Use of Next-Generation Satellite Systems forAeronautical Communications: Research Issues

DRAFT PROJECT FINAL REPORT

prepared by

NEXTOR, The National Center of Excellence for Aviation Operations ResearchResearch Report RR-01-1

Project Principal Investigators:

Michael O. Ball, University of Maryland (UMD)Antonio Trani, Virginia Polytechnic Institute (VPI)

Other Investigators:

Faculty:Leandros Tassiulas, UMD

Graduate Students:Ozgur Ercetin, UMD

C. Quan, VPIA. Switzer, VPI

February 15, 2001

DRAFT: NOT TO BE QUOTED WITHOUT AUTHORS PERMISSION

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The introduction of Next-Generation Satellite Systems (NGSS) into the globaltelecommunications landscape offers the potential to revolutionize how consumers andbusiness entities communicate in the future. Aviation is also currently undergoing majorchanges in its telecommunications infrastructure with modernization efforts underway toconvert from the current analog, primarily voice-based system to a digital voice anddatalink system for both Air Traffic Management (ATM) as well as airline operational(AOC)-type communications. The future air-ground aeronautical telecommunicationsinfrastructure will be a hybrid, made up numerous terrestrial and space-based links suchas VDL-Modes 2 and 3, Mode S, SATCOM, HF to name a few. Each of these will havetheir own specific link characteristics in terms of technical performance and cost as wellas applicability to aircraft platform. Traditionally, air-ground aeronauticalcommunications in domestic airspace has been considered primarily from the ground-based provider perspective. The application of NGSS systems for aviation offers thepotential to greatly change how aeronautical communications is performed in the future.NGSS stands to offer a new alternative to the mix of candidate communications links forfuture ATM and AOC communications.

This project had two inter-related goals. The first was to assess future aeronauticalcommunications applications. Our work developed a traffic scenario for the year 2020and then estimated associated bandwidth requirements for advanced weather applications.Included is a model in which aircraft act as sensors and feed back readings, which arefused by a weather model. The resultant weather information would then be sent to allinterested aircraft.

The second goal involved the investigation of the use of next generation satellitetechnology to support these advanced applications. We developed a model that supportsthe comparison of broadcast, uni-cast and hybrid broadcast/uni-cast satellite systems forthe delivery of weather and other information to aircraft. This model allows for theestimation of the bandwidth requirements for future aeronautical communicationsapplications. It also is supports the comparison, based on bandwidth requirements andprocessing cost, of broadcast or hybrid LEO/MEO architectures, broadcast or hybridGEO architectures and general unit-cast architectures.

Section 1 of the report describes the satellite communications model and summarizes theoverall results of the project. Section 2 described a methodology to estimate aircraft flowsin NAS. Section 3 provides the results of future aeronautical communicationsapplications research.

The authors would like to acknowledge the support and guidance of Robert J. Kerczewskiand Konstantinos S. Martzaklis of the NASA Glenn Research Center in this effort.

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Table of Contents

Section 1 - Communication Requirements and Solutions ..............................1

1.1 Overview........................................................................................................................11.2 Communications Model.................................................................................................21.3 Bandwidth and Cost Analysis........................................................................................31.4 Numerical Results..........................................................................................................51.5 Discussion and Remarks on the System ........................................................................9

Section 2 - Modeling NAS Operations............................................................14

2.1 Scenario Development.................................................................................................142.2 Concept of Operations in NAS 2020 ...........................................................................162.3 NAS Traffic Flows in 2020..........................................................................................162.4 Lumped Air Traffic Flow Model.................................................................................182.5 Critical Air Traffic Densities .......................................................................................22

Section 3 - Aeronautical Communications ....................................................29

3.1 Weather in the Cockpit Applications...........................................................................293.2 Aircraft as Weather Sensor..........................................................................................403.2 Flight and Traffic Information Service Applications...................................................45

Chapter 4 - Conclusions and Recommendations ..........................................51

Bibliography ...............................................................................................54

Appendix A - LATFM Stella II Model Code ........................................................55

Appendix B - Sample FAAETMS Data .....................................................................61

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14

2 Modeling NAS Operations

This section describes the methodology employed to derive realistic aircraft traffic flows in the ho-rizon year of our analysis (the year 2020). The section starts with a brief description of the existingconcept of operations in the National Airspace System (NAS). Then it expands to the concept of op-eration expected to be in place in the year 2020. The section ends with the mathematical descriptionof a Lumped Air Traffic Flow Model (LATFM) used to derive various aeronautical communicationservices associated with multiple airborne platforms sharing a large region of airspace over time.

Modeling operations in the future National Airspace System (NAS) is a difficult proposition. Thisis specially true when the analysis is expected to derive results two decades into the future. Generallyspeaking, the Federal Aviation Administration (FAA) performs forecasts ten and fifteen years into thefuture (FAA, 2000). For this project, we relied on a combination of FAA statistics and forecasts (upto the year 2010 typically), and simple forecast models derived from historical data considering pos-sible capacity constraints in the system.

2.1 Scenario Development

The current state of NAS is a complex combination of airports, airspace, airlines and assets, cor-porate and general aviation users and air traffic control facilities and personnel. The communicationbetween these entities plays an important role in the operational capabilities of NAS and withoutdoubt, contributes in some measure to the capacity of the system. Figure 15 illustrates some of thecurrent communication links used in NAS. These include services stated in Table 3 encompassingthree categories of service: surveillance, navigation, and communication services.

The functional requirements for a future NAS system are derived here under the premise that userswould want to exploit some of the advanced communication capabilities of a moderately developedAirborne Transportation Network (ATN). Deriving functional requirements two decades ahead oftime is a difficult proposition. Nevertheless, some assumptions are needed to derive feasible opera-tional scenarios in the future NAS. The scenarios presented here represent a more aggressive plan thanthat planned under NAS plan 4.0. An attempt is made here to prototype scenarios that would enhancethe safety and efficiency of operations.

While developing a suitable end-state scenario with future communication applications it is rele-vant to understand a proposed classification of services to be provided in the future NAS. In our opin-ion, communication channels can be effectively used in the following areas:

¥

Air traffic management

- Strategic traffic flow management

- Tactical (air traffic control)

¥

Aircraft status information

¥

Commercial services and cockpit applications

- Weather services to the cockpit

- Internet access

¥

Aircraft as weather sensor

¥

Airport and terminal area status

15

¥

Airline operating center related information

¥

General NAS status

More details about these applications will be provided in the following sections of the report.

Figure 15: Current operational concept in NAS.

Table 3: Current communication services offered in NAS.

Service System Remarks

Surveillance Long range radar Primary surveillance mechanism

Mode C and S Secondary radar

Automated Dependent Surveillance (ADS-B)

Oceanic surveillance functionLimited use in continental NAS

Navigation VORTAC and DME Enroute navigation

ILS and MLS Precision approach NAV tool

GPS Enroute and non-precision approaches

Communication ACARS Clearance delivery

VHF Voice Primary ATC-Pilot Communication chan-nel

Dedicated Datalinks Limited use (weather information such as Arnav system)

ACARS

VHF Voice

VHF Voice

ACARS

ACARS

ATC Tower

VHF Voice

Departing Airport

Arriving Airport

Enroute

VHF Voice

VHF Voice

AOC

ATC SystemsCommand Center

ARTCCCenter

Limited use of SATCOM

in NAS

Use of GPS for navigationCDM limited to airlines

16

2.2 Concept of Operations in NAS 2020

This scenario presents a more mature ATC and ATM system beyond of the NAS Plan 4.0 deploy-ment. Figure 16 illustrates the possible evolution of the NAS system showing a substantial use of sat-ellite technology for communication, navigation and surveillance tasks. The 2020 scenario representsa new era in ATC and ATM services. The control and responsibility of aircraft separation services isa decentralized service managed by aircraft (in the air or on the ground) and ATC controllers (on theground). Various forms of on-board automation exist to provide pilots with enhanced situationalawareness of neighboring traffic included new versions of Cockpit Traffic Information Displays(CTID) that fuse information from advanced versions of the Automated Dependence Surveillance(ADS-B) and Traffic Collision Advisory System (TCAS). Coordination of aircraft separation is ahighly decentralized activity with ATC ground controllers making final conflict resolution decisionswhen on-board sensors and aircraft logic cannot coordinate acceptable resolution advisories for mul-tiple vehicles. An array of cockpit services are commonplace providing pilots with advanced weatherinformation services critical to the conduct of flight operations in a single IFR environment.

The Wide Area Augmentation System (WAAS) has been deployed successfully and hundreds ofairports have dedicated Local Area Augmentation Services (LAAS) in support of Category II and IIIprecision approaches. Services provided by WAAS include safety-critical services such as enrouteand precision approach navigation (equivalent to Category I today). Ground radar systems are onlyavailable for backup surveillance tasks. Mode C and S secondary radars play now (in 2020) a similarback-up role played by primary radar in the 1980s and 1990s. The primary surveillance function inNAS is provided using satellite-based systems in the Continental U.S.

Voice communications are only used for critical NAS communications. The role of the ATC per-sonnel is to act as a broker and supervisor in a distributed ATC/ATM environment. ATC benefits fromaircraft sensors and position information to control traffic flows in congested TRACON and enrouteairspace. At the end of this period (circa 2020) it is possible to envision up to 75-80% of the ATC/ATM message exchange actions be given over a dedicated datalink environment.

In the derivation of communication requirements for each one of the three applications presentedin this report required some fundamental assumptions regarding the technology available in the year2020. Some of these assumptions are:

• The growth rate of the aircraft fleet and aircraft operations in NAS follows the FAA predicted pattern until the year 2010. A 1.75% growth in aircraft operations per year is assumed thereafter.

• The equipage rate of aircraft having onboard weather sensors is assumed to be 100%.

• The equipage rate of aircraft having advanced weather displays is 100% across all four classes of aircraft discussed in the report.

