AWO.A.CVS.101 CS AWO.A.CVS.101 General

- (a) Combined vision systems (CVSs) combine a real-time imaging sensor and a display with a synthetic image generated using a terrain, runway and obstacle database, and a precision navigation position. - (b) A CVS shall comply with the requirements of the respective certification specifications for the images that are generated, and in addition shall ensure that: - (1) the images are conformal with each other; - (2) the images are aligned within 5 mrad laterally and vertically at the boresight of the display; - (3) the images do not cause confusion to the flight crew; and - (4) significant image discrepancies due to failure conditions are obvious to the flight crew. - (c) If a HUD (or equivalent display) is used to display the images, then it shall meet the performance and integrity requirements applicable to the intended type of operation. Refer to CS-AWO Subpart B Section 2 SA CAT I, Section 3 CAT II or Section 4 CAT III. - (d) CVSs that are used for operational credits shall also meet the requirements that apply for EFVSs or SVGSs.

[Issue: CS-AWO/2]

APPENDIX 1 TO THE ACCEPTABLE MEANS OF COMPLIANCE TO SUBPART A 'MODELS'

Signal-in-space models for approach and landing simulation and fault analysis

ED Decision 2022/007/R

1 Purpose

The purpose of this Appendix to the AMC is to provide acceptable signal-in-space models based on known navigation means (facilities external to the aircraft) that can be used to demonstrate in simulation the system performance in approach and landing. It should be noted that system performance depends on the performance of the navigation means (nominal limit and fault), and a performance demonstration conducted using one navigation means may not be valid when using another navigation means due to different nominal limit and fault characteristics.

Note: These models are primarily intended to simulate the characteristics of beams at low altitude and, therefore, the results derived from its use should not be relied on for heights above 150 m (500 ft).

- 2 ILS CAT I/II/III signal-in-space model

The values given are derived from the performance characteristics for ILS, contained in ICAO Annex 10, Volume I, Sixth Edition, dated July 2006, at Amendment 90, except where otherwise indicated.

ICAO Annex 10 Volume I (Attachment C, paragraph 2.14) defines a standard classification of ILS by using three characters:

- (1) facility performance (I, II or III); - (2) ILS points (A, B, C, T, D or E -see definition below) to which the localiser structure conforms to the course structure of a CAT II/III localiser; and - (3) level of integrity and continuity of service (1, 2, 3 or 4).

ILS point 'A'

A point on the ILS glide path measured along the extended runway centre line in the approach direction at a distance of 7.5 km (4 NM) from the threshold.

ILS poi nt 'B'

A point on the ILS glide path measured along the extended runway centre line in the approach direction at a distance of 1 050 m (3 500 ft) from the threshold.

ILS point 'C'

A point through which the downward extended straight portion of the nominal ILS glide path passes at a height of 30 m (100 ft) above the horizontal plane containing the threshold.

ILS reference datum (point 'T')

A point at a specified height located above the intersection of the runway centre line and the threshold, and through which the downward extended straight portion of the ILS glide path passes.

ILS point 'D'

A point 4 m (12 ft) above the runway centre line and 900 m (3 000 ft) from the threshold in the direction of the localiser.

ILS point 'E'

A point 4 m (12 ft) above the runway centre line and 600 m (2 000 ft) from the stop end of the runway in the direction of the threshold.

Depending on the intended operation, the minimum class of ILS elected will define the detailed characteristics of the ILS to be used for:

- -nominal and limit case analysis; - -failure cases to be considered; and - -integrity and continuity analysis.

2.1 Glide path

2.1.1 Glide path angles

It should be assumed that the operationally preferred glide path angle is 3°. The system should be shown to meet all applicable requirements with promulgated glide path angles from 2.5° to 3°. Minimum and maximum glide path angle slopes considered in the demonstrations should be defined and the system should meet all applicable requirements within the defined limits. Where certification is requested for the use of a larger beam angle, the performance on such a beam should be assessed.

- -For CAT I operations, it is recommended to cover at least a 2.5° to 3.5° glideslope range. - -For CAT II or CAT III operations, it is recommended to cover a 2.5° to 3° glideslope range.

2.1.2 Height of the ILS reference datum (height of glide path at threshold)

For establishing compliance with the longitudinal touchdown performance limits, it may be assumed that the height of the ILS reference datum is 15 m (50 ft).

2.1.3 Glide path alignment accuracy

It should be assumed that the standard deviation of the beam angle about the nominal angle (θ) is 0.025 θ.

