european airport performanceframework
DESCRIPTION
Presented in the conference SPADETRANSCRIPT
Definition and measure of KPAs/KPIs to monitor airside airport performance across Europe.
SPADE WorkshopGran Canarias (Spain), 5th November 2009 Jose Luis Garcia-Chico Performance Review Unit/Eurocontrol(Aena secondment to PRU)[email protected] / [email protected]. +32 474 123814
Acknowledgements
• Work developed within ATMAP project by the Performance Review Unit of Eurocontrol in consultation with airport community
• ATMAP Project manager: Francesco Preti
• Main contributors to the framework from PRU staff: Heloise Cote, Luis Olmo, Holger Hegendorfer, Milen Dentchev, Philippe Enaud
• …but also airports, airlines, slot coordinators, and ANSPs that, through ATMAP, have provided valuable comments and inputs to our framework
ATMAP aims at establishing a performance measuring framework of airport airside operations within the SES legislative context
Institutional Framework
• PRC/PRU monitor and report on ATM related performance parameters to EUROCONTROL
• SES (2004) introduced the performance review function on ANS: requirements for monitoring and data collection
• SES II (2009) introduced Performance Scheme: setting binding targets
ATMAP Objectives
ATMAP Approach
Outcome
• Develop a framework to measure ANS performance on the airport airside operations consistently and continuously across Europe
• Identify a set of easy to understand relevant high-level performance indicators (KPIs)
• Specify data requirements to feed the framework;
• Developing understanding of the overall air transport at airports (focused on outcomes not accountability)
• Identifying external factors to ANS that have a significant impact on performance
• Validating the framework for high-level performance review with the project participants using various data sources
• Leveraging previous PRU experience on ATM performance monitoring
• Consistent approach to measure ANS airport performance across Europe: set of KPIs & common European data repository
• Common understanding of performance indicators and associated definitions
• Clear definition of data requirements to support performance based approach
Scope of the framework focuses on ANS around airports: airport airside-terminal environment
Runway system
Taxiway system
Gates/ turn-around
TMA - arrival TMA – departureEn-route En-route
Entry fix
Exit fix
Network NetworkAirport
Arrivals Departures
Turn-around
• Network delivery in and out of the surrounding airport airspace
• Interface between ground-handling and airside operations when impact ATM performance
• No detailed analysis of local airport and airline turn-around processes
• Airspace for arrival sequencing (0/40 and 0/100Nm)• Airport movement area (aprons, taxiways, runway)• Coordinated airports • Daily period 06:00– 21:59
ATM Performance Airline
Performance
LandsideAirport
Performance
Air Transport Airside Performance
Project Scope
ATM Performance Airline
Performance
LandsideAirport
Performance
Air Transport Airside Performance
Project Scope
Conceptual framework links scheduling and observed operations with external factors/drivers
Performance
Airport Airlines ANSP
Airport Airlines ANSP
Externalfactors