• The number of aircraft having access to traffic information services is also assumed to be 100%.

These assumptions are based on the premise that in a steady-state scenario all aircraft flying inNAS will require some form of weather information in the cockpit, act as a weather sensor, and re-quire traffic information services in order to conduct safe operations across NAS. The authors believethat this worst-case scenario assumptions should provide a best example of the cumulative effect oflarge numbers of airborne platforms requesting various services.

17

2.3 NAS Traffic Flows in 2020

The growth in NAS over the period 2000-2020 is expected to grow at a rate of 4-5% per year. NAScapacity limitations will play an important role at ultimately dictating how the demand function forair transport services will grow over time. The FAA has made estimates of demand growth until 2012that can be then extrapolated to predict demand flows in 2020 (FAA, 1999). Table 4 illustrates someof the predicted demand flows for the complete NAS per year.

The statistics shown in Table 4 illustrate the high utilization of air carrier aircraft (around 2,400hours per year) versus only 144 hours per year for the average General Aviation (GA) aircraft. Usingsimple estimates of the traffic growth patterns expected in NAS into the next two decades one mightconclude that up to

1450 operations per hour

(includes air carrier, corporate jets and GA users) couldfly over some of the most congested airspace in the country (for example over Indianapolis and NewYork ARTCC Centers). These numbers need to be adjusted by dwell times inside the area of interestin order to estimate the number of aircraft soliciting communication services. This analysis is ex-plained in the following section.

Figure 16: Conceptualization of future NAS air traffic services (circa 2020).

ACARS

VHF Voice

VHF Voice

ACARS

ACARS

ATC Tower

VHF Voice

Departing Airport

Arriving Airport

Enroute

VHF Voice

VHF Voice

AOC

ARTCCCenter

LEO Satellite

GNSS Satellite

LEO Satellite

LEO Satellite

GNSS Satellite

GES

WRS

ATC SystemsCommand Center

18

Table 4: Current and projected air traffic operations in NAS.

2.4 Lumped Air Traffic Flow Model

In order to quantify aircraft traffic flows over large regions of airspace we developed a LumpedAir Traffic Flow Model (LATFM). This model is based on the principles of Systems Dynamics (SD)and continuous simulation modeling to model aggregate traffic flows. LATFM uses the premise thataircraft inside a region of airspace (say an Air Route Traffic Control Center - ARTCC) can be in anyone of eight possible states (3 enroute, 2 terminal area, and 3 airport states described in the sequel).These states represent different phases of flight or idle conditions on the ground and each one havespecific communication requirements. Each state dwell time has been derived from actual flow anal-yses using FAA Enhanced Traffic Management System (ETMS) data (FAA 1998). The data used inour analysis is representative of traffic flows inside the Indianapolis ARTCC for the sake of discus-sion. However, the model can be tailored to represent any region of airspace desired with minor mod-ifications to some model parameters. So far the model development of this prototype model has beencarried out using STELLA II - a Systems Dynamics software package developed by High Perfor-mance Systems of Nashua, New Hampshire. The model equations are presented in Appendix A of thisreport.

The structure of the model is illustrated in Figure 17. Model inputs are the demand functions de-fining aircraft movements inside the region of interest. The model tracks the aggregate number of air-craft over time (Traffic Flow Module) and computes communication data size requirements forvarious aeronautical services such as weather products in the cockpit (Aviation Weather Module), air-craft as weather sensor (Aircraft as Weather Sensor Module), and flight information services (FlightInformation Services Module).

Measure Current (1998) 2010 2020 Remarks

Number of Operations (per year)

65,300,000 81,200,000 97,360,000 1) Based on FAA Fore-casts until 20102) 1.75% growth fac-tor after 2010

Enplanements (per year)

643,000,000 991,000,000 1,232,000,000 1) Based on FAA Fore-casts until 20102) 3% growth factor after 2010

Number of Air Carrier Aircraft in US (jets only)

5,850 8,284 10,200 Our estimates

Number of GA Aircraft 206,500 242,500 277,500 Our estimates

Hours Flown by Air Carriers

13,600,000 21,292,160 26,615,200 Our estimates

Hours Flown by GA Aircraft

29,800,000 38,494,673 44,050,605 Our estimates

19

Figure 17: Organization of the Lumped Air Traffic Flow Model (LATFM).

It is important to recognize that when modeling communication service requirements in NAS onecan adopt two distinct points of view: 1) an aircraft centered view point to understand the communi-cation services required by an individual flight and 2) an air traffic control and management centeredperspective to view systematically multiple flights traversing regions of airspace and requiring com-munication services. Both of these view points are useful in our analysis since the derivation of com-munication requirements across NAS requires an understanding of both operations densities acrossNAS (obtained using the air traffic control and management centric approach) and individual servicesprovided to the cockpit (derived from an aircraft centered view point). The relationship between thesedifferent viewpoints is illustrated in Table 5. Here we contrast the sizes of traditional air traffic serviceareas (i.e., Air Route Air Traffic Control Center, Terminal Approach Control Areas, and Airport Con-trol Zone) with the aircraft centered view point regions of interest (i.e., far-strategic, near-strategic,

Traffic FlowModule

Aviation WeatherProducts Module

Aircraft as WeatherSensor Module

Flight InformationServices Module

Traffic Demand

Data SizeData Size Data Size

20

and tactical regions).

Table 5: Definition of various regions of analysis.

LATFM Model Equations

A causal diagram depicting the possible transitions of flights inside the ARTCC center is shownin Figure 18. The diagram distinguishes between information flows (dashed arrows) and accumula-tion flows (solid arrows) to showcase eight aircraft states (boldfaced names) inside the volume of air-space of interest. Causal diagrams are standard techniques used in Systems Dynamics modeling(Trani, 1988). The polarity of the causal relationships shown in the diagram represent the generaltrend of the slope relating two immediate variables. For example, the Inbound Aircraft Demand Func-tion (IADF) “causes” an increase in the Number of Aircraft Entering the Enroute Airspace (NAENO)and in the Number of Inbound Transient Aircraft in the Enroute Airspace (NITAER). The nomencla-ture used in this diagrams is explained in the following paragraphs as the equations of motion of themodel are introduced.

Name of the Region Typical Shape and Size (for modeling purposes)

Typical Area (km

2

)(for modeling purposes)

AT

C-C

ente

red

Reg

ions

Enroute Air Traffic Control Center

Rectanglew = 600 km, h = 280 km 168,000

Terminal Approach Control Area

Circular R=110 km 38,000

Airport Control Zone

Circular

R=10 km 314

Air

craf

t-C

ente

red

Reg

ions

Far-strategic Region

Rectanglevariable dimensions

up to 2,700,000 in CONUS

Near-strategic Region

Trapezoidal wedgew=600 km, h1=300,h2=900 km

424,000

Tactical Region

Conical wedge47,100

21

Figure 18: Causal diagram of aircraft states inside ARTCC region.

Using the causal diagram as a starting point we define eight rate equations, one for each aircraftstate in the system.

(1)

(2)

(3)

TAEANITAER NOTAER

IADF

NAENO IAEA

NALETIDTEA

IDTTA NALTA

IDTAA NALAIS

IATA

IAAA

+TAER

+

+

+

-

+

+

-

+

-

-

-

-

OAEA

NALTE

NALAT

NALIS

OATA

OAAA

+

+

+

+

-

+

-

+ +

NAENO

IDAA

ODTTA

ODTAA

ITAA

+

-

-

-

-

+

-

+

ODTEA

-

-++

tdd

TAE At( ) NITAERt NOTAERt–=

tdd

IAE At( ) NAENOt NALET t–=

tdd

IAT At( ) NALET t NALT At–=

22

(4)

(5)

(6)

(7)

(8)

where the

state variables

are:

is the Total Aircraft in the Enroute Airspace system (aircraft) at time , is thenumber of Inbound Aircraft (aircraft) in the Enroute Airspace system at time , is number ofInbound Aircraft in the Terminal Areas (aircraft) at time , is the number of Inbound Aircraftin the Airport Areas (aircraft) at time , is the number of Idle Aircraft at Airports (aircraft)at time , is the number of Outbound Aircraft at Airport Areas (aircraft) at time , is the number of Outbound Aircraft in Terminal Areas (aircraft) at time , and is the numberof Outbound Aircraft in the Enroute Airspace (aircraft) at time . These state variables are representedas a system of eight first-order differential equations (Equations 1-8) that are solved numerically us-ing STELLA’s built-in integration algorithms (see Appendix A for details of the model).

The

rate variables

of the system are defined as follows:

is the Number of Inbound Transient Aircraft in the Enroute Airspace (aircraft/hour) attime , is the Number of Outbound Transient Aircraft in the Enroute Airspace (aircraft/hour) system at time , is the Number of Aircraft Entering the Enroute Airspace (aircraft/hour) at time , is the Number of Aircraft Leaving the Enroute airspace for Terminal Air-space (aircraft/hour) system at time , is the Number of Aircraft Leaving the Terminal air-space for Airport Areas (aircraft/hour) at time , is the Number of Aircraft Leaving theAirport Areas for Idle State (aircraft/hour) at time , is the Number of outbound AircraftLeaving the airport Idle State (aircraft/hour) at time , is the Number of outbound AircraftLeaving the Airport Areas for Terminal Airspace (aircraft/hour) at time , is the Numberof outbound Aircraft Leaving the Terminal areas for Enroute airspace (aircraft/hour) at time , and

is the Number of outbound Aircraft Leaving the Enroute Airspace (aircraft/hour) systemat time .