2.1.4 Displacement sensitivity

It should be assumed that the angular displacement from the nominal glide path for 0.0875 DDM has the value of 0.12 θ.

2.1.5 Glide path structure

For the purposes of simulation, the noise spectrum of the ILS glide path may be represented by a white noise passed through a low-pass first-order filter of time constant 0.5 s.

Note: For CAT I ILS, a combination of high-frequency and low-frequency noise would be more representative of the actual noise experienced in-service.

Easy Access Rules for All-Weather Operations (CS-AWO)

MEASA

Slope characterised threshold

For the whole of the approach path, the output of the filter should be set to a twosigma level of: time

- -0.035 DDM up to point 'C' for facility performance Type I; and - -0.023 DDM up to ILS reference datum (point 'T') for facility performance Τype II or III.

(Background: An interpretation of Annex 10, Volume I, Section 3.1.5.4.1 and 3.1.5.4.2)

- Note 1: ICAO Annex 10 Section 3.1.5.4 defines higher value prior point 'B' for Type II or III facilities. Since the model is intended to be used only below 500 ft, the increase value prior point B may not be considered.

- Note 2: This model is primarily intended to simulate the characteristics of beams at low altitude and, therefore, the results derived from its use should not be relied on for heights above 150 m (500 ft).

2.1.6 Glide fault mode

The effect of a glideslope malfunction can be modelled as a ramp with a start time, a ramp rate, a glide monitoring threshold and a time-to-alert, as illustrated in Figure 1. As the effect of the glide fault may differ depending on start time and ramp rate, the combination that provides the most severe effect on aircraft deviation shall be considered.

Figure 1: ILS glide malfunction transient

The glide malfunction transient depends on the facility performance type:

Table 1: Glideslope monitoring threshold and time-to-alert based on facility performance

ILS Class

MEASA

Point B

L.A.X

I.B.x

I.C.x

L.T.x

I.D.x

I.E.x

II.T.x

II.D.x

II.E.x

or III.E.x Point C

0.015 DDM

2.2 Localiser

Point T

Point D

Point E

Easy Access Rules for All-Weather Operations (CS-AWO)

Unknown Localiser

signal performance

Unknown Localiser

signal performance

2.2.1 Course alignment accuracy

Unknown

It should be assumed that at the threshold the standard deviation of the course line about the centre line is:

- -3.5 m (12 ft) for facility performance Type I; and - -1.5 m (5 ft) for facility performance Type II or III.

Note: The Type II or III value is between those given in ICAO Annex 10, section 3.1.3.6 for CAT II and CAT III ILS which are assumed to be three-sigma values, 2.5 m (8.3 ft) and 1.0 m (3.3 ft) respectively.

2.2.2 Displacement sensitivity

performance

It should be assumed that the nominal displacement sensitivity at the ILS reference datum (Point 'T') has the value of 0.00145 DDM/m.

2.2.3 Course structure

For the purposes of simulation, the noise spectrum of the ILS localisers may be represented by a white noise passed through a low-pass first-order filter of time constant 0.5 s.

The two-sigma level value of the filter output should be set according to the minimum class of the ILS considered as per the following table:

Note: For CAT I ILS, a combination of high-frequency and low-frequency noise would be more representative of the actual noise experienced in-service.

Slope characterised

MEASA

by ramp rate (DDM/s)

Localiser slope threshold

- -Facility performance Type I: for initial approach path, the output of the filter should be set to a two-sigma level of 0.005 DDM up to the ILS point (A, B, C, T, D or E) elected as the minimum required for the operation. - -If the minimum required for the operation is 'A' or 'B', for final approach path, the output of the filter should be set to a two-sigma level of 0.015 DDM up to point 'C'. After point 'C', the local iser signal performance is unknown and should not be used. - -If the minimum required for the operation is 'C', 'T', 'D' or 'E', after the elected point, the localiser signal performance is unknown and should not be used. - -Facility performance Type II or III: for the whole of the approach path, the output of the filter should be set to a two-sigma level of 0.005 DDM.

2.2.4 Localiser fault mode

The effect of a localiser malfunction can be modelled as a ramp with a start time, a ramp rate, a localiser monitoring threshold and a time-to-alert as illustrated in Figure 2. As the effect of the localiser fault may differ depending on start time and ramp rate, the combination that provides the most severe aircraft deviation shall be considered.