Weather
Environmental
Airport layout
Scheduling of Operations
Observed activity the day of operations
Traffic Mix
ATC procedures
Others
Scheduling practices and local external factors are drivers of performance
The airport scheduling process limits the utilisation ratio and reduce variability
Challenging task to develop a high-level framework affected by multiple factors
A clear allocation of causes and accountability to stakeholders is difficult
End-product results from complex interrelated systems
Current approach addresses the understanding the overall air transport performance at airports (Outcome)
Airport Airside Performance Framework Breakdown
KPIs Breakdown KPAs
Traffic Volume & Demand
Capacity
Punctuality
Efficiency
Predictability
Flexibility
Emissions
• Handled Traffic
• Coordinated Demand
• Coordinated Cancelled Demand
• Declared Capacity
• Service Rate
• Additional Time of Inbound Flow
• Additional Time of Outbound Flow
• Variability of Flight Segments
• Variability of Arrival Flow Rate
• TBD
• TBD
• On Time Arrivals & Departures
• Early Arrivals
• Departure Delay (including Causes)
• Slot utilization: Handled Traffic /Declared Capacity
• Ratio Service Rate /Peak Declared Capacity
Consolidation
Low High
KPI Handled Traffic: European traffic experienced a generalized decreased in both Winter (-8%) and Summer (-5%) seasons
• Definition:Number of flight movements served by an airport in a given time period
• Breakdown: total, daily rate, peak-month, by season, by calendar year,
• IFR movements in the European area continued dropping during last year
Winter: -8% Summer: -5%0%
2%-6%-6%
-3%-14%-4%
-8%-1%-3%
-9%-10%
-8%
-7%-4%-2%0%
-5%
0
200
400
600
800
1000
1200
1400
1600
Par
is C
harle
s-de
-Gau
lle
Lond
on H
eath
row
Fra
nkfu
rt
Mad
rid
Mun
ich
Rom
e F
ium
icin
o
Bar
celo
na
Vie
nna
Zur
ich
Lond
on G
atw
ick
Par
is O
rly
Bru
ssel
s
Mila
n M
alpe
nsa
Dub
lin
Pal
ma
Hel
sink
i
Pra
gue
Lisb
on
Air
po
rt D
aily
Mo
vem
ents
[fl
t/d
ay] Summer 08
Summer 09*
data source : EUROCONTROL/CFMU*Summer 09: up to Oct 1st
-4%-1%
-4% -13%-7%
-7% -17%-9%
-5%-9% -10%
-33%
-10%
-8%-2% -9%
-6%
-6%
0
200
400
600
800
1000
1200
1400
1600P
aris
Cha
rles-
de-G
aulle
Lond
on H
eath
row
Fra
nkfu
rt
Mad
rid
Mun
ich
Rom
e F
ium
icin
o
Bar
celo
na
Vie
nna
Zur
ich
Lond
on G
atw
ick
Par
is O
rly
Bru
ssel
s
Mila
n M
alpe
nsa
Dub
lin
Pal
ma
Hel
sink
i
Pra
gue
Lisb
on
Air
po
rt D
aily
Mo
ve
men
ts [
flt/
day
]
Winter 07-08
Winter 08-09
data source : EUROCONTROL/CFMU
0,66
0,61
0,73
0,67
0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8
Se
as
on
s
Slot Utilization
Winter 07-08
Winter 08-09
Summer 08
Summer 09
-9,4%
-7,7%
KPI Slot Utilization: European slot utilization decreased in both Winter (- 9,4%) and Summer (- 7,7%) seasons
• Slot utilization is determined by the traffic served by the airport and the scheduling parameters (i.e., declared capacity)
• This ratio illustrates how much the declared capacity is used, being higher during summer season. This difference is more significant at airports with seasonal demand
• Slot utilization has decreased last summer in line with traffic drop
Data source: EUROCONTROL/CFMUSample of 18 ATMAP airports
0,77
0,34
0,80
0,62
0,64
0,31
0,71
0,59