The dwell times of aircraft in every one of the eight possible states are explicitly represented inthe model. These key variables are: is the Inbound aircraft Dwell Time in the Enroute Air-space system (hours), is the Inbound aircraft Dwell Time in the Terminal Airspace system(hours), is the Inbound aircraft Dwell Time in the Airport Areas (hours), is the Idle

tdd

IAAAt( ) NALT At NALAISt–=

tdd

IDAAt( ) NALAISt NALISt–=

tdd

OAAAt( ) NALISt NALAT t–=

tdd

OAT At( ) NALAT t NALT Et–=

tdd

OAE At( ) NALT Et NALEOt–=

TAE At t IAE Att IAT At

t IAAAtt IDAAt

t OAAAt t OAT Att OAE At

t

NITAERtt NOTAERt

t NAENOtt NALET t

t NALT Att NALAISt

t NALIStt NALAT t

t NALT Ett

NALEOtt

IDTEAIDTTA

IDTAA ITAA

23

Time at the Airport (hours), is the Outbound aircraft Dwell Time in Airport Areas (hours), is the Outbound aircraft Dwell Time in the Terminal Airspace areas (hours), and

is the Outbound aircraft Dwell Time in the Enroute Airspace (hours).

In this first iteration of the model these dwell times are assumed to be constants and thus have notbeen subscripted as a function of time. However, with minor variations the model can represent dy-namic dwell times to incorporate the effect of congestion in any one of the eight state of the system.Dwell times are typically extracted by close examination of the FAA Enhanced Air Traffic Manage-ment data base (FAA, 1998). For example, enroute air carrier overflights dwell 0.4 hours (on the av-erage) over a busy ARTCC Center like Indianapolis if free flight routes are considered. Similarly, ifan air carrier flight ends or originates inside the volume of airspace assigned to the ARTCC in questionthe expected value of the dwell time would increase to 0.52 hours accounting for times in the descentand climb profiles (whichever is applicable). General aviation flight dwell times are higher since theaverage speeds of these aircraft are 150 knots in cruise and 130 knots inside the terminal area. How-ever, GA aircraft trip lengths are far shorter thus producing dwell times of 0.85 hours per ARTCC cen-ter on the average. Using these parameters Table 6 illustrates a typical load scenario for a singleARTCC with 8 terminal areas (1 large hub, 2 medium size hubs, 2 small size hubs, and 3 regionalairports), 20 utility General Aviation airports and 100 public general aviation with small traffic loadsinside the region of interest volume. Figure 19 illustrates two sample demand functions representingthe aircraft movements per hour at a large hub and a regional airport. These demand functions can beobtained (for the baseline year) from the Consolidated Delay and Airport System database (CODAS)(FAA, 2000) and adjusted to represent year 2020 conditions.

The numbers reflect first-order approximations to be used in the load analysis of traffic and weath-er information services of a busy region within NAS in the design year (2020). These numbers do notincorporate any assumptions regarding the presence of Small Aircraft Transportation System (SATS)aircraft.

LATFM Model Interface

To simplify the interaction between a user and the model a simple Graphic User Interface (GUI)was developed for LATFM. GUIs are standard features in the modeling approach adopted in STELLAII (Richmond, 1999), the simulation language used to prototype LATFM. Figures 20 through 24 il-lustrate five screens available as interface elements in LATFM. These screens represent input-outputgraphic user interfaces for each one of the 4 computational analysis modules available in LATFM: 1)traffic flow, 2) aircraft weather services, 3) aircraft as weather sensor, and 4) flight information ser-vices. Figures 20 through 23 illustrate the computational module screens in LATFM. A fifth screen isincluded representing an introduction to the model (Figure 24).

2.5 Critical Air Traffic Densities

The critical air traffic density for a complex airspace that combines airport, terminal, and enrouteservices has been estimated at 0.0090 aircraft/km2. This density would be consistent with the mostdemanding situation expected for a tactical region of analysis (aircraft-centered region). The densitiesof airport areas are much higher (at 0.1630 aircraft/km2 because they involve aircraft moving onground requesting COM services).Enroute densities are projected to be around 0.0055 aircraft/km2

under the most demanding conditions. This situation would be analogous to the near-strategic regionscenario.

ODTAAODTTA ODTEA

24

Table 6: Predicted peak aircraft traffic in region of interest (includes aircraft classes breakdown).

FAA Service Area Aircraft Type

Total Aircraft inside Region of Interest (Aircraft per hour)

Instantaneous Aircraft inside Region of Interest

Airport General Aviation 231 35

Corporate 32 5

Commuter 140 28

Transport-Type 245 61

Total 647 128

Terminal Area General Aviation 128 128

Corporate 16 16

Commuter 66 66

Transport-Type 101 101

Total 311 311

Enroute General Aviation 516 438

Corporate 71 32

Commuter 312 156

Transport-Type 547 219

Total 1446 845

25

Figure 19: Sample data input to LATFM (Large Airport Hub and Regional Airport Demand Func-tion).

0

2 0

4 0

6 0

8 0

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Local Hour (hr)

1999

2020

Large Hub

0

2

4

6

8

1 0

1 2

1 4

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4

Local Hour (hr)

1 9 9 92 0 2 0

Regional Airport

26

Figure 20: Traffic flow screen in LATFM.

Figure 21: Aircraft as a sensor screen in LATFM.

27

Figure 22: Aviation weather products screen in LATFM.

Figure 23: Aircraft flight information screen in LATFM.

28

Figure 24: Introductory screen in the LATFM model.

Terminal Area

R=110 km

Critical Slice of Tactical Region

Airport areas

ARTCCor

Tactical RegionAircraft

EnrouteAirspace

Inbound Flow Outbound Flow

29

3 Aeronautical Applications

There future aeronautical applications studied in this project are classified into three domains: 1) applications of weather information to the cockpit, 2) aircraft as a weather sensor, and 3) flight infor-mation services to help air traffic management. The following sections of the report describe in detail some of the applications considered relevant in future NAS operations.

3.1 Weather in the Cockpit Applications

Aviation weather is perhaps the most prominent application in the future of NAS operations. Ac-cording to statistics compiled by the National Transportation Safety Board (NTSB) and the FAA alarge percentage of the accidents in aviation are either Controlled Flight into the Terrain (CFIT) orweather related accidents (FAA, 2001). In the last decade 22.3% of the accidents reported in the U.S.were classified as weather related. Part 91 operators account for 85% of the total accidents reported(4,245 total accidents by Part 91 operators). These statistics are compelling to suggest that betterweather information systems in the cockpit could benefit the entire aviation community, and especial-ly those operators that lack on-board weather radars and other weather detection systems (i.e., storm-scopes, etc.). The impact of timely weather information to the cockpit can improve pilot situationalawareness and warn pilots of dangerous phenomena both during terminal area and enroute airspaceoperations. According to NTSB records 33% of the weather related accidents are related to low visi-bility and ceiling conditions at the airport. The remaining 67% of the remaining weather-related ac-cidents involve enroute operations (FAA, 2001). It is clear that timely weather information to thecockpit, coupled with more accurate weather forecasting models should enhance the safety of the avi-ation system at all levels.

In our analysis we first derive aviation weather data communications needs for a single aircraft.Extensions based on predicted aircraft flows at airport, terminal areas and enroute airspace are thenmade to derive communication requirements for large regions of interest in a dynamic traffic flow en-vironment. Both ground and airborne weather gathering sources are considered in this problem.

Today, the primary source for ground-based weather component is advanced Doppler radar(NEXRAD). There are 147 Doppler radar stations across NAS to facilitate data collection of severalweather data products ranging from base reflectivity to storm total precipitation. Doppler radars pro-vide accurate information on weather phenomena at ranges of up to 400 km. from the radar antenna(Mahapatra, 1999). Doppler radar update rates vary according to the data products derived but underideal conditions total scans of 3-4 minutes are the best sampling rates possible with the current state-of-the-art radars such as the WSR-88D (NCDC, 1988). To derive weather data communication re-quirements in the future it is necessary to assume an advanced form of Doppler radar system infor-mation available to pilots. Our analysis considers that in twenty years time a technical multiplier of3-4 will be achieved in sampling rates available (equating to a 60 second sampling rate across variousweather products). Moreover, the minimum cell resolution of advanced Doppler radar applications as-sumed in this analysis is expected to be down to 1 microsecond of radar pulse width. These technicalparameters are needed to derive future weather applications in the cockpit.

The primary source for airborne-based weather information is the airborne weather radar of eachaircraft in the area of interest. The information gathered by airborne radars is more limited in scope(due to power limitations of the radar) and the physical tilt limitations of the onboard the radar anten-na. Nevertheless, weather information gathered by onboard sensors could play a significant role to im-

30

prove the accuracy of the weather picture of a region if fusion algorithms are developed to synthesizeonboard and ground information weather data. The use of onboard weather information by pilots to-day is limited to the near-term strategic scenario (discussed in Section 1) given the power limitationsand cell resolution of onboard weather radars.

Aviation Weather Resolution Levels

The flight planning decision making process employs weather information of various degrees ofresolution. These resolution levels are necessary because a pilot planning detours around a largeweather front 600 km. away can afford to inspect the macroscopic trend of the weather pattern withmoderate resolution. The same pilot performing storm cell avoidance maneuvers in the terminal areawould require a finer detail in the weather picture to help in the decision making process. In thisproject we define three boundaries for pilot decision making: 1)

tactical

(20 minutes ahead), 2)

nearstrategic

(20-60 minutes ahead), and 3)

far strategic

(>60 minutes ahead). These boundaries are de-fined in terms of time to avoid complexity when dealing with dissimilar performance aircraft. Timevariations allow pilots to make better weather avoidance decisions throughout a typical flight (Figure25).

Figure 25: Decision domains for airborne weather advisory systems.

Tactical

NearStrategic

FarStrategic30-40

Degrees

30-40Degrees

Destination

Tactical

NearStrategic

FarStrategic

30-40Degrees

Destination

900 km.

300 km.

Variable

Resolutionchanges acrossvariousdecision makingboundaries

60 min.

20 min.