Figure 2: ILS localiser malfunction transient

The localiser malfunction transient depends on the facility performance type:

Table 2: Localiser monitoring threshold and time-to-alert based on facility performance

2.2.5 Integrity and continuity

The probability of radiating false ILS localiser or glide guidance can be assumed to be:

- -for integrity level 1 ILS: no demonstrated values; - -for integrity level 2 ILS: 1.0 × 10 -7 in any one landing; and - -for integrity levels 3 & 4 ILS: 0.5 × 10 -9 in any one landing.

The probability of losing the ILS guidance localiser or glide can be assumed to be:

- -for integrity level 1 ILS: no demonstrated values; - -for integrity level 2 ILS: 4.0 × 10 -6 in any period of 15 s; - -for integrity level 3 ILS: 2.0 × 10 -6 in any period of 15 s; and - -for integrity level 4 ILS: 2.0 × 10 -6 in any period of 30 s (localiser) or 15 s (glide).

3 MLS signal-in-space model

The MLS models defined by the ICAO All Weather Operations Panel (AWOP) (reference AWOP/14-WP/659, dated 4/2/93) should be used for approach simulations. Alternatively, if certification of MLS is only sought for ILS lookalike operations, the applicant may use the ILS model defined in paragraph 2. This is based on the assertion that the MLS quality is equal to or better than that of the ILS and requires no further substantiation.

4 GLS signal-in-space model

What follows describes one acceptable model for the assumed characteristics of the GLS guidance errors. Applicants that use an alternate model are responsible for documenting the alternate model, its basis (including a mapping to ICAO Annex 10 characteristics and any additional assumptions made), and its validity.

The ground-based augmentation system (GBAS) performance model simulates the outputs of a fault-free GBAS airborne receiver when used in conjunction with a GBAS ground station categorised as either GAST C or GAST D.

The architecture of the GLS model is illustrated in Figure 3. The GLS model includes a navigation system error (NSE) generator which generates NSEs representative of a GBAS providing approach service type C or D as defined by the applicable requirements [ 1 , 2 , 3 ]. The position calculator adds NSEs to the true position of the GLS reference point (GRP). The deviation calculator computes the deviations of the GRP given the FAS data. A latency model is applied to each output of the GLS model.

1 ICAO Standards and Recommended Practices (SARPs) for the Global Navigation Satellite System (GNSS). Annex 10 to the Chicago Convention, Vol 1.

2 RTCA / DO-245, Minimum Aviation System Performance Standards (MASPS) for the Local Area Augmentation System (LAAS) .

3 RTCA / DO-253C, Minimum Operational Performance Standards (MOPS) for GPS Local Area Augmentation System (LAAS) Airborne Equipment .

FAS Data, GERP Flag, Dev Scale Flags

MEASA

True Ant Pos

GAST

NSE

FAST

GRP

(ECEF)

GRP

The development of all components of the GLS model is documented in [ 1 ], [ 2 ] and [ 3 ]. The NSE generator and the NSE step generator are discussed below. (LLH)

4.1 GBAS NSE generator

Horiz Dist to L TP

The GBAS NSE generator produces NSEs in the along-track, cross-track and vertical directions. The block diagram of the GBAS NSE generator is shown in Figure 4. The Gaussian white noise (GWN) generator produces three independent noise sequences with zero, mean, and unity variance. Each sequence is filtered by a second-order Butterworth filter. The compensation gain which brings the root mean square (rms) of the filtered noise back to unity is obtained as:

<!-- formula-not-decoded -->

The filter output is scaled by NSE scale factors Katrk , Kxtrk , and Kvert . At the beginning of each run, the NSE generator filter should be initialised at a value sampled from a Gaussian distribution consistent with these scale factors.

Figure 3: GBAS signal model

1 ICAO Paper GNSSP-WP-8, Validation of GBAS CAT I Accuracy: A GLS Model and Autoland Simulations for Boeing Airplanes , presented at the ICAO Global Navigation Satellite Systems Panel, Working Group B Meeting, Seattle, WA, May 29 -June 9, 2000.

2 ICAO NSP Mar 2009 WGW/WP 30, SARPS Support for Airworthiness Assessments GLS Signal Modeling , presented by Tim Murphy, Bretigny, 17-27 March 2009.

3 ICAO NSP May 2010 WGW WP 19, SARPS Support for Airworthiness Assessments - More on GLS Signal Modeling , Prepared by Tim Murphy.

Seed &amp; AT

Deviation

Calc Latency H (s) =·

MEASA

Ep

K Value Katrk Kxtrk

(nominalvs.

