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Enda Item 5: Airspace Safety Monitoring Activities/Requirements in Asia/Pacific Region

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Enda Item 5: Airspace Safety Monitoring Activities/Requirements in Asia/Pacific Region RASMAG/19−IP/07 27-30/5/2014 International Civil Aviation Organization The Nineteenth Meeting of the Regional Airspace Safety Monitoring Advisory Group (RASMAG/19) Pattaya, Thailand, 27-30 May 2014 Agenda Item 5: Airspace Safety Monitoring Activities/Requirements in Asia/Pacific Region HEIGHT MONITORING RESULTS FROM THE AAMA TO APRIL 2014 (Presented by the AAMA) SUMMARY This paper provides details of a paper presented at the Regional Monitoring Agencies Coordination Group (RMACG) meeting held in Paris in May 2014. The paper summarises the latest height monitoring results from the Australian Airspace Monitoring Agency (AAMA) up to April 2014. 1. INTRODUCTION 1.1 The Ninth meeting of the Regional Monitoring Agencies Coordination Group (RMACG/9) was recently held in Paris during the period 19-23 May 2014. All 13 ICAO endorsed RMAs were in attendance. The AAMA provided a number of papers to the meeting including details of current ADSB monitoring outcomes. 2. DISCUSSION 2.1 Attachment 1 provides a paper presented by the AAMA to the RMACG/9 meeting. This paper discusses the latest height monitoring conducted by the Australian Airspace Monitoring Agency (AAMA) using the Automatic Data Surveillance Broadcast (ADS-B) network. Basic statistics are given along with example cases. 1.2 Currently the AAMA has monitored 85% of all Australian registered RVSM approved aircraft and approximately 99% of all major Australian airline fleets. Those unmonitored are so because of lack of ADS-B fitment and difficulties in accessing GMU monitoring. 3. ACTION BY THE MEETING 3.1 The meeting is invited to note the information contained in Attachment 1. ---END--- RMACG/9-WP/133 International Civil Aviation Organisation ` WORKING PAPER SEPARATION AND AIRSPACE SAFETY PANEL (SASP) WORKING GROUP MEETING NINTH MEETING Paris, France, May 2014 Agenda Item 7: Long Term Monitoring Requirements Implementation HEIGHT MONITORING RESULTS FROM THE AAMA TO APRIL 2014 (Presented by AAMA) (Prepared by Dr Steven Barry ) Summary This paper summarises the latest height monitoring results from the Australian Airspace Monitoring Agency (AAMA) up to April 2014. Results are given for overall statistics, Altimetry System Error (ASE) and geoid height reference for dierent aircraft types, and monitored aircraft types broken down by operator. Typical ASE behaviour is also illus- trated as well as a selection of atypical ASE behaviours. -1- RMACG/9-WP/133 01/01/2012 The rst date of ASE testing. 31/03/2014 The last date of ASE testing. 820 The number of days in time period. 2359 100% Total number of aircraft seen in our data. 2317 98.2% The number of aircraft seen in our data suitable for monitoring. 2124 90.0% The number of aircraft with ASE values that can be determined. 248 10.0% The number of aircraft monitored but with undetermined geoid. 1413 59.9% The number of aircraft using Height Above Mean Sea Level. 637 27.0% The number of aircraft using Height Above Ellipsoid. 17 0.7 % The number of aircraft with variable geoid reference. 290 12.3 % The number of aircraft with unknown geoid reference. Table 1: Basic statistics for AAMA height monitoring using ADS-B data 1 INTRODUCTION 1.1 This paper discusses the latest height monitoring conducted by the Australian Airspace Monitoring Agency (AAMA) using the Automatic Data Surveillance Broadcast (ADS-B) net- work. Basic statistics are given along with example cases. 1.2 Currently the AAMA has monitored 85% of of all Australian registered RVSM approved aircraft and approximately 99% of all major Australian airline eets. Those unmonitored are so because of lack of ADS-B tment and diculties in accessing GMU monitoring. 1.3 A major issue with using ADS-B data for calculation of Altimetry System Error (ASE) is establishment of which height geoid is being transmitted by the aircraft; height above mean sea level (HAMSL) or height above ellipsoid (HAE or WGS 84). This can be statistically determined if suciently good quality data is obtained from a wide range of geographic regions. Some aircraft switch between HAMSL and HAE when pilot or co-pilot avionics are used and this is termed a variable geoid. 