31

In the current and future NAS, the resolution of aviation weather services improves as each flighttransitions from ARTCC to the airport area due to the physical resolution of the weather sensors (i.e.,Doppler radar, Low Level Wind Shear, etc.) installed near or at the airports. Figure 26 illustrates theFAA requirements for weather radar coverage in NAS (FAA, 1981). The figure includes the resolutionrequired for various ATC domains. The hypothesis in this study is that these levels of weather radarresolution would be surpassed by a

factor of three

in the horizon year of our analysis (i.e., 2020).

Figure 26: Spatial resolution of ground-based radars (Mahapatra, 1999).

The resolution of the weather information provided to the cockpit today and in future applicationschanges as a function of time and space over NAS for three fundamental reasons: 1) ground-basedDoppler radar information has uneven volume resolutions as the target moves away from the radarantenna, 2) the airborne sensor information (i.e., weather radar and other sensors) are limited in scopedue to power limitations of the sensor, 3) the distribution of both ground and airborne sensors is noteven across NAS. Fortunately, the most advanced weather sensors are usually located near large air-ports thus contributing to better weather volume resolutions in the critical phases of flight (landingand takeoff).

Table 7 presents the most useful Doppler radar products used by pilots in flight planning. Theseproducts are available today at all National Weather Service stations across NAS via PLan PositionIndicator displays (PPI). Figures 27 through 31 illustrate graphically some of these products in theWeatherTap

TM

interface available through the internet (WeatherTap, 2000). The basic assumption inderiving weather applications in this project is the availability of these products in the future to thecockpit with higher resolutions (

3 times the resolution

available today using WSR-88D radars.

21,300 m

1,800 m

3.05 kmresolution

1.00 kmresolution

365 mresolution

20 km

6,100 m

150 m

3,050 m

54 km

EnrouteArea

TerminalArea

Airport

EnrouteArea

Center of Airport Complex

1,800 m

21,300 m

6,100 m

32

Table 7: Summary of weather data products available from ground-based Doppler radars.

Product Number

Product Identifier Product Description

19 R Base reflectivity - 230 km. range (4 elevation angles)

20 R Base reflectivity - 460 km. range (lowest elev. angle)

38 CR Composite reflectivity - 16 levels (precipitation and clear air modes)

65,66,90 LRM Layer composite reflectivity (three layers)

41 ET Echo tops

57 VIL Vertical integrated liquid

27 V Base radial velocity (lowest 4 elevation angles)

56 SRM Storm relative mean radial velocity (2 elev. angles)

48 VWP Velocity azimuth display (VAD) winds

78 OHD Surface rainfall accumulation - one hour total

80 STP Surface rainfall accumulation - storm total

81 DPA Hourly digital rainfall array

33

Figure 27: Base reflectivity weather data product available today (WeatherTap, 2000).

34

Figure 28:Winds at altitude weather data product available today (WeatherTap, 2000).

35

Figure 29:Echo tops weather data product available today (WeatherTap, 2000).

36

Figure 30:Base velocity weather data product available today (WeatherTap, 2000).

37

Figure 31:Vertical velocity weather data product available today (WeatherTap, 2000).

Communication Requirements Analysis

The following steps are necessary to derive realistic communication requirements associated withaviation applications: 1) derive a concept of operations (include interactions between aircraft, ATCand weather information services); 2) define the type of weather information derived from ground andairborne sensors. This will include assessment of data structures of weather information and samplingrates used in the collection of the weather data and the distribution of it to airborne users; and 3) de-termine possible communication modes of operation (segregated channel vs. broadcast mode, etc.).Once this last step is concluded the derivation of communication requirements can be executed if dy-namic traffic flows across the region of interest are known (i.e., through the use of dynamic traffic flowmodels such as LATFM).

The estimation of communication data requirements to bring advanced weather to the cockpit isexecuted for two types of aircraft: 1) those having an onboard weather detection equipment, and 2)those without it. As aircraft move in their flight paths, they traverse various ground and airborneweather sensor sites resulting in a non-uniform distribution of the resolution volumes gathered fromground sensors. This is illustrated in Figure 32. The first-order analysis performed in this project pro-

38

vides a static snapshot of the worst possible requirements for weather in cockpit applications.

Figure 32: Composition of weather information.

Data Analysis of Weather Information

A first-order analysis of the data size requirements to bring high quality weather information to thecockpit is illustrated in Figure 33. In the tactical domain weather information is assumed to be theresolution cells of the ground sensor. A worst case scenario (from a data size viewpoint) occurs whenthe aircraft flies near the ground sensor as the cell resolution of the ground sensor is highest. Using30 degrees in elevation and 60 degrees of azimuth coverage provided by ground sensors spaced everydegree requires 4.8 million bytes of information for a complete tactical region coverage.

In the near-strategic domain weather information is assumed to be the resolution cells of theground sensors available in the flight track (no collaborative weather information is assumed for air-borne sensors). The desired range of weather information precludes the use of onboard sensors. A to-tal of 28 million bytes of information will be required for a complete weather picture. In the analysiswe have assumed the resolution of information to be the same as that of the tactical boundary. In thefar-strategic domain weather information is assumed to be the resolution cells of the ground sensorsavailable in the flight track with some loss in the quality of the image provided (5 pixels into 1).

Techniques to reduce communication bandwidth requirements are data compression techniquesand a data validation and adaptive filtering algorithms to refresh weather cell elements that changefrom successive observations. It has been estimated that up to 25% of the weather information contentcould change between successive radar samples (using 60 second refresh rate) for a high-speed sub-sonic aircraft in the cruise mode. Anecdotal information from the AWIN program shows that tacticalweather information savings are substantial using these two techniques. In one case the data filteringalgorithms changed 41 kilobytes of a complete weather display (several Mbytes of information at 8bit resolution).

Time (min)

Time (min)

Weather Volume Resolution

Weather Volume Resolution

Aircraft 1

Aircraft 2

FlightTrack

Radar 1

Radar 2

Radar 3

Aircraft 1

Aircraft 2

39

Figure 33: Weather information analysis for various decision-making flight regions.

Aircraft 1

Assumed resolution = 2 bytes per pixelRadar range = 200 km (maximum)Training angles = +- 30 azimuth, +- 10 elevation

Total weather cell info / cycle = 4.8 E 6 bytes

Pulse width = 1

µ

s

Tactical Boundary Speed = 450 knots

Aircraft 1

Speed = 450 knots

Total weather cell info / cycle = 28 E 6 bytes (worst case) = 22 E 6 bytes (typical) 900 km

Note: assumes allradial cell info isavailable (150 mresolution)

Aircraft 1

Speed = 450 knots

Total weather cell info / cycle = 5.0 E 6 bytes (worst case) = 4.0 E 6 bytes (typical)

>> 900 km

Note: assumes allradial cell info isfuzed at a factorof 5 pixels for 1 (1-2 km radial resolution)

Near-term Strategic Scenario

Far-term Strategic Scenario

Tactical Scenario

40

The refresh rates of weather information are assumed to be consistent with the technology expect-ed to be in place in the horizon year. In our analysis we assumed a technology multiplier that will pro-vide faster sampling rate (down to one minute in 2020 for airport services).The tactical domainmatters the most and has the fastest update cycle (60 seconds). This level of detail over time would besufficient to detect most of the harmful convective weather phenomena in the terminal area. Table 8summarizes the weather information including sampling rates projected for the weather data sets ineach domain. The communication requirements shown in Table 8 apply to individual aircraft.

Table 8: Summary of weather information data to the cockpit.

3.2 Aircraft as Weather Sensor

So far the discussion has been centered around the use of airborne and ground-based weather sen-sors to detect two types of weather services: 1) convective weather (from doppler and airborne radarsources, and 2) wind information (collected from tracers detected by doppler radar). Many aircrafthave multitude onboard sensors that, under current conditions, are not exploited to provide informa-tion to others. The availability of aircraft-derived weather information could provide valuable pointmeasurements of weather-related services useful to pilots. Examples of these are: 1) wind data alongthe flight track (derived from FMS, INS or GPS sensors), 2) turbulence levels derived from aircraftaccelerator sensors, 3) convective weather information, and 4) icing information along a route.

In this applications aircraft would provide relevant weather information to a centralized NationalWeather Database System (NWDS)

at predetermined intervals. NWDS would collect these data and

apply tactical weather models and algorithms to complement ground-based products derived fromlong-range Doppler radar, terminal doppler radar and other ground sensors. The information derivedfrom NWDS system is sent to pilots (via satellite) at predetermined intervals (see Figure 34). Themain advantage of this concept is that many of the current voids in weather information would beclosed with the presence of aircraft at various flight levels (Figure 35).

Given the randomness of flight tracks across NAS the number of data points and the time and spa-tial distribution of the information will vary substantially. The NWDS will fuse all data collected andprovide a common format to all requesting aircraft, The data format assumed for this analysis is a rect-angular grid provided to all aircraft. Table 9 contains a description of the aircraft as weather sensordata products prototyped in this model. Table 10 summarizes the data size requirements to make air-

DomainTotal Weather Data Size (bytes)

Region Size

a

(km

2

)

a. For a typical transport-type aircraft traveling at 900 km/hr. (460 knots).

Data (bytes per sq. km)

Sampling Rate (seconds)

Tactical 4.8 E 6 4.71 E 4 101.85 60

Near-term Stra-tegic

22 E 6 4.24 E 5 51.86 180

Far-term Strate-gic

4.0 E 6 2.700 E 6 1.48 600

41

craft be collaborative sensors across NAS. The sampling rate assumed in this analysis is 60 seconds.

Figure 34: Aircraft as a sensor diagram.

Table 9: Aircraft as a weather sensor communication information data sets.