Model Type r Selector

Limit

Geometry)

GAST

Seed AT

*

2nd Order

Filter

·

+11

Compensation

Gain

*

Seq1

Seq2

Seq3

Step Event Time

Figure 4: GLS NSE generator

4.2 Second-order filter of GBAS NSE generator

The second-order filter to be implemented in the GBAS NSE generator is characterised by:

<!-- formula-not-decoded -->

where ω n is the natural frequency given by:

- -for GAST C: ω n = 0.01 rad/s - -for GAST D: ω n = 0.033 rad/s

4.3 Noise scale factor

The model accounts for the variation in accuracy due to satellite geometry by setting the noise scale factor to a constant which is sampled from a distribution. For each run, the value of Kvert is determined by selecting a sample, x , from a uniform distribution between 0 and 1. The value of Kvert is then given by the following function of x :

<!-- formula-not-decoded -->

where the parameters of the function are dependent on the GBAS approach service type as given in Table 3:

I- X

GWN

Generator o=1

NSE atr

Easy Access Rules for All-Weather Operations (CS-AWO)

Table 3: GAST-dependent parameters for Kvert

If the random pick from a distribution between 0 and 1 results in Kvert &gt; Kvert\_max , then the value should be discarded and another sample should be selected from the uniform distribution set to the maximum value from the table 1 . An alternative acceptable means for computing the NSE scale factors is given in Section 2 below.

For each run, the value of Kxtrk is determined by selecting a sample, x1 , from a uniform distribution between 0 and 1. For each run, the value of Katrk is determined by selecting a sample, x2 , from a uniform distribution between 0 and 1. The cross-track and along-track scale factors are then computed by:

<!-- formula-not-decoded -->

<!-- formula-not-decoded -->

where the parameters of the function are dependent on the GBAS approach service type as given in Table 4:

Table 4: GAST-dependent parameters for Kxtrk

If the random pick from a distribution between 0 and 1 results in Kxtrk &gt; Kxtrk\_max or Katrk &gt; Katrk\_max , then the value should be discarded and another sample should be selected from the uniform distribution.

4.4 NSE step generator

The NSE step generator is illustrated in Figure 5. Step errors will occur when individual satellites are removed from the position solution (e.g. a satellite fails and stops transmitting or the user receiver stops tracking a satellite for any reason) or due to an individual satellite rising or setting. The step generator produces representative step errors in the vertical, along-track and cross-track directions. This is accomplished by scaling a unit step function by factors that are derived from representative statistical distributions. First, three random samples, one for each axis, are selected from a zero mean unit variance normal distribution. Then, these samples are multiplied by scale factors that are chosen to simulate the statistical variation in the size of an error that would result from normal variations in the relative geometry between the user and the Ostop\_vert=f(x) = b, +b,x- Uniform Number MEASA

1 This corresponds to the case where VPL &gt; 10 m and, therefore, the system is not available.

Seed Random Generator

0≤x≤1

Scale Factor

Computation Kstep\_atrk Kstep\_xtrk

* - 1.01

Kstep\_ vert

One sample from normal distribution, |=0, G = 1

NSEstep\_atrk.

satellites. Finally, the resultant constant factors are multiplied with a unit step function time sequence. NSEstep\_xtrk

Figure 5: NSE step generator

4.5 NSE step generator scale factor computation

For each run, the value of  step\_vert is determined by selecting a sample, x , from a uniform distribution between 0 and 1. The value of  step\_vert is then given by the following function of x :

<!-- formula-not-decoded -->

where the parameters of the function are dependent on the GBAS approach service type as given in Table 5:

Table 5: GAST-dependent parameters for vert step \_ 

If the random pick from a distribution between 0 and 1 results in  step\_vert &gt;  step\_vert\_max , then the value should be discarded and another sample should be selected from the uniform distribution.

For each run, the value of  step\_xtrk is determined by selecting a sample, x1 , from a uniform distribution between 0 and 1. The value of  step\_atrk is determined by selecting a sample, x2 , from a uniform distribution between 0 and 1. The cross-track and along-track NSE step scale factors are then computed by:

Seed GWN

Generator

Easy Access Rules for All-Weather Operations (CS-AWO)

<!-- formula-not-decoded -->

<!-- formula-not-decoded -->

where the parameters of the function are dependent on the GBAS approach service type as given in Table 6:

<!-- formula-not-decoded -->

<!-- formula-not-decoded -->

If the random pick from a distribution between 0 and 1 results in  step\_xtrk &gt;  step\_xtrk\_max , or  step\_atrk &gt;  step\_axtrk\_max , then the value should be discarded and another sample should be selected from the uniform distribution.

