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Data Quality Control and Quality Data Quality Control and Quality MonitoringMonitoring

Jitze van der Meulen, 2013-09-11

WMO AMDAR PANEL

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Workshop on Aircraft Observing System Workshop on Aircraft Observing System Data ManagementData Management (2012) Recognize comparisons of aircraft observations to NWP

model background fields as a critical component of Aircraft Observations Quality Control (AO QC).

Consider whether or not such comparisons should be done before AO data are exchanged on the GTS.

Semi-automatic near real time monitoring information such as data counts, missing data, higher than normal rejects by the assimilation system, etc. should be exchanged regularly (monthly or more frequently as required and agreed to) between designated centres and data managers (and/or producers). This could include and Alarm/Event system.

Consideration should be given to the designation of centres to carry out international QC of Aircraft Observations (WMO and ICAO), possibly before insertion on the GTS, to flag the data.

That distribution of ICAO automated aircraft observations on the GTS be done using WMO approved format (BUFR) with an appropriate template (similar to the AMDAR ones) for clear identification of the source of the data (ADS, MODE-S, Aircraft ID, etc.).

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The AMDAR Panel has identified 20 key The AMDAR Panel has identified 20 key aspects for further developing the aspects for further developing the Aircraft Observations Quality Control Aircraft Observations Quality Control (AO QC);(AO QC);

• 3rd party data• ADS (ICAO), other new data

sources (Mode-S)• Archiving (data and

metadata)• Delivery (level II data; also

profile data for local), relation to time/place resolution)

• Optimization of observations • data targeting (additional,

for applications)• data coverage (global),

provision (e.g. Africa); programme extentions

• Developing countries, special constraints (data comm. issues)

• Data format (incl. resolution)

• Code issues (incl. data header)

• Data display• Data access• Data transfer• Typical data: Atmosph.

Composition data• Phenomena: Icing,

Turbulence, use of data (e.g. direct input, verification)

• Timeliness (taking into account Q/C processes)

• Data checking, filtering, flagging (relation with rules, M.GDPFS)

• Excluding aircraft (how to manage)

• Quality control: monitoring (availability), technics (NWP), stages (real time, off-line); flagging principles; archiving; logistics; feed back

• Metadata (definition, use, archive)

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Outline for the Definition of the Global AO DM Outline for the Definition of the Global AO DM FrameworkFramework

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Use of NWP NWP model forecast background fields are regarded as

the most appropriate references for (near) real time quality control. For operational practices it's will be necessary to evaluate these references to define the moist appropriate choices (update intervals, forecast interval), time and space interpolation techniques or algorithms.

NWP background fields as defined used for references require sufficient information on its uncertainties. Traceability to objective observations is required, providing information on its uncertainties (time and place related) and possible seasonal variations or daily characteristics (daytime/night time). In particular altitude related bias behaviour is relevant.

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Temperature differences statistics (all observations / quarter)

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Background references

For reference:

• positions: interpolation of grid point (3D)

• time: nearest time stamp of run time (analyses) or forecast (+nH), called background

• HIRLAM/HARMONIE (short range, high resolution): 00 (run), 03 (00+3H), 06 (run), 09 (06+3H), 12 (run), etc.

• ECMWF (medium term): 00 (run), 03 (00+3H), 06 (00+6H), 09 (00+9H), 12 (run –or- 00+12H), etc.

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HIRLAM/Harmonie error std

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Interpretation of differences

• Should be based on rules, defined by Int. Metrological Organizations (ICSU, BIPM)

• Statistical analyses first, then definition of parameter used for further interpretation and requirements

• Expressed in terms of uncertainty (not STD or RMSE), preferably 95% confidence interval

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Observed wind vector

Uncertainty in FF

Uncertainty in V

Uncertainty in U

Uncertainty in F (95% conf.)

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Windvector difference - expressed as scalar

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Windvector difference (median of daily means)- expressed as scalar

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Profiles: requirements, detailness

 

TA-TA[isa]

-100

0100

200

300400

500

600700

800

900

10001100

1200

13001400

1500

-30 -20 -10 0 10 20 30 40 50 60

DeltaTA / dK

PA

LT /

da

m

UUDD EU0845_20110519T1013TA-TA[isa]

-100

0100

200

300400

500

600700

800

900

10001100

1200

13001400

1500

-60 -50 -40 -30 -20 -10 0 10 20 30 40

DeltaTA / dK

PA

LT

/ d

am

UUDD EU0845_20110518T0517

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Obs data = processed data

AMDAR sensor data processing

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TA bias by aircraft typeO-B temperature difference distribution

QEvC 2004Q01

-1.0

-0.5

0.0

0.5

1.0

A-300,A-310

A-320 A-318,A-319,A-321

A-330,A-340

B-737 B-747 B-757,B-767

aircraft type

tem

pera

ture

diff

ere

nce

(K

)

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TA bias by aircraft type

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TA bias by aircraft type

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TA bias by aircraft type

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TA bias by aircraft type

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Types of errors1. Observations (air temperature, wind, humidity) are

incorrectly measured or derived (i.e. not confirming the required measurement uncertainties)

2. Incorrect position or time of observation3. Incorrect encoding (e.g. for altitude).May be reporting incorrect positions affects currently NWP most seriously, especially when reporting from (virtual) areas with few observations (data sparse areas), like over oceans and seas and especially at lower altitudes. Using NWP background fields only, usually no distinction can be made between these three types and it is assumed that the quantities are incorrectly measured only. Appropriate tools to detect horizontal and vertical positional errors are still no part of standard QC practices, although some methods are implemented (distance check between observations). The same holds for detecting inaccurate date and time stamps and latency in processing data on board (may be Mode-S data comparisons may be useful).

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Typically, some FM42 & BUFR reports show PALT, FL < pressure altitude[RWY]

Typically, some reports show only FL > altitude[RWY] or only FL > 0

The altitude issue (continued) …..

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Pressure altitude versus altitudeDelta(ISA-T)/K PALT/m P/hPa

| -1.4 K +1.2 K |

Bias: +0.1 K (median)U=1.3K

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Coverage

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PALT < 200 m

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Timeliness

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BUFR templates: confusion all aroundFM42 (only one)UDAS01 RJTD 011100 RRXAMDAR 0111ASC JP9ZX659 3532N 13949E 011024 //// PS250 046/012 TB/ S031333 F002 VG///=

BUFR Upper air aircraft:(multiple)IUAC01 RJTD 051846BUFR " Ý €Ë ð JP9Z4XW5}ÕH ¿ºwaTœü˜^5f?ÿô¥•£UuƒQd€GQù€˜D[WóÿÿJP9Z5WX9}ÕK ±�[ ucE€ŒXIuxÿÿô¥•£SsU§ÝQdÀGRk‘€˜Ä„ƒWÿÿJP9Z575Z}ÕO �±Fuil€tPIuxÿÿô¥•£E…sWÝQdð›Çj‘€™ÐI†3UÿÿÿJP9Z5WX9}ÕQ€³.Puó€|DOõr¿ÿô¥•£U•…§ÝQe éh‡-Wa€–ÖŃYçÿÿ7777

Source: Manual on the GTS, not Manual on Codes

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BUFR templates: confusion all around

11 templates for Class 11 reports: Single level report sequences (conventional data),

Recommended template: BUFR template for AMDAR, version 7 (ref. tabel 3 11 010, optionally added with 3 11 011 IAGOS template for a single observation, version 2

However all BUFR originating centres produce all different (obsolete) templates.

Profile reports are not generated

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BUFR templates: confusion all around

Source: Manual on Codes

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BUFR templates: confusion all around

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BUFR templates: confusion all around

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