0,0 0,2 0,4 0,6 0,8 1,0
Barcelona
Palma
Winter Summer
W07-08
W08-09
W07-08
W08-09
S09
S08
S09
S08
Traffic mix of service rate by airport
0
20
40
60
80
100
120
Paris
Charle
s-de-
Gaulle
Mad
rid
Mun
ich
Lond
on H
eath
row
Frank
furt
Rome Fium
icino
Vienna
Bruss
els
Barce
lona
Zurich
Palma
Paris
Orly
Mila
n M
alpen
sa
Lond
on G
atwick
Helsink
i
Dublin
Prague
Lisbo
nNu
mb
er o
f m
ove
men
ts [
#fli
gh
ts/h
ou
r]
Light Medium Heavy Peak Declared
KPI Service Rate as approximation of maximum airport throughput
• Service rate is used as approximate measure of maximum airport throughput
• This metric may be used to infer “operational capacity” only when the airport system is close to saturation:
Sufficient number of hours with peak demandTraffic must experience certain level of delay
• Metric is sensitive to demand variations
Definition: 1 percentile of the distribution of observed throughput (mov/hour) during the peak month
Data source: EUROCONTROL/CFMUPeriod 2008
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
7
10 13 16 19 22 25 28 31 34 37
1P
Peak Month
KPI Punctuality is calculated based on comparison between actual block times at the stand with airline scheduled times
Data source: EUROCONTROL/eCODAPeriod 2008
On Time flight Performance (Arrivals & Departures)(Based on delays as from 15 minutes)
50
5560
65
70
7580
85B
arce
lona
Bru
ssel
s
Dub
lin
Fra
nkfu
rt
Hel
sink
i
Lisb
on
Lond
on G
atw
ick
Lond
on H
eath
row
Mad
rid
Mila
n M
alpe
nsa
Mun
ich
Pal
ma
Par
is C
harle
s-de
-Gau
lle
Par
is O
rly
Pra
gue
Rom
e F
ium
icin
o
Vie
nna
Zur
ichP
erce
nta
ge
of
flig
hs
[%]
Departure Arrival
Early Arrivals(Based on arrivals 15 minutes ahead of scheduled)
0
5
10
15Per
cen
tag
e o
f fl
igh
s [%
]
Average Arrival: 76,3% Averagedeparture: 72,1%
Average: 10%
Both arrival (+7.9%) and departure (+7.5%) punctuality has improved in Europe in the last summer seasons
Data source: EUROCONTROL/eCODASample of 18 ATMAP airportsSummer 09 up to Oct 1st
S07W07/08
S08
W08/09S09
On-time Arrival [% flights]
0255075100
+7,9%
+3,7%
On-time Departure [% flights]
0 25 50 75 100
Summer 07
Summer 08
Summer 09
+7,5%
+2,8%
Early Arrival (ahead scheduled) [% flights]
0255075100
+26%
+43%
Criteria 3 minCriteria 15 minCriteria 15 minCriteria 3 min Early Arrival 15 minEarly arrival 3 min
• There is a general improvement on arrival and departure performance (with both punctuality criteria: 3 and 15 minutes)
• Early arrivals have increased: challenge for local resource allocation (e.g., stand allocation, ground handling)
• Performance is consistent with decreased traffic demand
Conceptual framework: Efficiency is measured by indicators of additional times experienced by flights for each phase of flight
Arrival FlowDeparture(from airport ‘j’)
En-route
Flight Airport ATaxi-out
Additional Time in
Taxi-in
Additional Time of Inbound Flow to Airport A
Departure(at airport ‘A’)
Eng
ines
-off
Eng
ines
-on
ANS-related holding at gate
due to downstream Airport A
(ATFM delays)
Additional Time
in ASMA(airborne)
Additional Time of Outbound Flow to Airport A
Eng
ines
-off
Eng
ines
-on
ANS-related holding at gate due to airport A(pre-departure
delays)
Additional Time in Taxi-out
En-route
Flight …