4.6 Latency model

The latency of the GLS output should be delayed for a period of 400 ms.

4.7. Fault mode generator

The limit case or fault mode generator is illustrated in Figure 6.

The fault mode generator produces a ramp error with characteristics as illustrated in Figure 7 Malfunction transient . The effect of a malfunction is modelled as a ramp, with a start time, a ramp rate, and a total exposure time, Tmax . The maximum value of the ramp depends on the ramp rate and the time-to-alert. The ramp is assumed to increase to the level of the maximum value and then to exceed that value for a period equal to the timeto-detect and mitigate the failure. The erroneous satellite is isolated and the error returns to the nominal value (i.e. the fault error is set to zero). The model may alternatively produce step errors where the maximum change in error due to the step is specified rather than the ramp rate. (See reference [ 1 ] for more details regarding GLS fault modelling.)

1 T. Murphy, M Harris, C. Shively, L. Azoulai , M. Brenner, Fault Modeling for GBAS Airworthiness Assessments , Proceedings of the Institute of Navigation Global Navigation Satellite System Conference, 2010.

Limit or Malfunction

Axis to Apply-

MEASA

Slope characterised

by ramp rate [ft/s]

Uniform

Position

Seed Random Number Generator

error on axis

of choice

Seed GWN

Generator Set Limits form sample distribution, Uniform

0SX≤1

+

Ramp Duration

Ramp Event Time

Figure 6: Limit malfunction generator

Figure 7: Malfunction transient

From Figure 7, it can be seen that for the ramp:

<!-- formula-not-decoded -->

EMAX and the effective vertical alert limit (VAL) depend on the type of malfunction. For satellite ranging sources, the effective VAL is a function of the maximum error allowable with probability greater than 1 × 10 -9 by the Pmd performance constraint with conditional

Kother =AX)

KLimit\_atrk

KLimit\_ xtrk

KLimit\_vert VAL

Easy Access Rules for All-Weather Operations (CS-AWO)

probability (reference [ 1 ], Appendix B, Section 3.6.7.3.3.3) (i.e. 1.6 m), multiplied by the geometry screening limit.

<!-- formula-not-decoded -->

where is the maximum vertical projection for any satellite allowed by geometry screening. \_ max vert S

The aircraft manufacturer limits the size of by specifying a maximum Svert for satellites used in the position solution as described in reference [ 2 ]. MAX E

For ground segment reference receiver failures, the effective VAL will depend on the geometry screening applied in the airborne equipment. If no additional geometry screening is applied other than VPL&lt;VAL, the maximum effective VAL is 9.35 [m] (see Table 7 Malfunction transient characteristics in the vertical direction ). If additional geometry screening is applied, a lower effective VAL may result. Reference [ 3 ] explains how to compute the effective VAL given additional geometry screening. Figure 8 Maximum error and TBAC as a function of alert limits shows a plot of maximum vertical and lateral errors as a function of vertical and lateral alert limit screening. The calculations to produce the plot in Figure 8 are described in detail in reference [ 4 ].

For ionospheric anomalies, the maximum vertical error Emax is limited to a specified maximum allowable position error for the airborne installation for each axis, vertical ( MaxEV ) and lateral ( MaxEL ), as a part of the satellite geometry screening in the avionics [ 5 ]. These values along with broadcast information provided by the ground station determine the geometry screening.

1 T. Murphy, M Harris, C. Shively, L. Azoulai , M. Brenner, Fault Modeling for GBAS Airworthiness Assessments , Proceedings of the Institute of Navigation Global Navigation Satellite System Conference, 2010.

2 M. Harris, T. Murphy, Geometry Screening for GBAS to Meet CAT III Integrity and Continuity Requirements , Proceedings of the Institute of Navigation International Technical Meeting 2007.

3 M. Harris, T. Murphy, Geometry Screening for GBAS to Meet CAT III Integrity and Continuity Requirements, Proceedings of the Institute of Navigation International Technical Meeting 2007.

4 Neri, P., Macabiau, C., Azoulai, L., Study of a GBAS Model for CAT II/III Simulations, Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009.

5 Use of GNSS signals and their augmentations for civil Aviation Navigation during Approaches with Vertical Guidance and Precision Approaches -PhD Thesis P. Neri -2011.