Service Type Sources of Data Sampling Rate Data Size

Winds Aloftalong the flight track

Air data, FMS, GPS derived wind data

Continuous variables reported (direction, magnitude, location,

time)every 10 seconds

for all aircraft types

512 bits

a

per aircraft per measurement

aircraft IDwind direction

wind magnitude3D location

time tag

a. Assumes no compression

Icing

Vehicle icing sensors

and on-board tempera-ture gradient measure-

ments

every 10 seconds(all types of aircraft)

reported as

64 bits per aircraft per measurement

Turbulence

Vehicle accelerome-ters, mechanically mea-

sured

every 10 seconds(all types of aircraft)

reported as

64 bits per aircraft per measurement

Convective

Moisture content instruments, Pressure,

etc.

every 10 seconds(all types of aircraft)

reported as

128 bits per aircraft per measurement

NWDS

RadarEnvelope

NEXRAD

Terminal

Radar SiteNEXRAD

Site

Satellite Satellite

Doppler

To NWDS

To NWDS

From NWDS

42

Figure 35: Spatial retrieval information for aircraft as a weather sensor.

Table 10: Aircraft as a sensor data size with low sampling rate.

DomainTotal Weather Data Size (bytes)

Region Size (km

2

)

Average Data Set (bytes

a

per sq. km)

a. Assumes 8 bit data representation, 8 bit error correction scheme and 1 km grid.

Sampling Rate (seconds)

Tactical 1.8 x 10

5

rectan-gular9.0 x 10

4

triangu-lar

9.0 x 10

4

rectan-gular4.5 x 10

4

triangu-lar

2.0 in bothcases

60

Near Strategic

1.44 x 10

6

rect-angular7.2 x 10

5

triangu-lar

7.2 x 10

5

rectan-gular3.6 x 10

5

triangu-lar

2.0 in bothcases

180

Far Strategic

2.48 x 10 5 rect-angular(4 km grid size)

up to 2.7 x 10 6 rectangular

0.19 600

FL270AA052

ReportingPoints

FL370US170

FL330DA183

10 s or 2.5 km

43

Aircraft in flight will probably require several of these data products on a continuous basis. Themost demanding situation will be for all aircraft requesting simultaneous services for all data sets.This will imply for example 8 bytes/km

2

(4 weather products times 2 bytes/km

2

) in the tactical region

of analysis. In the analysis using the LATFM model described in Section 2 we assumed Peak DemandFactors (PDF) of 0.4, 0.30 and 0.25 for every ATC centered service (airport, terminal and enroute ser-vices, respectively) to obtain peak demand loads for the most critical region. A sample output fromLATFM is shown in Figure 36. Peak demand factors represent an accumulation of traffic density incongested regions of airspace. PDF is the ratio of aircraft traffic in congested airspace and the aircrafttraffic in all the region of interest.

Figure 36: Aircraft as weather sensor results shown in the LATFM model.

One of the advantages of using a dynamic model to predict communication loads is its ability toadapt to a multitude of design conditions. Table 10 shows the resulting data sets associated withweather products derived using NWDS with aircraft sampling rates once every second (grid size is0.1 km). The matrix below represents the data sets for each product per aircraft. It is obvious that amultiplicative growth results in the amount of information generated by unit area.

10:53 PM Thu, Dec 28, 2000

0.00 360.00 720.00 1080.00 1440.00

Time

1 :

1 :

1 :

2 :

2 :

2 :

3 :

3 :

3 :

1.30

20.65

40.00

0.00

20.00

40.00

1.30

20.65

40.00

1: Total Aircraft as WX Sensor 2: Peak TERM Sensor Load 3: Peak ENR Sensor Load

1

1

1

1

2

2 223

3 3

3

Graph 4: p4 (Untitled) Time in minutes

Wea

ther

Inf

orm

atio

n (B

its

per

min

ute/

km

2 )

Peak Loadat 34 Bits/min

per km2Total Load

Enroute Wx Load

Terminal Area Wx Load

44

Table 11: Aircraft as a sensor data size with high sampling rate.

DomainTotal Weather Data Size (bytes)

Region Size (km

2

)

Average Data Set (bytes

a

per sq. km)

a. Assumes 8 bit data representation and 8 bit error correction scheme and

0.10 km grid size

.

Sampling Rate (seconds)

Tactical 1.8 x 10

7

rectan-gular9.0 x 10

6

trian-gular

9.0 x 10

4

rectan-gular4.5 x 10

4

trian-gular

200 in bothcases

60

Near Strategic

1.44 x 10

8

rect-angular7.2 x 10

7

trian-gular

7.2 x 10

5

rectan-gular3.6 x 10

5

trian-gular

200 in bothcases

180

Far Strategic

3.96 x 10

6

rect-angular(1km grid size)

up to 2.7 x 10

6

rectangular

3.14 600

45

3.3 Flight and Traffic Information Service Applications

Flight information services constitute another important area where satellite communications are

changing the nature of aviation operations. Examples of current Traffic Information Services (TIS)are: 1) TCAS - the Traffic Collision Avoidance System, and 2) ADS-B systems based on air-to-groundVHF data link (using either satellite or land datalinks).

Future Air Traffic Management systems are likely to implement advanced forms of CollaborativeRouting (CR). Under CR the premise is for flights to take both tactical and strategic actions on theground or in flight to minimize conflicts with others and increase the efficiency of operations (i.e.,reduce delays). Collaborative routing strategies might be directed by a centralized facility (i.e., an air-line operations center, or air traffic control) or by decentralization of ATC/ATM services to neighbor-ing flights. Under CR flights have primary flight plans filed before departure. As each flight progressesa number of events in NAS can change the character of the best strategy to conduct each flight. At thetactical level each flight will alter their course to avoid others (TCAS type service). In the context ofairline services, the airline operations center has no role in this resolution strategy. Aircraft requirereal-time position information, real-time conflict detour strategies considering

n

flights and

m

possi-ble flight plan alternatives.

In the near-term and far-term strategic domains each flight will probe conflicts and sector capacityboundaries to plan a system-optimal detour strategy. Figure 37 illustrates an example of system-opti-mal detours around a Special Use Airspace region in Florida. In this instance plan-ahead traffic infor-mation solution in the near-term strategic boundary. The character of the messages requires real-timeinformation but have less priority that the previous case. Enroute and terminal ATC sectors andboundaries are modeled in moderate detail (a single lumped queueing model is enough).The role ofthe airline operations center is critical in the decision making process. Failure to plan a detour strategyconsidering other traffic could result in longer delays at critical points in the airspace system.

Air Traffic Management Concept of Operations (Circa 2020)

Our assumptions for a concept of operations in 2020 ADS-B surveillance information is widelyavailable to all airspace users. LEO communications are used in two ways: 1) as backup system forADS-B (critical element of ground surveillance), and 2) as service provider of traffic information ser-vices - collaborative routing and self-separation information - to aircraft in the airspace (in all phasesof flight). Aircraft communications messages can, once again, be catalogued into three distinctgroups:1) tactical decision making information (typically flight critical), 2) near-term strategic infor-mation (aircraft to AOC for example), and 3) far-term strategic information (for long-range strategicplanning).

Under this concept of operations aircraft constitute a distributed network of intelligent agents (Fig-ure 38). Distributed control algorithms devise aircraft maneuvers (advanced real-time tactical andstrategic flight plan functionality). Other services exist (TCAS) for last-minute conflict resolutionstrategies. For initial conceptualization we assume that most of the ATM system-wide optimizationcomputations occur at a ground facility (called ATM Center - part of ARTCC or TRACON) wherefaster CPUs exist. In advanced version of this system aircraft could collaborate in the computationaltasks if they have idle CPU resources available.

In order to understand the communication loads required with advanced ATM applications foreach one of the areas of interest we use a prototype flight plan shown in Figure 39. This flight planstructure contains information typically stored in the current FAA ETMS system.

46

Figure 37: Sample collaborative routing around a warning area (SUA).

Figure 38: Traffic information services.

SUA

Several detourstrategies need to bestudied whileperformingsystem optimalcollaborativeroutingstrategies. Herewe show detoursaround SpecialUse Airspace(SUA)

ATM Service

Satellite Satellite

To ATM

To ATM

From ATM

Air-AirAir-Ground

Other Air-Air(TCAS, ADS-B)

47

Figure 39: Prototype flight plan data structure.

The average flight plan in ETMS today has 34 waypoints of information. This represents an aver-age of 3,350 bytes of information per flight (using double precision data types for most of the numer-ical information). The modeling assumption is that in the future NAS most of the flights will operateunder an equivalent IFR flight plan for safety reasons. In the tactical domain

n+4

waypoint informa-tion is used to probe and schedule detours. In the near-term strategic domain we assume

n+10

way-points used in the elaboration of surrogate flight plans for optimal detour strategies. An initial estimateof the communication requirements in a future NAS requires an estimate of the number of messagesproduced by every agent in the system. An assumption made here is that the number of messages willbe correlated to the number of conflicts that are likely to occur in the system.

A study by Trani et al.(1998) using 6000 random flights in ZMA and ZJX under current NAS sys-tem conditions reported that about 4.7% of the flights in the enroute airspace are in conflict (9.26 km.shell). Under Reduced Vertical Separation Mode (RVSM) conditions the number of blind conflicts isexpected to decrease to 3.1% and the geometries of the conflicts will change moderately. Other stud-ies confirm that random airspace conflicts grow

quadratically with the number of flights. Figure 40illustrates possible ways to study airspace conflicts including a notional conflict rate diagram.

System-wide optimal airspace models would employ this information to generate flight planningadvisories to every flight. A sample Airspace Planning Model is reported by Sherali et al. (2000). Traf-fic information plans (solutions of the APM model) are transmitted from the ATM Center to each air-craft at predetermined intervals. These intervals are dictated by the quickness of the APM solver.Tables 12 and 13 illustrate the typical sampling rates and data sizes of the various TIS messages as-sumed in this analysis. Figure 41 illustrates a sample screen in the LATFM model showing dynamiccommunication requirements for the region of interest modeled.

fname: 'AAL1________00_0'fmodel: 'B767'origin: 'JFK'dest: 'LAX'n: 50wp: [50x3 double]twp: [50x1 double]omodule: 222start_point: [-73.7830 40.6330 0]start_time: 866start_seg: 1start_lam: 0path: [ 1x5 struct]main_path: [ 1x4 struct]

Aircraft IDAircraft Model

Origin-Destination Info

Waypoint Information

Starting Leg Information

Total Data Set = 3708 bytes

(using double precision for all numeric data)

n+4 Waypoint/Sector Info

48

Figure 40: Estimating airspace conflicts (stochastic and deterministic flight tracks).