• Additional times are measured as difference between the actual length of a flight phase with respect to an unimpeded time (reference), which represents the time to typically complete the flight phase in period of low traffic
• Indicators of efficiency are split on outbound / inbound flows of the studied airportEngine-off delays: ATFM and pre-departure delaysEngine-on extra times in airborne holding and in taxi out phases
• Additional time of taxi-in is not initially included in the framework (relative less influence)
Arrival inbound efficiency (1): ATFM arrival delay isolates ATFM regulations originated from the destination airport
ATFM Arrival Delays
0,0
1,0
2,0
3,0
4,0
5,0
Barce
lona
Bruss
els
Dublin
Fran
kfurt
Helsink
i
Lisbo
n
Lond
on G
atwick
Lond
on H
eath
row
Mad
rid
Mila
n M
alpen
sa
Mun
ich
Palma
Paris
Charle
s-de
-Gau
lle
Paris
Orly
Pragu
e
Roma
Vienna
Zuric
h
AT
FM
del
ay [
min
/flt
]ATC & Aerodrome Capacity Weather All other arrival delay (strike, equiment, etc)
Data source: EUROCONTROL/CFMUPeriod 2008
• ATFM delays due to CFMU regulations are isolated to account for restrictions originated at the destination airport
• Airports are differently impacted by weather and capacity constrains. On average in 2008:32 % ATC & aerodrome capacity57 % weather11 % other
Arrival inbound efficiency (2): Arrival Sequencing and Metering Area additional time captures the arrival control strategies/inefficiencies
• ASMA transit time is defined as the time between entering the circle 100NM and landing•Small variations of ASMA additional time among European airports
LHR strategy of holding aircraft on stacks close to airport
• This metric is affected by several parameters such as type of aircraft, congestion level, airspace design, airport configuration, environmental restrictions
Data source: EUROCONTROL/CFMU/eCODAPeriod 2008
0
5
10
15
20
25
30
35
Lond
on Hea
thro
w
Frank
furt
Vienn
a
Zurich
Lond
on G
atwick
Dublin
Palm
a
Mun
ich
Paris
Charle
s-de
-Gaull
e
Bruss
els
Mad
rid
Prague
Mila
n Malpe
nsa
Rome
Fium
icino
Paris
Orly
Barce
lona
Lisb
on
AS
MA
tra
ns
it t
ime
[m
in/f
ligh
t]
Unimpeded time within the last 100 NM Additional time within the last 100 NM
ASMA average transit time : 25,8 min/flt
40 Nm
100 Nm
MadridMarch 6th 2008Source CFMU
ASMA additional time average: 3.3 min/flt
Departure outbound efficiency (1): pre-departure delays originated at the departure airport (normally do not generate AFTM)
Data source: EUROCONTROL/eCODAPeriod 2008
• Information based on airline submitted IATA delay codes
• Isolate, among delay causes, those weather and congestion related restrictions at the airport of departure
20 % departure congestion80 % weather departure
Pre-Departure Delays
0,0
1,0
2,0
3,0
4,0
5,0
Ave
rag
e D
elay
[m
in/f
lt]
Departure Airside Constraints (IATA code 87,89) Departure Weather (IATA code 71,75,76)
Departure outbound efficiency (2): taxi-out additional time as the period between take
• Taxi-out is defined as the period between off-block and the time of taking off
• Difference in taxi-out additional times are significant among European airports, as it depends of several factors: airport layout (distance stand-runway), type of stand, start-up process, apron congestion, de-icing procedures, and others.
