers’ retirement eligibility and facility challenges, contributes to a potential CPC shortage over the next several years. In 2016, the Office of the Inspector General reported that 27 percent of the FAA’s CPCs are eligible for retirement nationwide, with 35 percent of CPCs eligible to retire at the most critical facilities. As an increasing number of CPCs reach retirement age, fewer instructors are available to train new controllers, effectively delivering a “double whammy” to operational staffing levels.

According to the FAA’s strategic workforce plan for air traffic control, over 700 CPCs retired in 2015.[4] This number has remained fairly consistent for the last few years, and has contributed to the predicted shortfall in CPC numbers. Although the FAA plans to hire at approximately the same rate as retirements for the next several years, this does not ensure consistency in the controller workforce. Almost 30 percent of trainees will not successfully achieve CPC status.[4] This Spring 2017

SKILL DEVELOPMENT

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SKILL DEVELOPMENT

Pressmaster/Shutterstock.com

Almost 30 percent of trainees will not successfully achieve CPC status.

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shortfall represents a significant loss in training resources, in addition to the human cost the trainee bears in failing to certify in their chosen career. The majority of these training failures occur within the on-thejob training process, once a trainee has reached an operational facility.

Trainees spend most of their time in the on-the-job portion of the training process, where they practice applying the knowledge and skills they have acquired in a classroom and sim settings to real traffic at a real facility. A CPC on-the-job-training instructor (OJTI) oversees this process. FAA Order 3120.4P, Air Traffic Technical Training, specifies the requirements for the selection, training, and certification of OJTIs.[3] According to the order, OJTIs must be certified at the CPC level for a minimum of 12 months, have the recommendation of their front line manager (FLM) and a selection panel, and successfully complete the FAA OJTI course or OJTI Cadre course.

During the three-day initial OJTI training course, new OJTIs are taught basic learning concepts, including the OJTI’s role, and how to run an on-the-job training session. Much of the course focuses on the OJTI’s responsibilities, which include teaching, coaching, and demonstrating air traffic techniques, and documenting and discussing each training session with the trainee. In 2011, an FAA study found that 43 percent of OJTIs surveyed did not feel the OJTI training course appropriately prepared them with the skills and techniques necessary to be an effective training instructor.[7] Since OJTIs are tasked with training the next generation of controllers as well as maintaining operational integrity, one could argue that a three-day course is not sufficient. Further, OJTIs have limited requirements for continued training and professional development, and many have not received updated instructional training since completing the initial course.

Spring 2017

Applying research and best practices in instructional techniques to the on-the-job training process is one solution to the FAA’s training shortfall. The potential benefits to this approach are: (1) to reduce the time a trainee spends in the on-the-job portion of training; (2) to increase the rate of trainees successfully gaining CPC status; and (3) to increase OJTIs’ recognition and support as training professionals. In many fields, training increasingly incorporates adult learning principles, including commercial aviation pilots, doctors, and military.

This approach has also helped address a number of training issues, including the generation gap between baby boomer instructors and millennial trainees, which is important in ATC training.

In general, air traffic control has been slow to capitalize on some of these possibilities. The initial OJTI course’s basic content has not changed for some time, and the learning content largely focuses on the training process, such as the pre-brief, the debrief, effective documentation, and report writing. A recent FAA survey of Employee Requested Reassignments (ERRs) from trainees at facilities indicated that the OJTIs’ skills were a critical factor in requesting ERR.[10] Recent research into the most effective teaching, training, and instructing methods may provide fruitful information on how best to deliver ATC training in operational contexts.

Having identified this possible gap in training research and the application of adult learning principles to ATC instruction, the FAA’s NextGen Human Factors Division commissioned Evans Incorporated to examine human factors research as it applies to learning air traffic control skills. Based on this review, Evans Incorporated developed trial versions of OJTI instructional materials. An ATC test facility then validated these materials during one-day workshops. For the trial, Continued on Page 50

SKILL DEVELOPMENT

Applying research and best practices in instructional techniques to the on-thejob training process is one solution to the FAA’s training shortfall.

ESB Professional/Shutterstock.com

Evans selected an East Coast TRACON with highly complex airspace and critical staffing levels. The project sought to gain feedback from OJTIs, trainees, and the training department on this approach in order to provide data to the FAA on the value of providing enhanced skills training to OJTIs.

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responsibilities can affect an adult learner’s progress, and the longer the training takes, the more difficult it is to predict which external factors may come into play. Quite simply, life happens.

Many of these principles are not evident in the current ATC training culture. “Traditional” air traffic control training requires trainees to absorb the required theoretical knowledge before they actually Adult Learning Principles Adult learning as a distinct discipline is a recent development in the need it. Without having a clear path to follow throughout the training psychology of education. Malcolm Knowles was a key proponent of process, it can be difficult for trainees to know their next meaningful the andragogic approach, which focuses on a shift from “teaching goal, or what they need to do to accomplish it. It can also be difficult people” to “helping people learn.” Knowles, working throughout the for trainees to build on what they already know, which is a particular mid and late 20th century, saw adult learners as active participants in challenge for transferring trainees. Transfers with more complex airthe learning process and in achieving their own success. The overall space experience are often discouraged from referencing procedures and practices at their former facility. However, that is one of the priapproach is summarized in a few key principles: [5] mary ways by which a trainee grows in their career.

