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Data quality control for the ENSEMBLES grid
Evelyn Zenklusen
Michael Begert
Christof Appenzeller
Christian Häberli
Mark Liniger
Thomas Schlegel
Data Collation (KNMI)
Quality control
(KNMI, MeteoSwiss)
Tmean
Gridding (UEA, UOXFDC)
ECSN Datamanagement Workshop 2005, E. Zenklusen
What we have and what we aim at …
useful (), doubtful (), suspect ()
ECSN Datamanagement Workshop 2005, E. Zenklusen

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
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.
ECSN Datamanagement Workshop 2005, E. Zenklusen
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??
Deviation
14
12
10
References and details:
Steinacker Reinhold, Christian Häberli and Wolfgang
Pöttschacher, 2000: "A transparent method for the
analysis and quality evaluation of irregularly distributed
and noisy observational data",
Monthly Weather Review, Vol. 128, No. 7, pp. 2303-2316.
ECSN Datamanagement Workshop 2005, E. Zenklusen
8
6
4
2
0
0
5
10
15
20
VERAQC for homogenizing the
ENSEMBLES dataset
Homogeneity test
European
monthly data
(Easterling&Peterson two-phase
Regression homogeneity test
Alexandersson’s standard normal
homogeneity test)
VERAQC
“Deviations”
Significant
breakpoints
ECSN Datamanagement Workshop 2005, E. Zenklusen
Precipitation
1960-2004
VERAQC
Alexandersson
number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5()
ECSN Datamanagement Workshop 2005, E. Zenklusen
Tmin
1960-2004
VERAQC
Alexandersson
number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5()
ECSN Datamanagement Workshop 2005, E. Zenklusen
Example series: precipitation Beesel 1960-2004
Deviation series
Breakpoints detected by Easterling & Peterson
Breakpoints detected by Alexandersson
ECSN Datamanagement Workshop 2005, E. Zenklusen
Discovered limitations of VERAQC

sensitivity to changes in network density
 incomplete deviation series for some stations (example Amiandos)
ECSN Datamanagement Workshop 2005, E. Zenklusen
Changes in the station network:
Example Amiandos precipitation 1960 - 2004
Observation series:
Deviation series:
ECSN Datamanagement Workshop 2005, E. Zenklusen
Discovered limitations of VERAQC

sensitivity to changes in network density
 incomplete deviation series for some stations (example Amiandos)
 artificial breakpoints (example Andermatt)
ECSN Datamanagement Workshop 2005, E. Zenklusen
Changes in the station network:
Example Andermatt maximum temperature 1960-2004
Deviations Andermatt Tmax
Deviations Locarno Tmax
Deviations Engelberg Tmax
ECSN Datamanagement Workshop 2005, E. Zenklusen
Discovered limitations of VERAQC

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%)
ECSN Datamanagement Workshop 2005, E. Zenklusen
Precipitation
only complete series
1960-2000
VERAQC
Alexandersson
number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5()
ECSN Datamanagement Workshop 2005, E. Zenklusen
Tmin
only complete series
1960-2000
VERAQC
Alexandersson
number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5()
ECSN Datamanagement Workshop 2005, E. Zenklusen
Tmin
Difference
breakpointsall
- breakpointscomplete
1960-2000
VERAQC
Alexandersson
Lower(), equal() or higher () number of breakpoints if only
complete series are tested
ECSN Datamanagement Workshop 2005, E. Zenklusen
Skill of VERAQC:
CH-stations comparison with THOMAS
Precipitation 1960-2000, only complete series
35
number of breakpoints
30
25
20
VERAQC_ep
VERAQC_alex
15
Total amount of breakpoints
detected:
VERAQC_ep:
79
VERAQC_alex:
52
10
5
0
0-3 m
3-6 m
6-12 m
ECSN Datamanagement Workshop 2005, E. Zenklusen
false alarms missed
Skill of VERAQC:
CH-stations comparison with THOMAS
Tmin 1960-2000, only complete series
number of of breakpoints
140
120
100
80
VERAQC_ep
VERAQC_alex
60
40
Total amount of breakpoints
detected:
VERAQC_ep:
197
VERAQC_alex:
110
20
0
0-3 m
3-6 m
6-12 m
ECSN Datamanagement Workshop 2005, E. Zenklusen
false alarms
missed
Has VERAQC detected the large adjustments
and missed the small ones?
Precipitation
(mean adjustment factors
of THOMAS)
EP
SNHT
Minimum temperature
(mean adjustment amounts
of THOMAS)
detected
missed
detected
missed
21.0%
14.0%
0.81°C
0.62°C
(± 10.5)
(± 7.8)
(± 0.46)
(± 0.39)
24.0%
14.7%
0.89°C
0.61°C
(± 13.9)
(± 7.3)
(± 0.46)
(± 0.38)
ECSN Datamanagement Workshop 2005, E. Zenklusen
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)
ECSN Datamanagement Workshop 2005, E. Zenklusen
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
ECSN Datamanagement Workshop 2005, E. Zenklusen
Thank you for your attention
questions …?
ECSN Datamanagement Workshop 2005, E. Zenklusen