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Homogenization of monthly Benchmark temperature series of network no. 3 – using ProClimDB software PowerPoint Presentation
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Homogenization of monthly Benchmark temperature series of network no. 3 – using ProClimDB software

Homogenization of monthly Benchmark temperature series of network no. 3 – using ProClimDB software

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Homogenization of monthly Benchmark temperature series of network no. 3 – using ProClimDB software

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  1. Homogenization of monthly Benchmark temperature series of network no. 3 – using ProClimDB software COST Benchmark meeting in Zürich 13-14 September 2010 – Lars Andresen

  2. Software package • AnClim • Homogeneity analysis (using txt-files) • ProClimDB • Automating the homogenization procedure (using mainly dbf-files) Petr Štěpánek

  3. Rank of monthly values Comparing with neighbours Dist. / Stand. to alt. / Outliers Replacing suspicious values Stations within 10 km Demands on data coverage Merging of different series Reference series (40 years, 10 years overlap) from correl. / weights Standardization to base station (AVG/STD) SNHT (Alexandersson test) Assessment of hom. results Reference series (10 years around inhomogeneity) from distances Standardization to base station (AVG/STD) Smoothing monthly adjustments / Demands on corr. after adjustm. Normal homogenization procedure Original Data Quality control Reconstruction of series Homogeneity testing Adjusting Data Iteration process

  4. Detecting breaks of network 3 (15 series) • Outliers removed from manipulated series • 10 outliers from 8 stations • Testing settings of ProClimDB • 40 year periods, 10 years overlap versus 20 years • Excluding breaks closer than 4 years to edge of series or to nearest break • Finding the more distinct breaks before the less distinct ones

  5. Removing outliers Station 01400 Value of 5/1978 changed from 14.8°C (outlier) to 10.8°C (true) 1976, 14.3/14.3 1977, 11.5/11.5 1978, 10.8/14.8 1979, 13.2/13.2 1980, 8.8/8.8

  6. 0.3° 0.5° 0.7° Consequences by changing overlap years – A case study, using SNHT method Single shift of +/- 0.3, 0.5, 0.7° Each pair 9 and 19 years from edge of the series Single shift of +/- 0.5° 2, 4, 9, 19 years from edge of a homogeneous temperature series of 40 years

  7. Criteria for detection • Approved • Correct year (two years involved, both correct) • Adjustment within ± 0.1 degrees, e.g. 0.5 ± 0.1 • T0 ≥ 8.1 (40 years – significance level 95%) • Nearly approved • Correct year, T0 ≥ 8.1, Adj = 0.5 ± 0.3 degrees • Correct year ± 1, T0 ≥ 8.1, Adj = 0.5 ± 0.2 • Correct year, T0 ≥ 7.0 (s.l.90%), Adj = 0.5 ± 0.1 • Fault • Significant break not approved or nearly approved

  8. Network 3 – comparing 46 breaks B: Breaks detected , M: Missing detection , F: Fault detection After 0 1 2 iterations Overlap 10 years 20 years Y_Poss ≥30 Y_Poss ≥25 Y_Poss ≥20

  9. Left: ”Official result” (46 breaks) Case study Y_Poss ≥30, 25 and 20, 2 iterations Y_Poss ≥15, no iteration

  10. Discussion – 1 Homogeneity analysis Reference series for finding breaks • Using correlations • Using distances • Weighting of neighbour values (0.5 or 1.0?) • Period (40 years) / Overlap (10 or 20 years?) Processing of results • Method (SNHT alone or in combination with others?) • Finding most probable breaks (Y_POSSIBLE). How? • Weighting of month, season, year (1, 2, 5) • Metadata (improving?) • Nearness to begin/end/other breaks (2 or 4 years?)

  11. Discussion – 2 Adjustments of the series Reference series for making adjustments • Using distance alone (limitation on distance) • Using distance and correlation (limitations on distance and correlation) Smoothing monthly adjustments • Gauss filter (0~no smoothing, 2~period of 5 values is recommended, other?) Checking correlation after adjustments • Keep smoothed adjustment if correlation improvement between candidate and neighbours (Corr+value) ≥ 0.005 or ≥ 0.000 ?

  12. Discussion – 3 Iterations • Using adjusted file for new analysis • How finding most probable breaks • More stringent criteria when automating procedure (depends on metadata and Y_POSSIBLE)?

  13. Conclusion • It is reason for concern about the high number of fault detections • Use of metadata is necessary in homogenization! Using metadata allows lower values of Y_Possible • It’s important to find the optimal conditions of a procedure before comparing methods • Homogenization has no correct answer !