Probabilistic Sector Demand Prediction (TBO-Met project) · 2017. 7. 18. · A. Valenzuela A....

Introduction Methodology Application Example Summary

Probabilistic Sector Demand Prediction(TBO-Met project)

A. Valenzuela A. Franco D. Rivas

Department of Aerospace EngineeringUniversidad de Sevilla

Spain

International Workshop on Meteorology and Air Traffic ManagementManagement of Meteorological Uncertainty

Seville, Spain, May 24 - 25, 2017

Valenzuela et al. (U. Sevilla) Probabilistic Sector Demand - 1 / 25


Outline

1 Introduction

2 Methodology

3 Application Example

4 Summary



Outline

1 Introduction

2 Methodology


4 Summary



Motivation

Sector demand is a key element for the Network Manager.

⇓

The Network Manager balances sector capacity and sector demandbefore and during the day of operation.

The severity of the capacity shortfall determines which measure is to beimplemented, e.g. sector configuration, rerouting, or slot allocation.

⇓

An accurate prediction of the expected demand may lead to a betteridentification of the measures to be applied, thus minimizing theimpact on the network and improving the system efficiency.



Objectives

One of the objectives of the TBO-Met project is:

to increase the accuracy of the prediction of sector demand whenmeteorological uncertainty is taken into account.

In particular, the following sub-objectives are considered:1 To understand how weather uncertainty is propagated from the

trajectory scale to the traffic scale.2 To quantify the effects of weather uncertainty on the sector

demand at the pre-tactical and tactical phases.3 To quantify the benefits of improving the predictability of

individual trajectories on the predictability of sector demand.

Next, in this presentation, the first steps towards these goals arepresented:

the methodology to assess the uncertainty of sector demand, andsome preliminary results.



Objectives











Objectives











Outline

1 Introduction

2 Methodology


4 Summary



General Scheme

1 Definition of Scenario

1 ATC sector: geometry, capacity

2 Flights: origin, destination,

departure time...

3 Weather forecasts: EPS or

Nowcast, release and forecast

times...

2 Meteorological Data Processing

3 Trajectory Predictor

4 Sector Demand Analysis



General Scheme



Met data to be used by the trajectorypredictor: wind, air temperature,convection...





General Scheme




For each flight and for eachatmospheric realization, it computes adifferent aircraft trajectory xij .




General Scheme





The different atmospheric realizationslead to different predicted occupancyand entry counts, which arestatistically characterized.



General Scheme





For clarity, next, the methodology is presentedfor EPS, entry count, and assuming that all trajectories enter the

sector.



Definitions and Hypotheses

Sector geometry: fixed and does not change with time. The effects ofopening/closing sectors are not analysed.

m: number of flights.

n: number of members of the EPS.

xij(t): position of flight i (i = 1, . . . ,m) for member j (j = 1, . . . , n)at time t

xij(t) = [λij(t), φij(t), hij(t)]

where λ is the longitude, φ is the latitude, and h is the pressurealtitude.



Definitions and Hypotheses

Sector geometry: fixed and does not change with time. The effects ofopening/closing sectors are not analysed.

m: number of flights.

n: number of members of the EPS.

xij(t): position of flight i (i = 1, . . . ,m) for member j (j = 1, . . . , n)at time t

xij(t) = [λij(t), φij(t), hij(t)]

where λ is the longitude, φ is the latitude, and h is the pressurealtitude.

For each flight i , one has n trajectories xij, as many as EPS members



Entry Time and Entry Distance

For each trajectory xij , one has an entrytime to the sector tij ,E and its associatedentry point, xij(tij ,E ).

The n different entry times can bestatistically characterized:

Average entry time for flight i

ti,E =1

n

n∑j=1

tij,E

Dispersion of the entry time for flight i

∆ti,E = maxj

tij,E −minj

tij,E

The distance travelled by the aircraft from its origin to the entrypoint is denoted as dij ,E .







ti,E =1

n

n∑j=1

tij,E


∆ti,E = maxj

tij,E −minj

tij,E








ti,E =1

n

n∑j=1

tij,E


∆ti,E = maxj

tij,E −minj

tij,E




Entry Count I

Entry count: number of flights entering the sector during a selectedtime period, Pk .Because the entry times are uncertain, then the entry count isalso uncertain. The aircraft can enter the sector in different timeperiods.The duration of the time period plays a key role in the uncertaintyof the entry count. If it is very small, then the aircraft can enter thesector in different time periods for different ensemble members.

