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Data quality control for the ENSEMBLES grid

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 ). T mean. Gridding (UEA, UOXFDC). What we have and what we aim at ….

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Data quality control for the ENSEMBLES grid

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

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

  3. 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 () ECSN Datamanagement Workshop 2005, E. Zenklusen

  4. 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

  5. Deviation 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. 2303-2316. ECSN Datamanagement Workshop 2005, E. Zenklusen

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

  7. Precipitation 1960-2004 VERAQC Alexandersson number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5() ECSN Datamanagement Workshop 2005, E. Zenklusen

  8. Tmin 1960-2004 VERAQC Alexandersson number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5() ECSN Datamanagement Workshop 2005, E. Zenklusen

  9. Example series: precipitationBeesel 1960-2004 Deviation series Breakpoints detected by Easterling & Peterson Breakpoints detected by Alexandersson ECSN Datamanagement Workshop 2005, E. Zenklusen

  10. Discovered limitations of VERAQC • sensitivity to changes in network density • incomplete deviation series for some stations (example Amiandos) ECSN Datamanagement Workshop 2005, E. Zenklusen

  11. Changes in the station network:Example Amiandos precipitation 1960 - 2004 Observation series: Deviation series: ECSN Datamanagement Workshop 2005, E. Zenklusen

  12. 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

  13. 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

  14. 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

  15. Precipitation only complete series 1960-2000 VERAQC Alexandersson number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5() ECSN Datamanagement Workshop 2005, E. Zenklusen

  16. Tmin only complete series 1960-2000 VERAQC Alexandersson number of breakpoints detected: 0(), 1(), 2(), 3(), 4(), >5() ECSN Datamanagement Workshop 2005, E. Zenklusen

  17. 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

  18. 0-3 m 3-6 m 6-12 mfalse alarms missed Skill of VERAQC:CH-stations comparison with THOMAS Precipitation 1960-2000, only complete series number of breakpoints Total amount of breakpoints detected: VERAQC_ep: 79 VERAQC_alex: 52 ECSN Datamanagement Workshop 2005, E. Zenklusen

  19. 0-3 m 3-6 m 6-12 m false alarms missed Skill of VERAQC:CH-stations comparison with THOMAS Tmin 1960-2000, only complete series number of of breakpoints Total amount of breakpoints detected: VERAQC_ep: 197 VERAQC_alex: 110 ECSN Datamanagement Workshop 2005, E. Zenklusen

  20. Has VERAQC detected the large adjustments and missed the small ones? ECSN Datamanagement Workshop 2005, E. Zenklusen

  21. 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

  22. 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

  23. Thank you for your attention questions …? ECSN Datamanagement Workshop 2005, E. Zenklusen

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