• Adults need a reason for learning: Adult learners need to know Many ATC trainees report that the training process does not that the information will be useful, will help them to reach a goal, always encourage their input. Many report that more importance and/or will provide them with satisfaction or enjoyment. is placed on pleasing the OJTI to receive a positive daily training • Building on existing knowledge: Adults need to gain experience report. Asking questions or making errors and mistakes may be to learn – including the freedom to make errors and mistakes – seen as a weakness rather than an opportunity to develop problemand the opportunity to learn from those situations. solving strategies and enhance a trainee’s learning. A lack of traffic • Participation in decision-making: Adults need to share responsibility for their learning and have input into decisions about predictability in a live traffic environment makes it difficult for a trainee to practice the most necessary skills. This is compounded when their education and training. partly-qualified trainees are used on sectors for which they are already • Readiness and timing: Adults learn best when information or certified. This further reduces practice time for trainees, and ripens the opportunities are presented at “point-of-need,” when they can conditions for skill fade. Finally, the length of time that it takes to train immediately apply what they have learned. inevitably means that life events interfere to a certain extent – a lot can • Problem-solving learning: Adults prefer to learn to solve change within the three years it can take to certify. problems. They do not generally learn content simply for the sake of it; they want to know what issue, problem, or concern the Practice and the Transfer of Expertise knowledge or skill will accomplish for them.

Against this backdrop, trainees gain enough skill to become certified, • Learning motivation: The most powerful motivators for adult allowing them to work traffic without direct monitoring. Expertise, learning tend to be intrinsic.

• Life happens: For adults learning new professional skills or facing almost by definition, is implicit – experienced controllers and expert OJTIs will often control traffic automatically, at least for the more a new challenge, life commitments can sometimes reduce the routine elements. Like with any vocation, controllers’ frequent tasks time the adult can dedicate to learning. A wide range of external Spring 2017

SKILL DEVELOPMENT will eventually become habits, and it can then become difficult for them to vocalize them to a trainee.

Practice also greatly influences this progression from novice to expert.[2] Modern perspectives suggest that motivation and interest are critical for, and foundational to, practice. Especially in the case for ATC trainees, practice makes perfect, or as close to it as possible. In live traffic situations, trainees have to work with the situations that are presented to them. They cannot practice departures when they are working arrivals, and they can only practice rare and unusual events if they occur while on position. In contrast, the dogma in air traffic control is if a trainee has accomplished a task once, then their performance should be foolproof going forward. This does not take into account the human factor. Even experienced controllers make errors.

foundational teaching and learning techniques as well as modules on using these methods, approaches, and techniques in classroom, simulator, and live traffic contexts. For this research, the FAA made the workshop available to all OJTIs within the test facility. They kept the workshop length at one day to allow this to fit with operational schedules. As such, the FAA’s NextGen Human Factors Division, in collaboration with the test facility, selected four core topic areas to include in the workshop: 1. Adult learning and differentiation in teaching.

2. Social psychology of the ATC operations room.

3. Structuring and conducting OJT sessions.

4. Constructive feedback and report writing.

Prior to delivering the workshop, the FAA’s NextGen Human Factors Division arranged for Evans Incorporated to hold a pilot session with a number of facility personnel. A bargaining unit representaTraining Culture in Air Traffic Control Another ATC challenge is the social environment and work- tive also approved the workshop materials at the national level. Evans ing culture. A seminal ATC study identified a number of key beliefs Incorporated, working closely with the FAA’s NextGen Human Factors among air traffic controllers. Among 100 interviews with air traffic Division, then implemented the workshop program. Implementation involved delivering multiple one-day sessions to 118 OJTIs and controllers and OJTIs, three themes consistently came up:[9] 1. T he importance of ability: A belief that people are “born” to do the FLMs within the facility. The workshops took place between May and job, and not necessarily made – that you need a special gift or suite September 2015, and involved up to 12 participants per session. The test of abilities to be able to handle the role – and that “you’ve either got facility made an effort whenever possible to meet Evans Incorporated’s the right stuff, or you haven’t.” In contrast, recent research focuses on request to include participants from a range of different areas in each workshop, and to have FLMs attend a separate session. motivation level and time spent in deliberate practice.

2. T he importance of performance: A belief in achievement and performance, put simply – “those that can, do.” Performing the role Workshop Validation is believed to be the way to gain, and maintain, self-worth and peer Qualitative feedback was extremely positive. Most OJTIs at the test respect. The pinnacle of performance is someone who is sharp, swift, facility welcomed the opportunity to receive additional training and and full of finesse, and who can handle a lot of traffic, any eventu- felt such training should be a more regular occurrence. Some OJTIs ality, all with style and flair. These controllers are seen as “naturals.”

3. T he importance of confidence: A belief that a high level of confidence, almost bordering on arrogance, is a necessary job requirement. This refers to the widely held ATC belief that “it’s a confidence game,” and that calm authority and absolute confidence is a requirement. In reality, controllers are humans and subject to the same performance shaping factors and variability.