00:00 06:00 12:00 18:00 24:000

5

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40

45

Ent

ry c

ount

Time



Entry Count I


00:00 06:00 12:00 18:00 24:000

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Ent

ry c

ount

Time



Entry Count I


00:00 06:00 12:00 18:00 24:000

1

2

3

4

5

6

7

8

9

10

Ent

ry c

ount

Time



Entry Count II

1 Entry count for ensemble member j and time period Pk

Ej(Pk) =m∑i=1

Eij(Pk)

where Eij(Pk) is an entry function for flight i , ensemble member j , and time periodPk

Eij(Pk) =

{1 if tij,E ∈ Pk

0 otherwise.

2 Statistical characterization

Mean value

E(Pk) =1

n

n∑j=1

Ej(Pk)

Max and min values

Emax(Pk) = maxj

Ej(Pk)

Emin(Pk) = minj

Ej(Pk)

Dispersion

∆E(Pk) = Emax(Pk)− Emin(Pk)

Probability of exceeding capacity

P[E(Pk) > a] =number of Ej(Pk) > a

n



Entry Count II

1 Entry count for ensemble member j and time period Pk

Ej(Pk) =m∑i=1

Eij(Pk)

where Eij(Pk) is an entry function for flight i , ensemble member j , and time periodPk

Eij(Pk) =

{1 if tij,E ∈ Pk

0 otherwise.

2 Statistical characterization

Mean value

E(Pk) =1

n

n∑j=1

Ej(Pk)

Max and min values

Emax(Pk) = maxj

Ej(Pk)

Emin(Pk) = minj

Ej(Pk)

Dispersion

∆E(Pk) = Emax(Pk)− Emin(Pk)

Probability of exceeding capacity

P[E(Pk) > a] =number of Ej(Pk) > a

n



Entry Count III

For one particular flight, the uncertainty in the entry time isexpected to increase due to meteorological reasons when:

the forecasting horizon increases,the aircraft flies over regions with high uncertainty before enteringthe sector, andthe aircraft travels a large distance before entering the sector, thusaccumulating uncertainty along the flight.

Correspondingly, the uncertainty in the entry count is expected toincrease when:

the entry count is computed far in advance,the aircraft are affected by meteorological phenomena with largeuncertainty before entering the sector, andthe traffic is composed of many flights arriving from distantlocations.



Entry Count III

For one particular flight, the uncertainty in the entry time isexpected to increase due to meteorological reasons when:

the forecasting horizon increases,the aircraft flies over regions with high uncertainty before enteringthe sector, andthe aircraft travels a large distance before entering the sector, thusaccumulating uncertainty along the flight.

Correspondingly, the uncertainty in the entry count is expected toincrease when:

the entry count is computed far in advance,the aircraft are affected by meteorological phenomena with largeuncertainty before entering the sector, andthe traffic is composed of many flights arriving from distantlocations.



Outline

1 Introduction

2 Methodology


4 Summary



Traffic Scenario: ATC Sector

ATC sector: LECMSAU.

From FL345 to FL460.

Declared capacity: 36 flights/hour.

15° W 10° W 5° W 0° 5° E

30° N

35° N

40° N

45° N

50° N

LECMSAU



Traffic Scenario: Flights

464 flights that planned to cross this sector between 00:00 and 24:00on 07-Jan-2017 (retrieved from NEST, last filed flight plan).Fixed route for each flight (provided by the flight plan), the samefor all EPS members.All flights at constant pressure altitude (200 hPa). Cruise speedfrom BADA 3.13.

90° W 60° W 30° W 0°

30° S

15° S

0°

15° N

30° N

45° N

60° N

0−500 500−1000 1000−1500 1500−2000 > 20000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Distance from departure airport to entry point, dE [km]

Rel

ativ

e fr

eque

ncy

[−]



Traffic Scenario: Weather Forecasts

ECMWF-EPS, composed of 51 members.

Release time: 00:00 on 06-Jan-2017. Time steps: 12, 18, 24, 30,36, 42, 48, and 54 hours.

Met data: zonal wind, meridional wind, and air temperature.