Stochastic Track

Deterministic Track

TypicalEnvelopefor FISTIS Services

Waypoints

FL370US170

FL370DA183

Future TrackUncertainty

Envelopes

ProximityShell

49

Table 12: First-order TIS surveillance information.

Table 13: First-order TIS information (ATM mode).

Domain Message Size (bytes) Typical Message Update Rate(messages per minute)

Tactical 240 30

Near Strategic 480 5

Far Strategic 512 0.5

Domain Message Size (bytes) Update Rate

a

(messages /minute)

a. Represents how fast the decision-making Airspace Planning Optimization model can produce a new system-wide

flight plan solution.

Tactical 512 1

Near Strategic 1024 1/2

Far Strategic 4700 1/6

50

Figure 41: Aircraft flight information screen in LATFM.

51

4 Conclusions and Recommendations

This report has presented various modeling strategies for evaluating future aeronautical applica-tions that could benefit from the deployment of Low Earth Orbit satellites. The project had two inter-related goals. The first one was to assess the future aeronautical communication applications. Ourwork developed a traffic scenario for the year 2020 using the Lumped Air Traffic Flow Model (LAT-FM) and then estimated associated bandwidth requirements for advanced weather applications. In-cluded is a model in which aircraft act as sensors and feed back information to a centralized grounddatabase system, which are fused by a weather model. Another application uses ground radar infor-mation to deliver detailed weather products to the cockpit. A final application studied possible uses ofLEO satellites to support an advanced Air Traffic Management (ATM) system where Traffic Informa-tion is made available to all airborne platforms in NAS.

The second goal involved the investigation of the use of next generation satellite technology tosupport these advanced applications.We developed a model that supports the comparison of broad-cast, uni-cast and hybrid broadcast/uni-cast satellite systems for the delivery of weather and other in-formation to aircraft. This model allows for the estimation of the bandwidth requirements for futureaeronautical communications applications. It also is supports the comparison, based on bandwidth re-quirements and processing cost, of broadcast or hybrid LEO/MEO architectures, broadcast or hybridGEO architectures and general unit-cast architectures.

In the derivation of communication requirements for each one of the three applications presentedin this report required some fundamental assumptions regarding the technology available and thenumber of operations in the year 2020. Some of these assumptions are:

• The growth rate of the aircraft fleet and aircraft operations in NAS follows the FAA predicted pattern until the year 2010. A 1.75% growth in aircraft operations per year is assumed thereafter.

• The equipage rate of aircraft having onboard weather sensors is assumed to be 100%.

• The equipage rate of aircraft having advanced weather displays is 100% across all four classes of aircraft discussed in the report.

• The number of aircraft having access to traffic information services is also assumed to be 100%.

These assumptions are based on the premise that in a steady-state scenario all aircraft flying inNAS will require some form of weather information in the cockpit, act as a weather sensor, and re-quire traffic information services in order to conduct safe operations across NAS. The authors believethat this worst-case scenario assumptions should provide a best example of the cumulative effect oflarge numbers of airborne platforms requesting various services.

A flexible computer simulation model (called Lumped Air Traffic Flow Model - LATFM)has been developed and tested using data from an Air Route Traffic Control Center. This mod-el can be employed to study the impact of future aeronautical applications as they affect com-munication data requirements.

The model tracks the aggregate number of aircraft over time (TrafficFlow Module) and computes communication data size requirements for various aeronautical servicessuch as weather products in the cockpit (Aviation Weather Module), aircraft as weather sensor (Air-craft as Weather Sensor Module), and flight information services (Flight Information Services Mod-

52

ule). The development of this prototype model has been carried out using STELLA II - a SystemsDynamics software package developed by High Performance Systems of Nashua, New Hampshire.The model can be easily adapted to study the effects of variable aircraft demands inside an ARTCCon communication data requirements.

Some of the conclusions of the broadcast trade-off model developed are:

• The optimal area that leads to a minimum total bandwidth and processing costs is relatively independent from the air traffic density. This result enables a single static solution that can be applied throughout the country.

• Future aeronautical applications would require high bandwidth communication links. This supports the use of next generation satellite systems as an alternative to current terrestrial systems.

Conclusions of the traffic flow model are:

• The growth in the demand function expected in the next two decades assumed in this project produced estimates of

up to 1450 operations per hour (including air car-rier, corporate jets and GA users) in congested Air Route Air Traffic Control (ARTCC) centers such as Indianapolis and New York. When these operations are viewed as instan-taneous operations produce 800-850 aircraft requesting communication services inside an ARTCC area.

Future recommendations of this work are:

• Improvements to the broadcast model could be made to study hybrid terrestrial and satellite networks.

• Improvements to the integration between the broadcast and the air traffic models. This integration could take the form of better input-output file parser develop-ment. The benefits of such integration would be the added flexibility to run mul-tiple variations of communication scenarios faster.

• Improvements to the traffic flow modeling scheme used in LATFM could incorpo-rate feedbacks between airport resources and traffic dwell times to better charac-terize delays at congested airports and their impact on communication requirements.

• Improvements to the traffic flow model to accommodate other aeronautical appli-cations such as Airline Operations Center messages, ground-based flight data recorder style applications, etc. These would present a more complete picture of the overall communication requirements across dense regions of NAS

• Adaptation of the traffic model to hybrid aircraft equipage. This will provide a better assessment of the evolution of NAS communication requirements over time.

• Adaptation of the existing LATFM model to account for a larger growth in aircraft operations consistent with the deployment of SATS type aircraft technologies. These aircraft would require better airborne services and perhaps drive the band-width requirement of the LEO satellite network in the future. The current

53

LATFM model has a SATS switch to represent a probable growth in aircraft operations in the future. However, the current representation is very crude and should be studied in more detail if the results are to be considered reliable. At this point the CNS requirements of SATS are still sketchy to understand their full implication on future CNS NAS requirements.

54

Bibliography

[1] A.Jamalipour, Low Earth Orbital Satellites for Personal Communications Networks, Arthech House, Inc., 1998.

[2] C. O. McCaw, E. F. Tuck and W. Russell Daggatt, Application of Teledesic Corporation for a Low Earth Orbit Satellite System in the Fixed Satellite Service, FCC, March 21, 1994.

[3] M. A. Sturza, “Architecture of the Teledesic Satellite System,” Proceedings of International Mo-bile Satellite Conference, pp. 212-218, 1995.

[4] O. Ercetin, S. Krishnamurthy, S. K. Dao and L.Tassiulas, “A Predictive QoS Routing Scheme for Broadband Low Earth Orbit Satellite Networks,” Personal, Indoor and Mobile Radio Commu-nications, London, UK, 2000.

[5] B. A. Akyol and D. C. Cox, “Rerouting for Handoff in a Wireless ATM Networks,” IEEE Personal Communications, 3(5): 26-33, Oct. 1996.

[6] E. Lutz, M. Werner and A. Jahn, Satellite Systems for Personal and Broadband Communications, Springer Verlag, 2000.

[7] Federal Aviation Administration, Aviation System Capacity Enhancement Plan, FAA Office of System Capacity, Washington, D.C., 2001.

[8] P. Mahapatra, Aviation Weather Surveillance Systems: Advanced Radar and Surface Sensors for Flight Safety and Air Traffic Management, Progress in Astronautics and Aeronautics, AIAA, Washington, D.C. 1999.

[9] P. Mahapatra, R. J. Doviak, and D. S. Zrnic, “Aviation Weather Surveillance Systems : Advanced Radar and Surface Sensors for Flight Safety and Air Traffic Management,” IEEE Radar Series, Volume 8, 1998.

[10] Federal Aviation Administration, Consolidated Operational Delay Database, Web site at http://www.apo.data.faa.gov/.

[11] Federal Aviation Administration, Office of System Safety, Web site http://nasdac.faa.gov/safety_analysis/weather_study/parttb.HTM.

[12] A.A. Trani, H.D. Sherali, C. Smith and S. Srinivas, “Models to Support Collision Risk Modeling in NAS,” National Center of Excellence for Aviation Operations Research (NEXTOR) Report RR-98-12, 1998.

55

Appendix A. LATFM Stella II Source Code.

The following equations constitute the basis for the Lumped Air Traffic Flow Model. LATFM has been prototyped using STELLA II (a trademark software of High Performance Systems in Nashua, New Hampshire). The model representation of eight state variables implies a solution to eight cou-pled first order differential equations with variable coefficients. The STELLA II software handles the solution to this system of equations automatically.