0,0
5,0
10,0
15,0
20,0
25,0
Lond
on H
eath
row
Rome Fium
icino
Paris
Charle
s-de-
Gaulle
Barce
lona
Lond
on G
atwick
Mad
rid
Dublin
Mila
n M
alpen
sa
* Fra
nkfu
rt
Mun
ich
Bruss
els
Lisbo
n
Zurich
Paris
Orly
Palma
Prague
Vienna
Helsink
i
Tax
i-o
ut
tim
e [m
in/f
ligh
t]
Unimpeded time Additional time
ATMAP taxi-out time average: 14,2 min/flt Taxi-out additional time average: 4,6 min/flt
Data source: EUROCONTROL/CFMUPeriod 2008
There seems to be a link between the amount of slot utilization and quality of service in Europe
R2 = 0,4467
0
2
4
6
8
10
20% 40% 60% 80% 100%
Airport slots operated [%]
AS
MA
Ad
dit
ion
al T
ime
[min
]
R2 = 0,4329
0
2
4
6
8
10
20% 40% 60% 80% 100%
Airport slots operated [%]
Tax
i-o
ut
add
itio
nal
tim
e [m
in]
• Link between inefficiencies (quality of service) and airport slot utilization (served traffic / available slots)
• Trade-offs among indicators will help to understand performance
• Other affecting performance factors must be accounted for at local and aggregate level (e.g., meteorology) that need to be incorporated into the framework
Algorithm to classify weather conditions
Data source: EUROCONTROL/CFMU/EcodaPeriod 2008
KPI Predictability: Variability of departure/arrival times and flight phase duration
• Analysis from an airline scheduling point of view (same O&D, operator, STD)
• Flight Phase predictability
• Measured as the standard deviation or inter-percentile range of the distribution
• Variability is mainly generated in the turn-around phase (or due to reactionary delays), but amplified by the airside operations
• Delay is one driver, but not the only one
-10
-5
0
5
10
15
20
25
20
03
20
04
20
05
20
06
20
07
20
08
20
03
20
04
20
05
20
06
20
07
20
08
20
03
20
04
20
05
20
06
20
07
20
08
20
03
20
04
20
05
20
06
20
07
20
08
20
03
20
04
20
05
20
06
20
07
20
08
Departure time Taxi-out + holding Flight time (cruising+ terminal)
Taxi-in + waiting forthe gate
Arrrival time
min
utes
20th Percentile 80th Percentile Standard Deviation
Gate-to-gate phase Source: CODA
Time of operation
Nu
mb
er
of
ob
se
rva
tio
ns
(2)
Schedule Arrival
Average Actual
Departure
Average Actual Arrival
Time of operation
Time of operation
Nu
mb
er
of
ob
se
rva
tio
ns (2)Schedule
Departure(2)Schedule
Departure
Meteorology impact on ANS performance at airports
• Breakdown weather conditions consistently across European airports based on transparent and common criteria (infrastructure, procedures, and congestion are not initially considered)
Nominal Degraded Disrupted
• Definition of a severity scale (weather classes) for each weather phenomena Type of weather phenomena (e.g., wind, visibility) Duration Combination
• Algorithm initially applied to METAR reports Wind by RWY Visibility by RWY and cloud base Precipitations Freezing conditions (humidity and temperature) Thunderstorms / Convective weather Others (wind shear, wind aloft, etc.)
GUSTS22%
FREEZING10%
VISIBILITY11%
RVR7%
CEILLING7%
CLOUDS_TYPE3%
PRECIPITATIONS2%
WIND_INTENSITY38%
Data source: METAR reportsPeriod Jan-March 2008
Weather classification varies across European airports: reaction to weather situations are expected to vary too
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% d
ays
by
crit
eria
Degraded Severe
Degraded M oderate
Nominal Fair
Nominal Excellent
• Analyze performance drivers in different weather conditions
Nominal days to analyze general airport performance Degraded days to assess ANS airport reaction to weather phenomena Disrupted are assumed to be exceptional (performance drops dramatically)
Data source: METAR reportsPeriod Jan-March 2008
A dashboard of indicators evolution may help to identify drivers and understand performance at European level
On-time Departure [% flights]
40,7
47,6
31,4
30,2
0 25 50 75 100
Early Arrival (ahead scheduled) [% flights]
8,6
12,3
34,6
43,6
0 25 50 75 100
On-time Arrival [% flights]
53
62
23,9
20,7
0 25 50 75 100
Summer 08
Summer 09
+7,9%
Criteria 3 minCriteria 15 min Criteria 15 minCriteria 3 min Early Arrival 15 minEarly arrival 3 min
+7,5% +26%
0,67
0,73
0,4 0,5 0,6 0,7 0,8
Summer 09
Summer 08
Slot Utilization Efficiency Inbound Flow [min/flight]
2,6
2,5
0,2
0,1
0,6
0,3
0,6
0,5
0 1 2 3 4 5
-15%
ATFM-Other (equipment, strike, etc)
ASMA Additional Time ATFM-ATC&Aerodrome Cap
ATFM-Weather
Efficiency Outbound Flow [min/flight]
4,4
4,1
1,4
1,3
0,1
0,1
0 1 2 3 4 5 6 7
-7,7%
Departure Weather
Taxi-out Additional Time Departure Congestion
-7,7%
Conclusions and future work
An approach to measure and analyze airport-terminal joint system performance consistently across European airports
The airport scheduling process is linked to the quality of service delivered.