Evans Incorporated carefully designed the workshop materials to reflect some of the training challenges within this unique operational environment, and to give OJTIs some techniques.

Design of the OJTI Workshop Using some of the andragogic principles relevant to air traffic control training as well as best practices, such as active learning, interactive engagement, and real world relevance, Evans Incorporated designed a one-day enhanced training workshop for OJTIs. The FAA’s NextGen Human Factors Division delivered this session on a trial basis to all OJTIs at a TRACON test facility. The materials included several interactive activities: • Class discussions and small group activities.

• Building a three-dimensional model.

• Breaking down a task into component parts for teaching purposes.

• Using real examples of previous daily training reports.

Due to time restrictions, the workshop was limited in scope and did not cover all possible learning topics. For example, one European ANSP has an 11-day modular Advanced Training, Learning, and Assessment Skills (ATLAS) program for providing enhanced training skills to OJTIs. The ATLAS program includes a four-day module covering

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SKILL DEVELOPMENT “Training is an uphill battle, but working together, the trainee and the trainer can get to the top and be successful.”

“ATC is a challenge from the beginning to end, just as life is. This card signifies this challenge.”

“With hard work, trainees need to go into the process knowing that there is no mountain too high to climb.”

“You can’t do everything by rote. You have to look at what is going on and make a plan on how to accomplish individual tasks. For trainees, if you do something wrong (like fall), don’t just say – hey, you fell! Give advice on how not to fall.”

Figure 1. Post-workshop quotations from facility OJTIs.

Dudarev Mikhail/Shutterstock.com

expressed views during the workshop that their colleagues challenged.

The workshops gave OJTIs a forum to discuss their views and experiences, informed by adult learning research and teaching best practices.

In providing feedback immediately on completion of the workshop, the Evans Incorporated instructor asked participants to select an image from a wide range of photographs to summarize the key learning point from the day, and to explain why they had chosen that card.

Quotes associated with one of the most frequently selected images appear in Figure 1.

In addition to the OJTIs’ comments, trainees also reported changes in OJTI behavior following the workshops, as did the training support manager. The facility’s training support manager highlighted the differences in monthly training reports. Before the workshop, the report samples contained limited analysis and no trainee suggestions for improvement; after the workshop, reports contained much more detail, including focus areas for trainees. The training support manager noted, “This is another example of the positive and significant changes the human factors OJTI workshops are having at this facility.”

It is difficult to provide more objective data on the effect of the workshops on training outcomes for two reasons. First, training evaluations are more robust when conducted in a longitudinal manner. The timing of this research did not permit a more long-term follow-up of OJTIs and trainees, or an analysis of training metrics before and after the workshop. Secondly, the enhanced OJTI workshop was one of a number of human factor initiatives the FAA was exploring. These initiatives were in varying stages of completion at the time of the research, and hence any one of those programs may influence more long-term evaluation data. Overall, facility feedback has been positive, and the FAA is interested in adding similar workshops at other facilities.

52

Conclusions Initial results from the enhanced OJTI techniques workshop are promising. However, in considering a wider rollout, the FAA is also mindful that what works well at one facility may not be suitable for others. The FAA will need to determine how best to introduce the content on a national scale, while allowing customization at the facility level. For example, a trainer with professional education and ATC adult learning experience delivered this workshop to the test facility; however, it’s unlikely that each facility will have access to this type of trainer. Management and union support was also an enabling factor in the success at the test facility. The workshop may not have been so well received had it not been for facility personnel engaging in the material’s development and delivery. Identifying what fostered success is essential for positive results at the national level.

Spring 2017

Providing enhanced OJTI skills workshops to those providing on-the-job training at facilities is just one approach. Improving on-the-job training outcomes across the whole FAA training system will likely require a more holistic approach, including refresher training for OJTIs, coaching in instructional techniques, and access to updated information and support through other activities, such as training conferences. As the FAA continues to evaluate the enhanced OJTI skills workshop described here, it will also be necessary to determine how to best combine and align this with other OJTI training provisions within the FAA. Of course, while OJTIs are a vital part of the training system, they are not the only driver of success—trainee recruitment and selection, FAA Academy training, and the quality and availability of simulation also determine training outcomes across the FAA. Nevertheless, with a significant shortage of qualified controllers in the near future, developing an enhanced OJTI techniques workshop is crucial. Equipping OJTIs with the skills to become more effective instructors will help to prevent a critical shortage of CPCs by increasing trainee success rates and decreasing certification times. By investing in a key resource, the OJTIs themselves, the FAA can ensure trainees gain maximum learning value from the time they spend with the experts on their training team.

References

[1.] Benner, P. (1984) From Novice to Expert. The American Journal of Nursing.

82(3), pp. 402–407.

[2.] Ericsson, K.A., Krampe, R.T. & Tesch-Römer, C. (1993) The role of deliberate practice in the acquisition of expertise. Psychological Review, 100(3), pp.

363–406.

[3.] FAA (2015) Air Traffic Technical Training. Order JO3120.4P, 10/30/2015.