Table 7 and Table 8 give the characteristics for transient errors in the vertical and horizontal directions respectively for each of the three major identified fault types.

Table 7: Malfunction transient characteristics in the vertical direction

Note: This value is an absolute worst case assuming no additional geometry screening is afforded based on reference receiver fault monitoring using TBAC. Smaller maximum values can be obtained by using additional geometry screening as per reference [ 1 ].

Table 8: Malfunction transient characteristics in the lateral direction

Note: This value is an absolute worst case assuming no additional geometry screening is afforded based on reference receiver fault monitoring using TBAC. Smaller maximum values can be obtained by using additional geometry screening as per reference [ 2 ].

1 Neri, P., Macabiau, C., Azoulai, L., Study of a GBAS Model for CAT II/III Simulations, Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009.

2 Neri, P., Macabiau, C., Azoulai, L., Study of a GBAS Model for CAT II/III Simulations, Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009.

MEASA

20

Maximum E, or E, at Pmd=0.0001or Tbac Im]

18

... ТвАс: M=2

16

14

12

10

Max E, or E, with Pmd=0.0001 vs VAL or LAL

Max E, or E, M=3

·. TBAC: M=3

Ма E, or E, M=4

ТвАс: M=4

Figure 8: Maximum error and TBAC as a function of alert limits

4.8 Integrity and continuity

The probability of issuing false GLS guidance can be assumed to be:

- -for GAST C: 2.0 × 10 -7 in any one landing; and - -for GAST D: 1 × 10 -9 in any period of 15 s.

The probability of losing GLS guidance can be assumed to be:

- -for GAST C: 8.0 × 10 -6 in any period of 15 s; - -for GAST D: 2 × 10 -6 in any period of 15 s.

- 4.9 Alternative method for calculating and using the NSE model scale factors

- 4.9.1 Estimation of 10-point piecewise linear interpolation of GBAS NSE -GAST D

An alternative method to use the NSE model is to compute, before launching any run of the Monte Carlo autoland simulations, the distributions of the scale factors, Kvert and Kxtr = Katrk . For these two last quantities, we conservatively allocate the worst horizontal sigma. These distributions have been computed using assumptions described in [ 1 ] and [ 2 ]. Then, for each run of the Monte Carlo V'O'

1 Neri, P., Macabiau, C., Azoulai, L., Study of a GBAS Model for CAT II/III Simulations, Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009.

2 Use of GNSS signals and their augmentations for civil Aviation Navigation during Approaches with Vertical Guidance and Precision Approaches -PhD Thesis P. Neri -2011.

0.05

MEASA

0.045

0.04-

0.035

0.03

0.025

0.02

0.015

0.01

0.005-

0

simulations, we draw, from these two distributions, a sigma vertical = Kvert and a worst horizontal sigma for Kxtrk and Katrk , at the beginning of the approach, which are kept constant during the approach.

In order to facilitate the use of these distributions, the 10-point piecewise linear interpolation of Kvert and Kworst\_horizontal\_sigma are provided on the histograms and using a dual entry table X-axis corresponding to sigma\_vert or worst\_horizontal\_sigma in metres.

4.9.2 Sigma vertical

0.4

0.6

0.8

Figure 9: Sigma vertical samples

nb of occumences

0.2

nb of occumences

0.12

MEASA

0.1

0.08

0.06

0.04

0.02-

4.9.3 Worst horizontal sigma

MAX=1.7m

MIN=0.005m

0.6

0.8

Figure 10: Worst horizontal sigma samples

As with the NSE noise scale factors, an alternative way of using the step function is to derive scale factors sigma\_step\_vert and worst\_sigma\_step horizontal to take into account steps induced by the loss of tracking of satellites by the receiver due to geometry (rise/set of satellites) or due to satellite failures. The distributions and the 10-point piecewise linear interpolations are provided below for these two cases for the two dimensions.