Working close to the limits makes the system more sensitive to variability
Last year traffic evolution seem to have lessened pressure into the ATM system, increasing quality of service
Trade-off among objectives are key to understand performance: efficiency, punctuality, predictability, environment and others
Future steps are needed to complete the picture: Consolidating the framework, addressing external affecting factors, assessing trade-offs
Jose Luis Garcia-Chico Performance Review Unit/Eurocontrol
(Aena secondment to PRU)[email protected] /
[email protected]. +32 474 123814
www.eurocontrol.int/prc
BACK UP SLIDES
TMA
Arrival airportDeparture airport
En-route ATFM delays
Airport ATFM delays
Airborne holdingReactionary
delays
Network delivery(volume and variability
of TMA entry flow)
Airport scheduling
(utilisation ratio)
Management of
arrival flows
Landing interval (actual throughput)
Local turnaround
delays
Arrival time variabilityDeparture time
variability
Pre-departuredelays
ATM
Weather
Controlling variability of air transport operations
•The airport scheduling process determines the utilisation ratio (e.g. scheduled capacity / peak capacity) and the corresponding quality of service (i.e. delays). It also tries to reduce variability
•On the day of operation, measures such as local airborne holdings or ATFM regulations are applied in order to balance variations in demand with variations in actual throughput;
•The way traffic inbound flows are managed at airports on the day of operation is an important factor to control variability and airport utilisation
STDSTA
Decrease on Service Rate due to drop of demand
• Service rate is an approximate measure of maximum airport throughput
• The metric varies with traffic demand
• 10-minute rolling hours may introduce same stability on the calculation
-6,5%
-4,2% -3,1% -2,2%-2,2%
-11,3%-9,6%
-3,0% -7,6% -3,2% -6,6% -20,7% -1,9% -6,1%-11,4% 0,0% -4,9%
-6,6%
0
20
40
60
80
100
120
140
Par
is C
harle
s-de
-Gau
lle
Mad
rid
Mun
ich
Lond
on H
eath
row
Fra
nkfu
rt
Rom
e F
ium
icin
o
Vie
nna
Bar
celo
na
Bru
ssel
s
Pal
ma
Par
is O
rly
Zur
ich
Mila
n M
alpe
nsa
Lond
on G
atw
ick
Hel
sink
i
Dub
lin
Pra
gue
Lisb
on
Nu
mb
er o
f M
ove
men
ts [
flt/
ho
ur]
2008 2009
data source : EUROCONTROL/CFMU
Calculation of unimpeded and additional times in the taxi-out phase is derived by statistical analysis of historic data
CFMU/eCODA Airport Data
AOBT
Stand Runway
ALDT
15R102Jet789456
36L155Turbo123456
RwyStanda/c classFlight ID
15R102Jet789456
36L155Turbo123456
RwyStanda/c classFlight IDStep 1:Grouping Flights
Step 2:Calculating Aircraft Congestion level 6
3
Congetion
level
15R102Jet789456
36L155Turbo123456
RwyStanda/c classFlight ID
6
3
Congetion
level
15R102Jet789456
36L155Turbo123456
RwyStanda/c classFlight ID
Step 4:Calculating Group Unimpeded Time
Step 3:Calculating Group Congestion Index