FAA Air Traffic Organization; US Department of Transportation; Washington, DC. [4.] FAA (2016) A plan for the future: 10-year strategy for the air traffic control workforce. US Department of Transportation; Washington, DC. [5.] Knowles, M.S. (1973) The adult learner: A neglected species. American Society for Training and Development; Madison, WI. [6.] Knowles, M.S. (1988) The modern practice of adult education: From pedagogy to andragogy. Cambridge; Englewood Cliffs; NJ. [7.] OIG (2013) Audit Report: FAA is making progress but improvements in its air traffic controller facility training are still needed. Report Number AV-2013121. August 27, 2013. Office of the Inspector General; Washington, DC. [8.] OIG (2016) Audit Report: FAA continues to face challenges in ensuring enough fully trained controllers at critical facilities. Report Number AV-2016-014.

January 11, 2016. Office of the Inspector General; Washington, DC. [9.] Owen, C. (1999) Learning in the workplace: The case of air traffic control.

Unpublished PhD Thesis; University of Tasmania.

[10.] Pierce, L.G., Byrne, C.L., Broach, D., & Bleckley, M.K. (2014) Perspectives of Unsuccessful Air Traffic Control Specialists. White Paper, FAA Civil Aerospace Medical Institute.

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MH370:

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Spring 2017

THREE YEARS LATER

Three Years Later

By Steve Winter, Independent Aviation Consultant

T

he Southern Indian Ocean is a lonely and remote place. Very little shipping plies its hostile waters these days; no longer do clippers sail its famous Roaring Forties. Only an occasional aircraft crosses its skies, leaving a trail of water droplets in its wake between South Africa and Western Australia (and even these routes are further to the south). It’s the perfect place to disappear.

Three years ago, this pristine oceanic wilderness was disturbed.

As the sun rose in the east, an aircraft intruded on the morning’s kaleidoscope of colors. It was a large plane, one suited to flying long distances over deep waters, a Boeing 777-200. But it didn’t belong there.

At first, everything seemed routine as the aircraft headed on a steady course at its cruising altitude. However, it was heading south, a route to nowhere. Inside the tanks of the aircraft, the fuel, intended to provide adequate reserves for its planned route from Kuala Lumpur to Beijing, was rapidly diminishing as the Boeing 777 neared its endurance limit of 7.5 hours flying time.

Eventually, as the sun rose higher in the largely cloud-free sky, the last drops of fuel were exhausted, and the inevitable occurred. The two engines, one after the other, flamed out and drag began to reduce the airframe’s speed. The autopilot, which had been programmed to maintain the southerly cruise, became disengaged with the loss of power.

All too quickly one wing started to dip, and the plane began a descent to the ocean, freefalling into a steep spiral dive to the depths below.

In response, the auxiliary power unit (APU) deployed to restore power to critical systems, enabling the aircraft’s satellite data unit (SDU) to communicate one last time with the Inmarsat Indian Ocean Region (IOR) satellite.

This story may seem like fiction, but it is an interpretation of the mostly likely scenario for the doomed Malaysia Airlines Flight 370, universally referred to as “MH370.” This is based on information provided by the Australian Transport Safety Board (ATSB) in their analysis, investigation, and search for the aircraft that vanished in the early hours of March 8, 2014. Three years later, despite all this technical analysis, we still have startlingly few facts about what ultimately caused the demise of MH370. On January 17, 2017, shortly before the time of writing of this article, a joint statement was issued that the planned underwater search was completed and that in the absence of firm evidence of a new search location, the search had been suspended.[1]

Though it is not the place of this article to comment on this decision, it will attempt to provide some insight for it.

It may seem to the casual reader that not much has been happening these past three years, but, in fact, the search of MH3070 has been one of the largest and most complex searches in history, challenging the boundaries of knowledge and technology and covering a vast area of ocean – nearly 50,000 square miles to depths of over three miles.

The remarkable image shown in Figure 1 is an actual view of the Indian Ocean taken by the Meteosat-7 geostationary weather satellite at the time of MH370’s final disappearance. In addition, the ATSB and its partners have continued to refine their existing analysis and develop new analysis, integrating information from different sources.

Figure 1. Meteosat-7 image of Indian Ocean, 00:30, March 8, 2014 UTC. The probable flight path of MH370 would have been at the right, from the Malaysian Peninsula down to the southern Indian Ocean off the coast of Western Australia. The approximate search area is shown in red. Source: EUMETSAT

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THREE YEARS LATER The question remains: if such good search and analysis has been performed, why is it that the aircraft has yet to be found? Is there something fundamentally wrong with the analysis and its conclusions? Is the only conclusion that the team has been searching in the wrong place?

New Information and Analysis The new information and analysis gained in the past year falls into several areas: • Additional flight path analysis • Continued underwater search • Retrieved debris and analysis • Debris drift analysis However, an important theme of the past year has been Integrated Analysis (the author’s term), in which the team has pulled together information from these different areas, culminating in the “First Principles” report, released in December 2016.[2] It is indeed this “Integrated Analysis” that indicates that the probability the aircraft is located within the Indicative Search Area is lower than originally thought and that it may be higher elsewhere.