State Variables

Idle_Aircraft_at_Airport(t) = Idle_Aircraft_at_Airport(t - dt) + (To_Idle_State - Out_of_Idle_State) * dt

INIT Idle_Aircraft_at_Airport = Initial_Aircraft_at_Airports

INFLOWS:

To_Idle_State = NALAIS

OUTFLOWS:

Out_of_Idle_State = Idle_Aircraft_at_Airport/Dwell_Time_at_Airport

Inbound_Aircraft_at_Airports(t) = Inbound_Aircraft_at_Airports(t - dt) + (NALTA1 - NALAIS) * dt

INIT Inbound_Aircraft_at_Airports = 0

INFLOWS:

NALTA1 = NALTA

OUTFLOWS:

NALAIS = Inbound_Aircraft_at_Airports/Inbound_Airport_Dwell_Time

Inbound_Aircraft_in_Enroute_Airspace(t) = Inbound_Aircraft_in_Enroute_Airspace(t - dt) + (NAENO - NALET) * dt

INIT Inbound_Aircraft_in_Enroute_Airspace = 0

INFLOWS:

NAENO = Acft_Demand_Function*Time_Scale_Factor*SATS_Scaling_Factor

OUTFLOWS:

NALET = Inbound_Aircraft_in_Enroute_Airspace/Inbound_Dwell_Time_Enroute

Inbound_Aircraft_in_Terminal_Areas(t) = Inbound_Aircraft_in_Terminal_Areas(t - dt) + (NALET1 - NALTA) * dt

INIT Inbound_Aircraft_in_Terminal_Areas = 0

INFLOWS:

NALET1 = NALET

OUTFLOWS:

NALTA = Inbound_Aircraft_in_Terminal_Areas/INbound_Dwell_Time_Terminal

Outbound_Aircraft_at_Airports(t) = Outbound_Aircraft_at_Airports(t - dt) + (NAEOIS - NALAT) * dt

INIT Outbound_Aircraft_at_Airports = 0

INFLOWS:

56

NAEOIS = Out_of_Idle_State

OUTFLOWS:

NALAT = Outbound_Aircraft_at_Airports/Outbound_Airport_Dwell_Time

Outbound_Aircraft_in_Enroute_Airspace(t) = Outbound_Aircraft_in_Enroute_Airspace(t - dt) + (NALTE1 - NALEO) * dt

INIT Outbound_Aircraft_in_Enroute_Airspace = 0

INFLOWS:

NALTE1 = NALTE

OUTFLOWS:

NALEO = Outbound_Aircraft_in_Enroute_Airspace/Outbound_Dwell_Time_Enroute

Outbound_Aircraft_in_Terminal_Areas(t) = Outbound_Aircraft_in_Terminal_Areas(t - dt) + (NALAT1 - NALTE) * dt

INIT Outbound_Aircraft_in_Terminal_Areas = 0

INFLOWS:

NALAT1 = NALAT

OUTFLOWS:

NALTE = Outbound_Aircraft_in_Terminal_Areas/Outbound_Dwell_Time_Terminal

Transient_Aircraft_in_Enroute_Airspace(t) = Transient_Aircraft_in_Enroute_Airspace(t - dt) + (NAERC_in -NAERC_out) * dt

INIT Transient_Aircraft_in_Enroute_Airspace = 50

INFLOWS:

NAERC_in = Scaling_Factor_for_Enroute_Flights*NAENO*SATS_Scaling_Factor

OUTFLOWS:

NAERC_out = Transient_Aircraft_in_Enroute_Airspace/TD_ENER_cr

Rate and Auxiliary Variables

Aircraft_in_Airport_Areas = Inbound_Aircraft_at_Airports+Outbound_Aircraft_at_Airports

Aircraft_in_Enroute_Region =Inbound_Aircraft_in_Enroute_Airspace+Outbound_Aircraft_in_Enroute_Airspace+Transient_Aircraft_in_Enroute_Airspace

Aircraft_in_Terminal_Areas = Inbound_Aircraft_in_Terminal_Areas+Outbound_Aircraft_in_Terminal_Areas

Aircraft_Wx_Sampling_Rate = 60

Airport_Area = 314

Airport_Area_Peak_Demand_Factor = .45

Antenna_Beam_Width = 1

Average_Airport_Density = Aircraft_in_Airport_Areas/Total_Airport_Areas

Average_ENR_Density = Aircraft_in_Enroute_Region/Total_ARTCC_Area

Average_TERM_Density = Aircraft_in_Terminal_Areas/Total_Terminal__Areas_Footprint

Bytes_in_Far_Strategic_Surveillance_Mode = 1040

Bytes_in_Near_Strategic_Surveillance_Mode = 480

Bytes_in_Tactical_Surveillance_Mode = 240

57

Bytes_perTerminal_Conflict_Message = 512

Bytes_per_Area_FST = Total_Bytes_FST_Service/Far_Strategic_Area

Bytes_per_Area_NST = Total_Bytes_NST_Service/Near_Strategic_Area

Bytes_per_Area_TAC = Total_Bytes_TAC_Service/Tactical_Area

Bytes_per_ENR_Conflict_Message = 1048

Convective_Weather_Information = 128/8

Convective_Wx_Sampling_rate = Aircraft_Wx_Sampling_Rate

Elevation_Multiplier = Radar_Elevation_Coverage/Antenna_Beam_Width

Enroute_Area = 168000

Enroute_Conflict_Message_Rate = 1

Enroute_Peak_Demand_Factor = .25

Exchange_Messages_Enroute = Enroute_Conflict_Message_Rate*No_of_Enroute_Conflicts

Exchange_Messages_terminal = No_of_Terminal_Conflicts*Terminal_Conflict_Message_Rate

Far_Strategic_Area = 1.2e6

Far_Strategic_Surveillance_Update = .5

Icing_Information = 16

Icing_Sampling_Rate = Aircraft_Wx_Sampling_Rate

Inbound_Airport_Dwell_Time = NORMAL(Mean_Inbound_Airport_Dwell_Time,3)

Inbound_Dwell_Time_Enroute = NORMAL(Mean_Inbound_Enroute_Dwell_Time,5)

INbound_Dwell_Time_Terminal = NORMAL(Mean_Inbound_Terminal_Dwell_Time,5)

Initial_Aircraft_at_Airports = 1200

Mean_Inbound_Airport_Dwell_Time = 15

Mean_Inbound_Enroute_Dwell_Time = 45

Mean_Inbound_Terminal_Dwell_Time = 34

Near_Strategic_Area = 7.2e5

Near_Strategic_Surveillance_Update = 5

No_of_Airports = 24

No_of_ARTCCs = 1

No_of_Bytes_per_Cell = 2

No_of_Enroute_Conflicts = Aircraft_in_Enroute_Region*Percent_of_Aircraft_in_Conflict/100

No_of_Terminal_Areas = 8

No_of_Terminal_Conflicts = Aircraft_in_Terminal_Areas*Percent_of_Aircraft_in_Conflict/100

Outbound_Airport_Dwell_Time = 12

Outbound_Dwell_Time_Enroute = 40

Outbound_Dwell_Time_Terminal = 18

Peak_Airport_Density = Aircraft_in_Airport_Areas*Airport_Area_Peak_Demand_Factor/Airport_Area

Peak_Convective_Downlink_ENR = Enroute_Peak_Demand_Factor*Total_Convective_Wx_ENR/Terminal_Area

Peak_Convective_Downlink_TERM = Terminal_Area_Peak_Demand_Factor*Total_Convective_Wx_TERM/Terminal_Area

Peak_Enroute_Density = Enroute_Peak_Demand_Factor*Aircraft_in_Enroute_Region/Terminal_Area

Peak_ENR_Sensor_Load =Peak_Convective_Downlink_ENR+Peak_Icing_Downlink_ENR+Peak_Turbulence_Downlink_ENR+Peak_Winds_Downlink_ENR

Peak_Icing_Downlink_ENR = Enroute_Peak_Demand_Factor*Total_Icing_Info_ENR/Terminal_Area

58

Peak_Icing_Downlink_TERM = Terminal_Area_Peak_Demand_Factor*Toal_Icing_Info_TERM/Terminal_Area

Peak_Terminal_Density = Aircraft_in_Terminal_Areas*Terminal_Area_Peak_Demand_Factor/Terminal_Area

Peak_TERM_Sensor_Load =Peak_Convective_Downlink_TERM+Peak_Icing_Downlink_TERM+Peak_Turbulence_Downlink_TERM+Peak_Winds_Downlink_TERM

Peak_Turbulence_Downlink_ENR = Enroute_Peak_Demand_Factor*Total_Turbulence_Info_ENR/Terminal_Area

Peak_Turbulence_Downlink_TERM = Terminal_Area_Peak_Demand_Factor*Total_Turbulence_Info_TERM/Terminal_Area

Peak_Winds_Downlink_ENR = Total_Winds_Aloft_ENR*Enroute_Peak_Demand_Factor/Terminal_Area

Peak_Winds_Downlink_TERM = Terminal_Area_Peak_Demand_Factor*Total_Winds_Aloft_TERM/Terminal_Area

Percent_of_Aircraft_in_Conflict = Conflict_Percentage*Traffic_Multiplier

Proximity_Shell = 10

Pulse_Width = 1e-6

Radar_Angular_Resolution = 1

Radar_Azimuth_Coverage = 60

Radar_Elevation_Coverage = 30

Radar_Range = 450

Radial_Distance_per_Pulse = Speed_of_Light*Pulse_Width/2

Sampling_Rate_of_Aircraft_Sensor =MAX(Convective_Wx_Sampling_rate,Icing_Sampling_Rate,Turbulence_Sampling_Rate,Winds_Aloft_Sampling_Rate)

SATS_Scaling_Factor = if SATS_Switch = 1 then

1.60

else

1.0

SATS_Switch = 1

Scaling_Factor_for_Enroute_Flights = 1.9

Speed_of_Light = 3e5

Tactical_Area = 9e4

Tactical_Surveillance_Update = 30

TD_ENER_cr = 40

Terminal_Area = 38000

Terminal_Area_Peak_Demand_Factor = .30

Terminal_Conflict_Message_Rate = 3

Toal_Icing_Info_TERM = Icing_Sampling_Rate*Aircraft_in_Terminal_Areas*Icing_Information

Total_Aircraft_as_WX_Sensor_Load =Peak_Convective_Downlink_ENR+Peak_Convective_Downlink_TERM+Peak_Icing_Downlink_ENR+Peak_Icing_Downlink_TERM+Peak_Turbulence_Downlink_ENR+Peak_Turbulence_Downlink_TERM+Peak_Winds_Downlink_ENR+Peak_Winds_Downlink_TERM

Total_Airport_Areas = No_of_Airports*Airport_Area

Total_ARTCC_Area = Enroute_Area*No_of_ARTCCs

Total_Bytes_for_Enroute_Conflicts = Bytes_per_ENR_Conflict_Message*Exchange_Messages_Enroute

Total_Bytes_for_Terminal_Conflicts = Bytes_perTerminal_Conflict_Message*Exchange_Messages_terminal