Step 5:CalculatingTaxi-out additional time
0%
2%
4%
6%
8%
10%
12%
1 4 7 10 13 16 19 22 25 28
Average
0%
2%
4%
6%
8%
10%
12%
1 4 7 10 13 16 19 22 25 28
Average
0%1%2%3%4%5%6%7%8%9%
10%
7
10 13 16 19 22 25 28 31 34 370%1%2%3%4%5%6%7%8%9%
10%
7
10 13 16 19 22 25 28 31 34 37
20P
• Group: a/c-stand-rwy
Max Throughputof Runway
• Group: a/c-stand-rwy• Unimpeded flights:Cong level < Cong Index•Truncated distribution
5
4
Congetion
Index
15R102JetB
36L155TurboA
RwyStanda/c classGroup
5
4
Congetion
Index
15R102JetB
36L155TurboA
RwyStanda/c classGroup
18 min
16 min
Unimpeded Time
1 to 5
1 to 4
Congt
levels
15R102JetB
36L155TurboA
RwyStanda/c classGroup
18 min
16 min
Unimpeded Time
1 to 5
1 to 4
Congt
levels
15R102JetB
36L155TurboA
RwyStanda/c classGroup
Unimpeded Time of Group = Average
Step 6:CalculatingAirport Taxi-outAdditional Time
Distribution of Taxiout time - Unimpeded of Group
Averaged additional time of group
0%1%2%3%4%5%6%7%8%9%
10%
7
10 13 16 19 22 25 28 31 34 37
0%1%2%3%4%5%6%7%8%9%
10%
7
10 13 16 19 22 25 28 31 34 37 18 min
16 min
Unimpeded Time
3 min
4 min
Addit
time
15R102JetB
36L155TurboA
RwyStanda/c classGroup
18 min
16 min
Unimpeded Time
3 min
4 min
Addit
time
15R102JetB
36L155TurboA
RwyStanda/c classGroup
Weighted average ofindividual groups
Airport Additional Timefor Period of Study
Index=0.5 (MaxThroup*20P group)
ATOT
Aircraft Class
CODA: Central Office for Delay Analysis
• Baseline: IFR flights
• European coverage of IFR flights > 60%
• Data coverage by airport upto 90%
• > 100 data partners (majority airlines, but also ANSP’s & airports
• Feed of flight-by-flight operational data direct from Airlines since 2000:
• AC-registration• Callsign• City-pair• Scheduled Times• OOOI-Times• Delay reasons (IATA delaycodes) and times• …
METAR contents
Weather phenomena Cloud & Ceiling
Gusts
Wind speed
Wind direction
CB/TCU
Visibility
EBBR 010420Z 31031G51KT 270V340 3000 R25L/P1500N R25R/P1500N R02/1400N +SHRAGR TS SCT008 BKN012 CB 05/04
9999 m9999 m – 5000 m4900 m – 3000 m2900 m – 1500 m1490 m – 750 m 750 m – 350 m 350 m – 0 m
Visibility
>1000 m 600 m – 400 m 400 m – 300 m 300 m – 200 m 200 m – 100 m 100 m – 50 m
Cloud base
-RA+RA-FZRA+FZRA
Precipi-tations 0 kt – 5 kt
5 kt – 8 kt 8 kt – 10 kt 10 kt – 15 kt>15 kt
Wind speed
CBTCU
ConvectiveweatherTemperature/
Dewpoint+3°C to -10°C
FreezingConditions
• METAR: observed average weather conditions measured over the preceding 10-minute period each 30 minutes
• Example:
RVR Values (m)
T° (°C°)
Example of weather classification: Wind class for steady wind direction
• Head wind component (severity)• 0 to 5 Kts; No severity; Code 1• 6 to 10 kts; moderate severity ; Code 2• 10kts to 20kts; medium severity ; Code 3• >20Kts; high severity ; Code 4
• Degraded day when duration is:• Code 2 for more than 3 consecutive hours • Code 3 for more than 2 consecutive hours • Code 4 for 1 consecutive hour or more
α
Wind
α
Wind