Additional Flight Path Analysis The flight path area analysis has focused on the “seventh arc” [3] aircraft SDU transmissions at 00:19 Coordinated Universal Time (UTC) on March 8, 2014. The analysis by the Defense Science and Technology (DST) group shows that the Burst Frequency Offsets (BFO) – in very simple terms, the “Doppler” of the aircraft transmissions – of these final transmissions are inconsistent with the aircraft continuing a southerly flight path.[4] Furthermore, the first of these transmissions appears to be associated with a power-up event, as would happen with the auxiliary power unit (APU) starting after an engine flameout.

The failure to detect transmissions from the in-flight entertainment system, a system not powered by the APU that was detected earlier in the flight, further reinforces the evidence of a loss of engine power.

The seventh arc itself remains a firm basis for the search area, as it is derived from the Burst Timing Offset (BTO) of the aircraft transmissions and is subject to fewer error factors and interpretation than the BFO.[5] The BFO analysis estimates a range of aircraft descent rates that would be consistent with the observed values (once causes of errors following the loss of power, such as “warm-up drift” in the SDU’s crystal oscillator, have been taken into account). Minimum and maximum descent-rate cases were examined (Figure 2). It is clear therefore that the aircraft entered a steep descent and would have hit the surface at high speed.

SDU Transmission

Minimum Descent Rate Maximum Descent Rate

00:19:29 Log-on request

3,800 ft/min

been released, presented conclusions postulate that the aircraft would have most likely impacted the water within 15 nm of the arc. It would be useful to know the expected aircraft acceleration during descent, as the BFO analysis indicates a substantial increase in descent rate in the eight seconds between the transmissions. The author does not have access to the data to analyze this further.

Incidentally, an important inference from this analysis is that the aircraft navigation systems continued to operate; otherwise, the SDU would not have had the ability to track and maintain contact with the satellite. This would appear to contradict those theories that claim that the aircraft was substantially damaged in flight, e.g., by fire.

Continued Underwater Search The underwater search began in October 2014 and has been ongoing despite weather and equipment interruptions. The initial search area was 60,000 sq. km (23,000 sq. miles) but was expanded to 120,000 sq. km (46,000 sq. miles) in April 2015 (Figure 3). The search of this area was completed in January 2017.[6] Although some underwater wreckage was found, it has been confirmed that none of it is associated with MH370. [3]

Figure 3. Indicative underwater search area, as of December 2015.

Source: ATSB

Given the apparent thoroughness of the search and the successful detection and identification of some washed up debris, it is hard to criticize the decision to halt the search: there is no evidence that re-searching the area would be any more successful. The search confidence is estimated at greater than 95 percent.[2] One recommendation of the “First Principles” review in November 2016 was that an additional search, outside the previously scoured area, should be undertaken. However, as noted earlier, in January 2017, the governments jointly decided to not extend the search for now.

Retrieved Debris and Analysis Several pieces of debris, which have been identified as coming from 00:19:37 Log-on ack 14,600 ft/mn 25,000 ft/min MH370, have washed ashore in the past year.[7] Most important was the Figure 2. Minimum and maximum estimated descent rates based on recovery of the inboard section of the right outboard flap (Figure 4) in 00:19 SDU transmissions. Both sets of estimates show a substantial increase in descent rate in the 8 seconds between the two transmissions, June 2016. The significance was that this section of the flap was adjacent to the right flaperon found in July 2015. By comparing damage to the likely indicative of an increased pitch angle. Source: ATSB flap section and the right flaperon, investigators were able to establish The DST group expects to released further detailed analysis in that the damage was consistent with both being in the retracted/cruise position at time of impact. This yields a high confidence that the airearly 2017.

The ATSB then requested the aircraft manufacturer to simulate craft was in its cruise configuration when it hit the water, indicative of a number of end-of-flight scenarios to examine their consistencies an uncontrolled impact. In addition, the small size of some of the other with the BFO analysis. While full details of this analysis have not MH370 debris found confirms a high impact speed.

56

Spring 2017

14,200 ft/min

THREE YEARS LATER

Figure 5. Residual probability area with drift analysis probabilities overlaid. Red circle highlights intersection of the two. Source: ATSB

Figure 4. MH370 right outboard flap inboard section (inverted).

Source: ATSB

The Australian Commonwealth Scientific and Industrial Research Organisation (CSIRO) conducted a drift analysis and in-water testing of the right flaperon.[8] These showed that not only did the flaperon on La Réunion originate in the estimated MH370 search area in March 2014 but that the flaperon’s drift rate was similar to, but slighter higher than, the undrogued drifters used in the analysis.

An important aspect of this analysis was the integration of the drift analysis with conclusions from the search and flight-path analysis.

The analysis and its conclusions are founded on several axioms: • The debris found indicates that there would have been an initial surface debris field.

• The coverage of the 2014 surface search, which failed to find any debris from MH370 (and which overlapped part of the 7th arc search area), eliminates a significant search area.

• No debris has been found on the coast of Western Australia.

• The timing and locations of the retrieved debris in the western Indian Ocean indicate more likely locations for the aircraft.