Total_Bytes_FST_Service = No_of_Cells_in_FST*No_of_Bytes_per_Cell

Total_Bytes_in_Air_to_Air_Mode = Total_Bytes_for_Enroute_Conflicts+Total_Bytes_for_Terminal_Conflicts

Total_Bytes_in_FS_Surveillance =

59

(Aircraft_in_Airport_Areas+Aircraft_in_Enroute_Region+Aircraft_in_Terminal_Areas)*Bytes_in_Far_Strategic_Surveillance_Mode*Far_Strategic_Surveillance_Update

Total_Bytes_in_NS_Surveillance =(Aircraft_in_Airport_Areas+Aircraft_in_Enroute_Region+Aircraft_in_Terminal_Areas)*Bytes_in_Near_Strategic_Surveillance_Mode*Near_Strategic_Surveillance_Update

Total_Bytes_in_Tactical_Surveillance =(Aircraft_in_Airport_Areas+Aircraft_in_Enroute_Region+Aircraft_in_Terminal_Areas)*Bytes_in_Tactical_Surveillance_Mode*Tactical_Surveillance_Update

Total_Bytes_NST_Service = No_of_Bytes_per_Cell*No_of_Cells_in_NST

Total_Bytes_TAC_Service = No_of_Bytes_per_Cell*No_of_Cells_in_TAC

Total_Convective_Wx_ENR = Convective_Weather_Information*Aircraft_in_Enroute_Region

Total_Convective_Wx_TERM =Convective_Weather_Information*Convective_Wx_Sampling_rate*Aircraft_in_Terminal_Areas

Total_Icing_Info_ENR = Icing_Information*Aircraft_in_Enroute_Region*Icing_Sampling_Rate

Total_Terminal__Areas_Footprint = No_of_Terminal_Areas*Terminal_Area

Total_Turbulence_Info_ENR = Turbulence_Information*Aircraft_in_Enroute_Region*Turbulence_Sampling_Rate

Total_Turbulence_Info_TERM = Aircraft_in_Terminal_Areas*Turbulence_Information*Turbulence_Sampling_Rate

Total_Winds_Aloft_ENR = Aircraft_in_Enroute_Region*Winds_Aloft_Info_per_Acft*Winds_Aloft_Sampling_Rate

Total_Winds_Aloft_TERM = Aircraft_in_Terminal_Areas*Winds_Aloft_Info_per_Acft*Winds_Aloft_Sampling_Rate

Traffic_Multiplier = (Time_Scale_Factor+SATS_Scaling_Factor)^2

Turbulence_Information = 16

Turbulence_Sampling_Rate = Aircraft_Wx_Sampling_Rate

Winds_Aloft_Info_per_Acft = 64

Winds_Aloft_Sampling_Rate = Aircraft_Wx_Sampling_Rate

Year_of_Analysis = 2020

Table Functions

Acft_Demand_Function = GRAPH(time)

(0.00, 1.00), (60.0, 1.00), (120, 1.00), (180, 1.50), (240, 2.40), (300, 3.50), (360, 4.80), (420, 5.40), (480, 4.40), (540, 4.80),(600, 5.20), (660, 5.10), (720, 4.30), (780, 4.40), (840, 4.20), (900, 4.30), (960, 4.60), (1020, 4.20), (1080, 4.60), (1140,3.70), (1200, 3.20), (1260, 2.80), (1320, 3.20), (1380, 2.70), (1440, 1.20)

Conflict_Percentage = GRAPH(Proximity_Shell)

(10.0, 4.00), (15.0, 10.0), (20.0, 22.0), (25.0, 43.0), (30.0, 75.0)

Dwell_Time_at_Airport = GRAPH(time)

(0.00, 300), (60.0, 300), (120, 300), (180, 250), (240, 250), (300, 250), (360, 250), (420, 120), (480, 120), (540, 120), (600,75.0), (660, 60.0), (720, 60.0), (780, 60.0), (840, 60.0), (900, 69.0), (960, 90.0), (1020, 111), (1080, 146), (1140, 200),(1200, 243), (1260, 285), (1320, 700), (1380, 700), (1440, 1000)

Grid_Size_of_Cell_Map = GRAPH(Sampling_Rate_of_Aircraft_Sensor)

(6.00, 1.00), (15.0, 0.85), (24.0, 0.7), (33.0, 0.55), (42.0, 0.4), (51.0, 0.25), (60.0, 0.1)

No_of_Cells_in_FST = GRAPH(Grid_Size_of_Cell_Map)

(0.1, 2e+06), (0.25, 1.2e+06), (0.4, 0.00), (0.55, 0.00), (0.7, 315000), (0.85, 0.00), (1.00, 123000)

No_of_Cells_in_NST = GRAPH(Grid_Size_of_Cell_Map)

(0.1, 7.2e+07), (0.25, 1.6e+07), (0.4, 6e+06), (0.55, 3e+06), (0.7, 1.5e+06), (0.85, 1e+06), (1.00, 720000)

No_of_Cells_in_TAC = GRAPH(Grid_Size_of_Cell_Map)

60

(0.1, 9e+06), (0.25, 2e+06), (0.4, 900000), (0.55, 500000), (0.7, 183000), (0.85, 100000), (1.00, 90000)

Time_Scale_Factor = GRAPH(Year_of_Analysis)

(1999, 0.46), (2001, 0.51), (2004, 0.582), (2006, 0.648), (2008, 0.708), (2011, 0.774), (2013, 0.851), (2016, 0.923), (2018,0.96), (2020, 1.00), (2023, 1.06), (2025, 1.09)

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Appendix B. Sample FAA ETMS Data.

The following dataset showcases the structure of the streamlined FAA Enhanced Traffic Manage-ment System flight plan information. This information was used in this research project to study air traffic flows around congested regions of airspace in NAS. These traffic flows serve as the basis for the derivation of aeronautical communication requirements in the LATFM model.

The data presented below illustrates a typical flight plan from New York John F. Kennedy Interna-tional Airport (KJFK) to Los Angeles International (KLAX). The flight is performed with a Boeing767 aircraft by American Airlines (AA). Figures B1 and B2 illustrate graphically the spatial trajectoryof the filed flight plan.

AAL1________00_0 YYY B767 YYY YYY 1

AAL1________00_0 JFK LAX 40.640 73.779 866 33.943 118.408 1152 14:26 286 0 C 50

IZYYYYYY 40.633 73.783 0 866.000 123 123

YYYYYYYY 40.417 74.143 112 871.885 330 304

YYYYYYYY 40.200 74.500 208 875.749 386 345

YYYYYYYY 40.285 74.988 282 879.526 441 386

YYYYYYYY 40.369 75.477 330 882.941 477 413

YYYYYYYY 40.450 75.967 347 886.272 477 411

YYYYYYYY 40.500 76.283 348 888.425 477 412

YYYYYYYY 40.550 76.600 349 890.573 477 412

YYYYYYYY 40.592 77.016 350 893.360 477 413

YYYYYYYY 40.633 77.433 350 896.142 477 414

YYYYYYYY 40.650 77.800 350 898.568 477 414

YYYYYYYY 40.687 78.282 350 901.762 477 415

YYYYYYYY 40.723 78.765 350 904.951 477 416

YYYYYYYY 40.756 79.249 350 908.132 477 417

YYYYYYYY 40.787 79.732 350 911.305 477 418

YYYYYYYY 40.817 80.217 350 914.472 477 419

YYYYYYYY 40.900 81.683 350 923.999 477 424

YYYYYYYY 40.900 81.817 350 924.854 477 425

YYYYYYYY 40.967 84.200 350 940.081 477 430

YYYYYYYY 40.983 85.183 350 946.289 477 433

YYYYYYYY 41.167 86.583 350 955.179 477 437

YYYYYYYY 41.167 89.583 350 973.665 477 448

YYYYYYYY 40.550 91.400 350 985.723 477 455

YYYYYYYY 40.417 91.783 350 988.259 477 455

YYYYYYYY 40.283 92.183 350 990.891 477 457

YYYYYYYY 40.133 92.583 350 993.574 477 456

62

YYYYYYYY 39.783 94.183 350 1003.617 477 459

YYYYYYYY 39.633 94.800 350 1007.519 477 459

YYYYYYYY 38.933 97.617 350 1025.428 477 461

YYYYYYYY 37.917 100.733 350 1046.062 477 461

YYYYYYYY 36.500 104.867 350 1074.123 477 457

YYYYYYYY 35.483 108.867 350 1100.881 477 456

YYYYYYYY 34.933 111.383 350 1117.655 477 458

YYYYYYYY 34.700 112.483 350 1124.986 477 461

YYYYYYYY 34.567 113.633 350 1132.452 477 456

YYYYYYYY 34.550 113.683 350 1132.802 477 459

YYYYYYYY 34.465 114.101 350 1135.583 477 459

YYYYYYYY 34.378 114.518 350 1138.365 477 459

YYYYYYYY 34.290 114.935 350 1141.146 477 459

YYYYYYYY 34.200 115.350 350 1143.926 477 460

YYYYYYYY 34.117 115.767 350 1146.704 477 466

YYYYYYYY 34.109 116.250 348 1149.798 438 427

YYYYYYYY 34.100 116.733 275 1153.199 435 419

YYYYYYYY 34.075 116.983 234 1155.014 429 410

YYYYYYYY 34.050 117.233 191 1156.889 409 393

YYYYYYYY 34.033 117.400 162 1158.184 393 381

YYYYYYYY 34.017 117.633 129 1160.075 369 361

YYYYYYYY 34.000 117.767 113 1161.221 345 340

YYYYYYYY 33.975 118.083 66 1164.440 252 253

LZYYYYYY 33.950 118.400 0 1168.621 219 225

63

Figure B1. Graphical depiction of the ETMS filed plan.

64

Figure B2: Speed and altitude profiles of the ETMS flight plan.