0.2

0.4

MEASA

0.055

0.03

0.025

0.02

0.015

0.01

0.005

nb of occurrences bins width = 0.01 m

4.9.4 Rise/set -sigma step vertical

MAX=1.605m

MIN=0.005ml

1.5

2.5

Rise/set - sigma step vert (meters)

Figure 11: Rise/set sigma samples

0.5

MEASA

0.06

0.05

4.9.5 Rise/set -worst sigma step horizontal

MAX=1.425ml

MIN=0.005m

Figure 12: Rise/set worst sigma step horizontal samples

nb of occurrences

0.04

0.03

0.02

0.01

0.5

MEASA

0.025

0.02

4.9.6 Signal loss -sigma step vertical

MAX=1.565ml

MIN=0.005m

Figure 12: Rise/set worst sigma step horizontal samples

nb of occurrences

0.015

0.01

0.005

0.5

'O'

MEASA

0.04

0.035

0.03

0.025

0.02

0.015

0.01

0.005

4.9.7 Signal loss -worst sigma step horizontal

MAX=4.995ml

MIN=0.005ml

Figure 14: Signal loss worst sigma step horizontal sigma samples

nb of occurrences

0.5

Easy Access Rules for All-Weather Operations (CS-AWO)

Wind models for approach and landing simulation

1 Purpose

The purpose of this AMC is to provide acceptable wind, turbulence and wind shear models that can be used to demonstrate in simulation system performance in approach and landing.

2.1 Wind model Number 1

2.1.1 Mean wind

It may be assumed that the cumulative probability of reported mean wind speed at landing, and the crosswind component of that wind are as shown in Figure 15 Cumulative probability of reported mean wind and headwind, tailwind and crosswind components when landing . Normally, the mean wind which is reported to the pilot is measured at a height which may be between 6 m (20 ft) and 10 m (33 ft) above the runway. The models of wind shear and turbulence given in paragraphs 3.2 and 3.3 assume this reference height is used.

2.1.2 Wind shear

2.1.2.1 Normal wind shear

Wind shear should be included in each simulated approach and landing unless its effect can be accounted for separately. The magnitude of the shear should be defined by the expression:

<!-- formula-not-decoded -->

where 'u' is the mean wind speed at height z metres (z ≥ 1 m) and 'U' is the mean wind speed at 10 m (33 ft).

2.1.2.2 Abnormal wind shear

The effect of wind shears exceeding those of paragraph 2.1.2.1 should be investigated using known severe wind shear data.

2.1.3 Turbulence

2.1.3.1 Horizontal component of turbulence

It may be assumed that the longitudinal component (in the direction of mean wind) and lateral component of turbulence may each be represented by a Gaussian process having a spectrum of the form:

Φ(Ω)

<!-- formula-not-decoded -->

where:

Φ(Ω) =

a spectral density [[metres/s] 2 per [radian/metre]]

σ =

root mean square (rms) turbulence intensity = 0·15 U

L =

scale length = 183 m (600 ft)

Ω =

frequency [radians/metre]

<!-- formula-not-decoded -->

MEASA

Cross Wind

Component

Tail Wind

Component

0

10

99.99

99

2.1.3.2 Vertical component of turbulence

It may be assumed that the vertical component of turbulence has a spectrum of the form defined by equation (2) in paragraph 2.1.3.1.

The following values have been in use:

<!-- formula-not-decoded -->

or alternatively

70

= 0.09 U with L = 4·6 m (15 ft) when z &lt; 9.2 m (30 ft)

and L = 0.5 z when 9.2 &lt; z &lt; 305 m (30 &lt; z &lt; 1 000 ft)

20

WIND SPEED KNOTS

Figure 15: Cumulative probability of reported mean wind and headwind, tailwind and crosswind components when landing

Easy Access Rules for All-Weather Operations (CS-AWO)

Note: This data is based on worldwide in-service operations of UK airlines (sample size: about 2 000).

These cumulative probabilities could be approximated by a Gaussian distribution with characteristics as below:

2.2 Wind model Number 2

2.2.1 Mean wind

The mean wind is the steady-state wind measured at landing. This mean wind is composed of a downwind component (headwind and tailwind) and a crosswind component. The cumulative probability distributions for these components are provided in Figure 16 Headwind -tailwind description (downwind) and in Figure 17 Crosswind description (crosswind).

Alternatively, the mean wind can be defined with magnitude and direction. The cumulative probability for the mean wind magnitude is provided in Figure 8 Annual per cent probability of mean wind speed equalling or exceeding given values , and the histogram of the mean wind direction is provided in Figure 9 Histogram of the mean wind direction relative to runway heading . The mean wind is measured at a reference altitude of 20 ft above ground level (AGL). The models of the wind shear and turbulence given in Sections 2.2.2 and 2.2.3 assume this reference altitude of 20 ft AGL is used.