• The known drift paths of ocean drifters from the Global Drifter Program (GDP) and Self-Locating Datum Marker Buoys (SLDMBs) deployed during the 2014 surface search and the expected similar drift of aircraft debris.

• The high-confidence ( greater than 95 percent) that the underwater search would have found the aircraft wreckage in the searched areas, leading to the conclusion that the aircraft is most likely elsewhere in the uncertainty area.

Taken together with the Bayesian flight path analysis these conclusions lead to the generation of a new Residual Probability Area for the location of the aircraft (Figure 5). Contrary to what has been stated in some media, the conclusion does not show that the team was searching in the “wrong” area. Rather, it uses the new information to refine the probability area and identify areas of increased probability (as was done in the Air France flight 447 search). In particular, it identifies an unsearched area between 32.5⁰S and 36⁰S along the seventh arc as being the most likely location for the wreckage (Figure 5).

In addition, a team of NOAA-funded scientists have performed a forward drift analysis using a range of start locations in and near the MH370 search area.[9] Initial results from the analyses show that drifters from start locations to the north of the search area tended to cross the Indian Ocean too fast, whereas many of those to the south of the search area tended to reach Western Australia. However, a significant number of drifters from the northern half of the search area reached the vicinity of La Réunion and Madagascar; in fact, one drifter that was in the vicinity of the MH370 search area in March 2014 actually reached the vicinity of the islands in July 2015, around the time the flaperon was found. One intriguing suggestion from the analysis is that there is an expectation that some debris would have been found on the Western Australian coast. Failure to find wreckage there could imply that the impact was further to the northwest than estimated (the predominant South Equatorial Current (SEC) of this region of the Indian Ocean would then have tended to push the debris further to the west, preventing it from reaching Australia). This more northerly drift trajectory is also consistent with the observed growth of barnacles on the flaperon, which only live in warmer waters.

Figure 6 highlights the dependency of drift on the initial location.

Figure 6. Simulated locations of drifters from the seventh arc after eight months, color coded by initial location (yellow, white, and red are the most northerly). The velocities of the predominant currents are also shown. Source: CSIRO via ATSB

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THREE YEARS LATER

Three years later, we still have startlingly few facts about what ultimately caused the demise of MH370.

Other Information There have been various rumors and misstatements regarding the disappearance of MH370, like the claim that ATSB knew the search area was incorrect (which ATSB were forced to refute in a publication).

[10] However, the ATSB confirmed one intriguing claim in 2016 that information was found by the FBI on the pilot’s home flightsimulator, including six data points in the southern Indian Ocean.[11] No further explanation for this has been offered.

Conclusion In the continuing attempt to determine the fate of MH370, scientists and search parties have undertaken an extraordinary and unparalleled analysis and investigation. Even after three years, new information and new insights continue to emerge. Most importantly, the team has now undertaken integrated analysis using flightpath, drift, debris, search, and satellite communications (SATCOM) analysis to provide new insight into the aircraft’s probable location.

Figure 7 shows the “Remaining” search area recommended by the “First Principles” report [1], which will not, as of writing, be searched in the foreseeable future.

likely area, but the probabilities of other areas have increased as new information has become available.

It is likely that additional debris will be found in the months and years ahead that may further indicate what happened to the aircraft.

For now, we can be confident that the sad fate of the aircraft was likely similar to that described at the start of this article. The author’s personal view is that further analysis of the aircraft’s SATCOM, flightpath, and drift may indicate that its flight ended somewhat north of the original search area. It is to be hoped that eventually this will provide sufficient evidence in the future for the search to be resumed (e.g., as further debris is recovered). On this basis, there is indeed no technical advantage to extending the search now.

Lastly, we should never forget that this was, first and foremost, a human tragedy, and we should keep the families and friends of those on board MH370 in our thoughts and prayers. We can only hope that the mystery of MH370 will eventually be solved.

Steve Winter is an independent aviation consultant and an engineering fellow at Raytheon Company. Opinions expressed in the article are those of the author alone. This article is the latest in a series of MH370 articles that have appeared in The Journal.

References

Figure 7. Remaining search area. Source: ATSB

In answer to the questions posed at the beginning of this article: • Why has the aircraft not been found? There are great uncertainties in the likely location of the aircraft. To date, the team has only searched the most likely area. As knowledge has improved (e.g., incorporating the revised drift analysis), the probability of other areas has increased.

• Is the something wrong with the analysis? There is no evidence that the analysis is incorrect, but the team has continued to refine it to improve their confidence. Furthermore, the apparent consistency between the independent drift analysis and the SATCOM/flightpath analyses illustrates that there are no fundamental problems.

• Has the team been searching in the wrong place? As noted above, the team has searched what was considered, at the time, the most 58

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[1.] Joint Communique, Joint Agency Coordination Centre, 17 January 2017, http://jacc.gov.au/media/communiques/2017/com005.aspx [2.] MH370 - First Principles Review, ATSB, 20 December 2016, www.atsb. gov.au/publications/investigation_reports/2014/aair/ae-2014-054/#tab_1_ content [3.] MH370 - A Year Later, Steve Winter, Journal of Air Traffic Control, Spring 2015 issue.