1.4 Given sucient data the ASE can be determined to high degree of accuracy (±10 ft). For some aircraft which have limited data a precise ASE value can not be determined, however if the range of possible ASE is still acceptably below the ±245ft limit then the aircraft can be considered monitored. 1.5 In line with previous work the data was corrected for due to HAMSL data interpolation errors and bias in time of day or location, as determined from sampling against 1500 aircraft with accurate ASE results. 1.6 The Altimetry System Error (ASE) data was obtained by processing ADS-B messages for aircraft from the Australian ADS-B network with additional data provided from Indonesian ground stations and a station in Bangkok. The data spans 1 January 2014 to 31 March 2014. Table 1 shows the main statistics for the data. 1.7 Figure 1 shows the ASE values for the most common 17 aircraft types in our sample. For each aircraft type a box plot is given with each accompanying red dot indicating the ASE -2- RMACG/9-WP/133 Figure 1: ASE by aircraft type for 17 of the most common aircraft types ordered by median ASE. Box plots are given for each distribution with the accompanying red dots visually representing the ASE for each aircraft in that sample. value for that aircraft. The results here are for the 2124 aircraft for which ASE values can be determined. 1.8 Figure 2 shows the ASE values for the less common aircraft types in our sample. For each aircraft type a box plot is given with each accompanying red dot indicating the ASE value for that aircraft. The results here are for the 2124 aircraft for which ASE values can be determined. 1.9 Table 2 shows the data that accompanies Figure 1; that is, ASE by aircraft type including the mean, lower and upper bounds, and standard deviation. 1.10 Figure 3 shows the ASE distribution for the 2124 aircraft for which ASE values can be determined. The distribution is a Kernal Density Estimate caculated using the Python default routine (scipy.stats.gaussian_kde). The extreme ASE values are discussed later in this paper. 1.11 Table 2 shows the data that accompanies Figure 1; that is, ASE by aircraft type including the mean, lower and upper bounds, and standard deviation. -3- RMACG/9-WP/133 Figure 2: ASE by aircraft type for less common aircraft types ordered by median ASE. Box plots are given for each distribution with the accompanying red dots visually representing the ASE for each aircraft in that sample. -4- RMACG/9-WP/133 Figure 3: The kernal density estimate of the ASE distribution for the 2124 aircraft for which ASE values could be determined. Numerical values for mean (0 ft), median (8 ft), mode (16 ft), minimum (-244 ft), standard deviation (59ft), and maximum (233 ft) are shown. -5- RMACG/9-WP/133 Type ASE_mean ASE_lb ASE_ub ASE_st.dev Number B772 16.0 -63.0 143.0 40.0 195 B773 27.0 -36.0 118.0 32.0 220 A320 33.0 -154.0 156.0 55.0 222 B737NX -7.0 -123.0 84.0 32.0 297 B767 -86.0 -192.0 15.0 53.0 44 E170 8.0 -75.0 123.0 56.0 22 B744-10 -90.0 -210.0 2.0 41.0 186 B744-5 -68.0 -146.0 27.0 39.0 48 A340 -20.0 -85.0 46.0 36.0 62 A346 14.0 -69.0 56.0 28.0 45 BE20 13.0 -56.0 62.0 27.0 38 B748 2.0 -37.0 40.0 21.0 20 MD11 33.0 -116.0 153.0 63.0 101 F100 -59.0 -151.0 153.0 68.0 42 BE30 -5.0 -129.0 82.0 61.0 24 A380 -33.0 -140.0 48.0 39.0 38 Table 2: ASE values (mean, lower bound, upper bound, standard deviation) for the main aircraft types. op op A330 B737NX B773 A320 B772 B744-10 MD11 B744-5 A340 A380 B767 A346 F100 BE20 B787 UNK E170 AAR Asiana Airlines 5 11 ACA Air Canada 5 6 ACI Air Caledonia 2 1 AFE Airfast Indones AGC Strategicairlin 1 AIC Air India 13 AIQ Thai Airasia 4 AMSA Australian Mari ANA All Nippon Airw 1 ANG Air Niugini 3 2 3 ANO Capiteq 4 ANZ Air New Zealand 5 18 8 2 1 5 ARES Aerorescue ARG Aerolineas 7 AUH Abu Dhabi Amini 1 AVN Air Vanuatu 1 AWK Airwork AWQ Indonesia Air A 14 AXM Air Asia 1 BAW British Airways 10 9 22 8 BKP Bangkok Airways 1 BOE Boeing 1 BOM Bombardier BTV Metro Batavia A 1 CAL China Airlines 17 6 Continued on Next Page -6- RMACG/9-WP/133 Table Results Continued op op A330 B737NX B773 A320 B772 B744-10 MD11 B744-5 A340 A380 B767 A346 F100 BE20 B787 UNK E170 CBJ Beijing Capital 1 1 CCA Air China 28 16 1 CEB Cebu Pacic Ai 2 CES China Eastern A 33 4 4 CFH Careight 4 CHH Hainan Airlines 3 CKS Kalitta Air CLX Cargolux 1 CNV Us Navy 2 2 CON Conocophillips CPA Cathay Pacic 31 1 1 25 8 11 CRK Hong Kong Airli 3 CSC Sichuan Airline 3 CSH China Eastern A 2 2 CSN China Southern 29 2 4 CTM French Air Forc 1 2 DAL Delta Airlines 10 DCS Dca Gmbh 1 DHK Airtours Intern DUB Dubai Air Wing 1 EDG Western Air Cha EJA Netjets Av.
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