data quality control for the ensembles grid

Data quality control for the ENSEMBLES grid

Evelyn ZenklusenMichael Begert

Christof Appenzeller Christian HäberliMark LinigerThomas Schlegel

ECSN Datamanagement Workshop 2005, E. Zenklusen

Data Collation (KNMI)

Gridding (UEA, UOXFDC)

Tmean

Quality control (KNMI, MeteoSwiss)


What we have and what we aim at …

Methods based on ECA&D experience:

implemented statement if series are

homogeneous or not for a given period (e.g.1946-1999)

Additional goals: date the breakpoints homogeneous subperiods separate information for each

climate variable

useful (), doubtful (), suspect ()


THOMAS(Tool for Homogenization of Monthly Data Series at MeteoSwiss)

Pro: Twelve different homogeneity tests implemented Includes full station history Based on monthly time series but daily output resolution possible

Contra: Includes a lot of manual work (construction of reference series,

interpretation of test results) not suited for large datasets (ENSEMBLES)

But: the Swiss series homogenized by THOMAS provide a highly valuable

core dataset for the testing in ENSEMBLES

Reference and details:Begert Michael, Schlegel Thomas and Kirchhofer Walther, 2005: “Homogenous temperature and precipitation series of Switzerland from 1864 to 2000”, Int. J. Climatol. 25: 65-80.


VERAQC (Vienna Enhanced Resolution Analysis Quality Control at Univ. Vienna)

Pro: based on objective spatial interpolation designed for quality control applied at MeteoSwiss on daily data idea: use VERAQC-output for

homogenization

Contra: Not yet tested. - Does it work??

References and details:Steinacker Reinhold, Christian Häberli and WolfgangPöttschacher, 2000: "A transparent method for the analysis and quality evaluation of irregularly distributedand noisy observational data", Monthly Weather Review, Vol. 128, No. 7, pp. 2303-2316.

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Deviation


European monthly data

VERAQC for homogenizing the ENSEMBLES dataset

Significantbreakpoints

“Deviations”

Homogeneity test(Easterling&Peterson two-phase

Regression homogeneity test

Alexandersson’s standard normalhomogeneity test)

VERAQC


number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5()

Precipitation

1960-2004

VERAQCAlexandersson



Tmin

1960-2004

VERAQCAlexandersson


Example series: precipitation Beesel 1960-2004

Breakpoints detected by Easterling & Peterson

Deviation series

Breakpoints detected by Alexandersson


Discovered limitations of VERAQC

sensitivity to changes in network density incomplete deviation series for some stations (example Amiandos)


Changes in the station network:Example Amiandos precipitation 1960 - 2004

Observation series:

Deviation series:



sensitivity to changes in network density incomplete deviation series for some stations (example Amiandos) artificial breakpoints (example Andermatt)


Changes in the station network:Example Andermatt maximum temperature 1960-2004

Deviations Andermatt Tmax

Deviations Locarno Tmax

Deviations Engelberg Tmax



sensitivity to changes in network density incomplete deviation series for some stations (example Amiandos) artificial breakpoints (example Andermatt)

One step further to test the process… analyse only complete station series of a desired period

e.g. 1960-2000 (network density of complete climate series is high) Precipitation: 795 stations (~55%) Tmin: 527 stations (~60%)



Precipitationonly complete series

1960-2000

VERAQCAlexandersson



Tminonly complete series

1960-2000

VERAQCAlexandersson


Lower(), equal() or higher () number of breakpoints if only complete series are tested

Tmin

Difference breakpointsall

- breakpointscomplete

1960-2000

VERAQCAlexandersson


Skill of VERAQC:CH-stations comparison with THOMAS

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VERAQC_epVERAQC_alex

0-3 m 3-6 m 6-12 m false alarms missed

Precipitation 1960-2000, only complete series

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f bre

akpo

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Total amount of breakpointsdetected:VERAQC_ep: 79VERAQC_alex: 52


Skill of VERAQC:CH-stations comparison with THOMAS

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VERAQC_epVERAQC_alex

0-3 m 3-6 m 6-12 m false alarms missed

Tmin 1960-2000, only complete series

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ber o

f of

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akpo

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Total amount of breakpointsdetected:VERAQC_ep: 197VERAQC_alex: 110


Has VERAQC detected the large adjustments and missed the small ones?

Precipitation(mean adjustment factors

of THOMAS)

Minimum temperature(mean adjustment amounts

of THOMAS)

detected missed detected missed

EP 21.0%(± 10.5)

14.0%(± 7.8)

0.81°C(± 0.46)

0.62°C(± 0.39)

SNHT 24.0%(± 13.9)

14.7%(± 7.3)

0.89°C(± 0.46)

0.61°C(± 0.38)


Summary and conclusions

ECA&D procedure is implemented and works With VERAQC an automated homogeneity test procedure

has been implemented and tested method shows unsatisfying results

significant loss of stations at the edge of investigated area sensitive to changes in the network density high number of undetected inhomogeneities and false alarms sensitive to inhomogeneities in “reference series”

(dispersion of inhomogeneities)


Outlook

Two ways to proceed: Improvement of VERAQC test procedure

reduce influences of the varying network density(anomalies as inputdata, flag breakpoints generated by network changes)

reduce false alarm rate(combination of test results, test tuning)

Calculation of deviation series according to THOMAS procedure

selection of reference stations due to correlation analysis use a mean of chosen reference series to calculate the deviations


Thank you for your attention

questions …?

data quality control for the ensembles grid

Documents

observation series

zenklusenexample series

swiss series

monthly time series

station network

example amiandos precipitation

data quality control

zenklusennumber of breakpoints