[4.] MH370- Search and Debris Examination Update. ATSB, 2 November 2016, www.atsb.gov.au/publications/investigation_reports/2014/aair/ae-2014054/3tab_3_content [5.] Bayesian Methods in the Search for MH370, Davey, S. et al., Springer Open, 2016 [6.] First Principles Press Conference, The Hon Darren Chester MP, Australian Minister for Infrastructure and Transport, 2 November 2016. http://minister. infrastructure.gov.au/chester/interviews/2016/dci011_2016 [7.] Summary of Possible MH370 Debris Recovered, Malaysian Government, updated 14 October 2016, www.mh370.gov.my/index.php/en/416-summary-of-possible-mh370-debris-recovered-14-october-2016 [8.] The Search for MH370 and Ocean Drift, Griffin, D., Oke, P., Jones, E., Commonwealth Scientific and Industrial Research Organisation (CSIRO), 8 December 2016, www.atsb.gov.au/publications/investigation_reports/2014/ aair/ae-2014-054/#tab_2_content [9.] Analysis of flight MH370 potential debris trajectories using ocean observations and numerical model results, Joaquin A. Trinanes, et al., Journal of Operational Oceanography, 9:2, 126-138,16 November 2016, www.aoml. noaa.gov/phod/news/load.php?pFullStory=20161118_20161118_Drifer_ Trajectories.html [10.] Correcting the Record, ATSB, 25 July 2016, www.atsb.gov.au/newsroom/ correcting-records/false-and-inaccurate-media-report-on-the-search-formh370/ [11.] FBI brought in by Malaysia to help with MH370 mystery, News.com.au, 30 July 2016, www.news.com.au/travel/travel-updates/incidents/fbi-broughtin-by-malaysia-to-help-with-mh370-mystery/news-story/5a534038db02f11913bbc82eb2c0afd1

Directory of Member Organizations Academic/Research Institutions

Advanced ATC Atlantic Beach, FL AIMS Community College Greeley, CO Arizona State University Mesa, AZ Embry-Riddle Aeronautical University Daytona Beach, FL Hampton University, Department of Aviation Hampton, VA Kent State University Kent, OH MIT Lincoln Laboratory Lexington, MA ReiMei Inc. Tokyo, Japan Stockton Aviation Research and Technology Park of New Jersey, Inc. Galloway, NJ Southern New Hampshire University Manchester, NH The Community College of Baltimore County Baltimore, MD The MITRE Corporation/CAASD McLean, VA University of North Dakota/JDOSAS Grand Forks, ND University of Oklahoma Norman, OK Vaughn College of Aeronautics & Technology Flushing, NY VT UASTS Blacksburg, VA

Air Navigation Service Providers

AEROTHAI Bangkok, Thailand Airservices Australia Canberra, Australia ANS CZ Jenec,Czech Rep Austro Control GmbH Vienna, Austria HungaroControl Zrt. Budapest, Hungary NATS Edinburgh, United Kingdom NAV CANADA Ottawa, Canada ROMATSA-Romanian ATS Administration Bucharest, Romania

Aviation Associations

AAAE-American Association of Airport Executives Alexandria, VA Airlines for America Washington, DC AOPA-Aircraft Owners & Pilots Association Frederick, MD CANSO Amsterdam, Netherlands

FAA Managers Association, Inc. Chandler, AZ NATCA Washington, DC National Safe Skies Alliance Alcoa, TN PASS-Professional Aviation Safety Specialists Washington, DC Professional Women Controllers, Inc. Washington, DC

Government & Military Orgs

DOT/RITA/Volpe Center Cambridge, MA EUROCONTROL Brussels, Belgiun FAA Academy Oklahoma City, OK German Air Operation/Command Nordhein-Westfalen, Germany NASA Washington, DC NATO Headquarters Brussels, Belgium NCAR - National Center for Atmospheric Research Boulder, CO US Army Air Traffic Services Command Fort Rucker, AL US Navy SSC LANT N. Charleston, SC USAF Flight Standards Agency Oklahoma City, OK USAF HQ Air Mobility Command/A3 Scott Air Force Base, IL William J. Hughes Technical Center Atlantic City, NJ

Industry – Products & Service Providers

A3 Technology, Inc. Egg Harbor City, NJ Adacel Systems, Inc Orlando, FL Addx Corporation Alexandria, VA Advanced Sciences & Technologies LLC Berlin, NJ AIRBUS Herndon, VA Aireon McLean, VA AirMap Santa Monica, CA Airtel ATN Dún Laoghaire, Ireland AIRTOPSOFT, SA Brussels, Belgium All Weather, Inc. Sacramento, CA Antenna Associates, Inc. Brockton, MA ARCON Corporation Waltham, MA

ASRC Federal Beltsville, MD AT&T Government Solutions Oakton, VA ATAC Corporation Santa Clara, CA ATECH - Negocios Em Technologias Sao Paulo, Brazil Aurora Sciences Washington, DC AvMet Applications Inc. Reston, VA BCF Solutions, PMA Division Arlington, VA BCI-Basic Commerce & Industries, Inc Moorestown, NJ Beacon Management Group Mitchellville, MD

