Data quality control for the ENSEMBLES grid Evelyn Zenklusen Michael Begert Christof Appenzeller Christian Häberli Mark Liniger Thomas Schlegel

ECSN Datamanagement Workshop 2005, E. Zenklusen Data Collation (KNMI) Gridding (UEA, UOXFDC) T mean Quality control (KNMI, MeteoSwiss )

ECSN Datamanagement Workshop 2005, E. Zenklusen 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 )  Additional goals: date the breakpoints homogeneous subperiods separate information for each climate variable useful ( ), doubtful ( ), suspect ( )

ECSN Datamanagement Workshop 2005, E. Zenklusen 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:

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?? 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 Deviation

ECSN Datamanagement Workshop 2005, E. Zenklusen European monthly data VERAQC for homogenizing the ENSEMBLES dataset Significant breakpoints “Deviations” Homogeneity test ( Easterling&Peterson two-phase Regression homogeneity test Alexandersson’s standard normal homogeneity test) VERAQC

ECSN Datamanagement Workshop 2005, E. Zenklusen number of breakpoints detected: 0( ), 1( ), 2( ), 3( ), 4( ), >5( ) Precipitation VERAQC Alexandersson

ECSN Datamanagement Workshop 2005, E. Zenklusen number of breakpoints detected: 0( ), 1( ), 2( ), 3( ), 4( ), >5( ) Tmin VERAQC Alexandersson

ECSN Datamanagement Workshop 2005, E. Zenklusen Example series: precipitation Beesel Breakpoints detected by Easterling & Peterson Deviation series 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 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 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 (network density of complete climate series is high) Precipitation: 795 stations (~55%) Tmin: 527 stations (~60%)

ECSN Datamanagement Workshop 2005, E. Zenklusen number of breakpoints detected: 0( ), 1( ), 2( ), 3( ), 4( ), >5( ) Precipitation only complete series VERAQC Alexandersson

ECSN Datamanagement Workshop 2005, E. Zenklusen number of breakpoints detected: 0( ), 1( ), 2( ), 3( ), 4( ), >5( ) Tmin only complete series VERAQC Alexandersson

ECSN Datamanagement Workshop 2005, E. Zenklusen Lower( ), equal( ) or higher ( ) number of breakpoints if only complete series are tested Precipitation Difference breakpoints all - breakpoints complete VERAQC Alexandersson

ECSN Datamanagement Workshop 2005, E. Zenklusen Lower( ), equal( ) or higher ( ) number of breakpoints if only complete series are tested Tmin Difference breakpoints all - breakpoints complete VERAQC Alexandersson

ECSN Datamanagement Workshop 2005, E. Zenklusen Skill of VERAQC: CH-stations comparison with THOMAS 0-3 m 3-6 m6-12 m false alarms missed Precipitation , only complete series number of breakpoints Total amount of breakpoints detected: VERAQC_ep:79 VERAQC_alex:52

ECSN Datamanagement Workshop 2005, E. Zenklusen Skill of VERAQC: CH-stations comparison with THOMAS 0-3 m 3-6 m6-12 m false alarms missed Tmin , only complete series number of of breakpoints Total amount of breakpoints detected: VERAQC_ep:197 VERAQC_alex:110

ECSN Datamanagement Workshop 2005, E. Zenklusen Has VERAQC detected the large adjustments and missed the small ones? Precipitation (mean adjustment factors of THOMAS) Minimum temperature (mean adjustment amounts of THOMAS) detectedmisseddetectedmissed 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)

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 …?