2.2.2 Wind shear

When stable and steady horizontal wind blows over the ground surface, terrain irregularities and obstacles such as trees and buildings alter the steady wind near the surface and a boundary layer will cause a form of wind shear. The magnitude of this shear is defined by the following expression:

<!-- formula-not-decoded -->

where Vwref is the mean wind speed measured at h ft and V20 is the mean wind speed (ft/s) at 20 ft AGL.

Note: This expression does not represent the violent wind shears created by unstable air mass conditions.

2.2.3 Turbulence

2.2.3.1

Turbulence spectra

The turbulence spectra are of the Von Karman form.

The vertical component of turbulence (perpendicular to the earth's surface) has a spectrum of the form defined by the following equation:

<!-- formula-not-decoded -->

D/VT

2T

~ y

Ow

91(0"1 688 1) + 1)

10 E78-0 + 41-0)

1+8

· 1-339 L 2)

11+11-339 L 2) 41/6

MEASA

Easy Access Rules for All-Weather Operations (CS-AWO)

The horizontal component of turbulence (in the direction of the mean horizontal wind) has a spectrum of the form defined by the following equation:

<!-- formula-not-decoded -->

The lateral component of turbulence (perpendicular to the mean horizontal wind) has a spectrum of the form defined by the following equation:

<!-- formula-not-decoded -->

where:

Ω =

spectral density [ft/s] 2

σ =

root mean square (rms) turbulence intensity [ft/s]

L =

scale length

Ω =

spatial frequency [radians/ft] = /VT

ω =

temporal frequency [radians/s]

VT =

aircraft speed [ft/s]

2.2.3.2 Turbulence intensities and scale lengths

At or above altitude h1, turbulence is considered to be isotropic, i.e. the statistical properties of the turbulence components are independent. This means that one can consider the turbulence components to have equal intensities.

Below h1, turbulence varies with altitude. In this case, intensity and scale length are expressed as functions of V20 and altitude.

Turbulence intensities

σW = 0.1061 V20

where V20 is expressed in kt

where σ W is expressed in ft/s

For h &lt; h1,

σ W

σU = σU =

For h ≥ h 1 ,

σU = σU =

where h1 = 1 000 ft

99.99

MEASA

99.9

99

TAILWIND SPEED KT (WIND FROM 180 DEG)

- 30

0.01

90

10

Probability of Exceedance, %

80

70 60 50 40 30 20

Scale lengths

<!-- formula-not-decoded -->

2.2.3.3 Fixed turbulence intensities for pilot-in-the-loop simulations

The following fixed levels of turbulence intensity [ft/s] have been found to be representative when used to programme low-altitude simulations with the pilot in the loop.

Turbulence scale lengths vary with altitude according to the equations of paragraph 2.2.3.2.

Figure 16: Headwind -tailwind description

10

0.01

99.99

MEASA

99.9

99

PERCENT PROBABILITY OF EXCEEDANCE

CROSSWIND SPEED KT COUNTERCLOCKWISE FROM 0 DEG

1.0

0.8

0.6

0.4

0.2

0.1

90

Probability of Exceedance, %

80

70

20

10

0.1

Figure 17: Crosswind description

10

15

20

Wind Speed Vw kt

25

30

35

40

Figure 18: Annual per cent probability of mean wind speed equalling or exceeding given values

5

MEASA

Wind Direction Histogram

12

P 10

e

r

e

n

228

Figure 19: Histogram of the mean wind direction relative to runway heading

2.3. Wind model Number 3

This wind model is a derivative of wind model Number 2. The changes are a result of experience from pilot-in-the-loop simulator tests for Category III HUD certification, where the wind shear and turbulence intensities were found to be more representative.

The changes to wind model Number 2 are as follows:

- (a) Paragraph 2.2.2

Change Vwref = 0.204 × V20 log n [(h+0.15) ÷ 0.15]

to Vwref = 0.165 × V20 log n [(h +0.046) ÷ 0.046]

- (b) Paragraph 2.2.3.2

Change  w = 0.1061 V20

to  w = 0.0625 V20

where V20 is expressed in kt

where σw is expressed in ft/s

- (c) Paragraph 2.2.3.2

Change

Lu to Lu = 600 ft

[Issue: CS-AWO/2]

SUBPART B -APPROACH AND LANDING

SECTION 1 -AIRWORTHINESS CERTIFICATION OF AEROPLANES FOR OPERATIONS WITH DECISION ALTITUDES/HEIGHTS (DAS/HS) NOT LOWER THAN 60 M (200 FT) -CATEGORY I (CAT I) OPERATIONS

GENERAL