For example, spreads at 36 hours (difference between max and min):

90° W 60° W 30° W 0°

30° S

15° S

0°

15° N

30° N

45° N

60° N

Zonal wind [m/s] − Spread

5

10

15

20

25

30

35

40

Meridional wind [m/s] − Spread

90° W 60° W 30° W 0°

30° S

15° S

0°

15° N

30° N

45° N

60° N

5

10

15

20

25

30

35

40

45

90° W 60° W 30° W 0°

30° S

15° S

0°

15° N

30° N

45° N

60° N

Temperature [ºC] − Spread

2

4

6

8

10

12

14

16

18

20



Trajectory Predictor

Developed for this application.

Inputs for each flight: departure time (from flight plan), route (fromflight plan), altitude (set to 200 hPa), and airspeed (from BADA3.13).

Output for each flight and for each forecast member: an aircrafttrajectory xij(t) when variable horizontal winds and variable airtemperature are encountered.

Pediction: it integrates a reduced ground speed which depends on airtemperature, crosswind, and along-track wind, encountered at eachlocation and each time (linear interpolations are used):

Vg ,ij(r , t) =√γRgTij(r , t)M2

i − w2c,ij(r , t) + wij(r , t)



Entry Time

0 2000 4000 6000 8000 10000 120000

100

200

300

400

500

600

di,E

[km]

∆ t i,E

[s]

0−60 60−120 120−180 180−240 >2400

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Dispersion of the entry time, ∆ti,E

[s]

Rel

ativ

e fr

eque

ncy

[−]



Entry Count, 60 minutes

00:00 06:00 12:00 18:00 24:000

5

10

15

20

25

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35

40

45

Ent

ry c

ount

Time

E

min

Emean

Emax

00:00 06:00 12:00 18:00 24:000

0.5

1

1.5

2

2.5

3

∆ E

Time



Entry Count, 10 minutes

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4

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8

10

12

Ent

ry c

ount

Time

E

min

Emean

Emax

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0.5

1

1.5

2

2.5

3

3.5

4

∆ E

Time



Probability of Exceeding the Declared Capacity

00:00 06:00 12:00 18:00 24:000

0.2

0.4

0.6

0.8

1

Time

Pro

babi

lity

0

2

4

6

8

10

Max

imum

cap

acity

def

icit

00:00 06:00 12:00 18:00 24:000

0.2

0.4

0.6

0.8

1

TimeP

roba

bilit

y00:00 06:00 12:00 18:00 24:00

0

2

4

6

8

10

Max

imum

cap

acity

def

icit



Outline

1 Introduction

2 Methodology


4 Summary



Conclusions

Methodology to assess the uncertainty of sector demand whenmeteorological uncertainty is taken into account.

Scenario described in terms of ATC sector, flights, and weather forecasts(e.g., EPS or Nowcasts). A trajectory predictor computes a differentaircraft trajectory for each flight and for each possible atmosphererealization.

Analysis based on:

mean, max, min, and dispersions of entry time and entry count,probability of exceeding the capacity and the maximum capacitydeficit (useful for DCB).

Uncertainty in the entry count expected to increase when: computed farin advance, regions with high uncertainty, flights from distant locations.

Realistic application, pre-tactical phase, considering ECMWF-EPS:

dispersion in the entry time vs entry distance,dispersion in the entry count rather large when compared with thecapacity of the sector (8%).



Conclusions



Analysis based on:







Conclusions



Analysis based on:







Conclusions



Analysis based on:







Conclusions



Analysis based on:







Future Work

Application of the methodology to tactical scenarios, withtrajectories avoiding the convective cells provided by Nowcasts.

To quantify the benefits of improving the predictability ofindividual trajectories on the predictability of sector demand.



Future Work





Future Work



0 2000 4000 6000 8000 10000 120000

100

200

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600

di,E[km]

∆t i,E[s] ⇒

00:00 06:00 12:00 18:00 24:000

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Ent

ry c

ount

Time

Emin

Emean

Emax


Thanks for your attention

This project has received funding from the SESAR Joint Undertakingunder grant agreement No 699294 under European Union’s Horizon 2020

research and innovation programme

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Probabilistic Sector Demand Prediction (TBO-Met project) · 2017. 7. 18. · A. Valenzuela A....

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