The Boeing Company Alexandria, VA Booz Allen Hamilton Washington, DC BPA Services LLC Washington, DC CACI Arlington, VA CGH Technologies, Inc. Washington, DC Çhangeis, Inc. Arlington, VA Chickasaw Nation Industries Norman, OK CI2 Aviation, Inc. Dunwoody, GA Clancy JG International Lancaster, LA CNA Corporation Alexandria, VA Cobec Consulting, Inc. Washington, DC COMSOFT Solutions GmbH Karlsruhe, Germany Concept Solutions, LLC Reston, VA CPS Professional Services Fairfax, VA Crown Consulting, Inc Arlington, VA

CSRA Falls Church, VA CSSI, Inc. Washington, DC Diamond Antenna and Microwave Corporation Littleton, MA DIGITALiBiz, Inc. Rockville, MD

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MEMBER COMPANIES DSI-Dynamic Science, Inc. Phoenix, AZ Easat Antennas Limited Staffordshire, UK ECS-EnRoute Computer Solutions Egg Harbor Township, NJ EMCOR Enclosures-Crenlo Rochester, MN

Engility Corporation Chantilly, VA Ernst & Young McLean, VA Esterline Duluth, GA Evans Consoles Vienna, VA Flatirons Solutions Arlington, VA Foxhole Technologies, Inc. Fairfax, VA Frequentis USA Columbia, MD General Dynamics IT Needham, MA General Dynamics Mission Systems Fairfax, VA Global Business Analysis Gig Harbor, WA Global Engineering Management Services, Inc. Washington, DC Grant Thornton LLP Alexandria, VA Gryphon Sensors North Syracuse, NY

Guntermann & Drunck GmbH Siegen, Germany

Harris Corporation Melbourne, FL Hewlett Packard Enterprise Herndon, VA Hi-Tec Systems, Inc. Egg Harbor Township, NJ Human Solutions, Inc. Washington, DC IHSE USA, LLC Cranbury, NJ Imtradex Hor-/Sprechsysteme GmbH Dreieich, Germany Indigo Arc LLC Rockville, MD Infina, Ltd McLean, VA Information Sciences Consulting, Inc. Manassas, VA Intelligent Automation, Inc. Rockville, MD Intersoft Electronics NV Olen, Belgium Iron Bow Technologies Niceville, FL

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Spring 2017

JMA Solutions Washington, DC Joint Venture Solutions (JVS), LLC Washington, DC JTA Washington, DC Kearney & Company Alexandria, VA Kongsberg Gallium Ottawa, ON Landrum & Brown, Inc. Cincinnati, OH Leidos San Diego, CA LMI - Logistics Management Institute McLean, VA

Lockheed Martin Rockville, MD LS Technologies, LLC Washington, DC MCR, LLC Bedford, MA

Metron Aviation, Inc. Dulles, VA MicroSystems Automation Group Falls Church, VA Midwest ATC Services, Inc. Overland Park, KS Mosaic ATM, Inc. Leesburg, VA NEC Corporation Tokyo, Japan New Bedford Panoramex Corporation Claremont, CA Noblis Falls Church, VA Nokia Corporation Murray Hill, NJ

Northrop Grumman McLean, VA Orion Systems, Inc. Huntingdon Valley, PA OST, Inc. McLean, VA Parsons Washington, DC PASSUR Aerospace Washington, DC Plastic-View ATC, Inc. Simi Valley, CA Pragmatics, Inc. Reston, VA

Raytheon Company Marlborough, MA Red Hat, Inc. Raleigh, NC

Regulus Group, LLC Woodstock, VA Ricondo & Associates Chicago, IL Rigil Corporation Washington, DC Robinson Aviation (RVA) Inc Manassas, VA Rockwell Collins Cedar Rapids, IA Russ Bassett Corp.

Whittier, CA

Saab Sensis Corporation East Syracuse, NY SAIC-Science Applications International Corporation Washington, DC Searidge Technologies Ottawa, ON Serco Inc Reston, VA Sierra Nevada Corporation Sparks, NV SJ Innovations Oklahoma City, OK Skysoft-ATM Suwanee, GA Snowflake Software Hampshire, UK STR - SpeechTech Ltd. Victoria, BC Subsystem Technologies, Inc. Arlington, VA Sunhillo Corporation West Berlin, NJ Systems Atlanta, Inc. Atlanta, GA Tantus Technologies, Inc. Arlington, VA Tech Source, Inc. Altamonte Springs, FL Telephonics Corporation Farmingdale, NY Tetra Tech AMT Arlington, VA

Thales Air Traffic Management U.S. Overland Park, KS Thinklogical Milford, CT TKOâ&#x20AC;&#x2122;s East Syracuse, NY Triumph Enterprises, Inc. Fairfax, VA UFA, Inc. Burlington, MA Unifly Antwerp, Belgium Vaisala Louisville, CO Veracity Engineering Washington, DC WCG-Washington Consulting Group, Inc. Bethesda, MD WIDE USA Corporation Anaheim, CA

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