1. Benchmark database inhomogeneous data, surrogate data and synthetic data Victor Venema

2. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Goals of COST-HOME working group 1  Literature survey  Benchmark dataset –Known inhomogeneities –Test the homogenisation algorithms (HA)

3. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Benchmark dataset 1)Real (inhomogeneous) climate records  Most realistic case  Investigate if various HA find the same breaks  Good meta-data 2)Synthetic data  For example, Gaussian white noise  Insert know inhomogeneities  Test performance 3)Surrogate data  Empirical distribution and correlations  Insert know inhomogeneities  Compare to synthetic data: test of assumptions

4. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Creation benchmark – Outline talk 1)Start with (in)homogeneous data 2)Multiple surrogate and synthetic realisations 3)Mask surrogate records 4)Add global trend 5)Insert inhomogeneities in station time series 6)Published on the web 7)Homogenize by COST participants and third parties 8)Analyse the results and publish

5. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 1) Start with homogeneous data  Monthly mean temperature and precipitation  Later also daily data (WG4), maybe other variables  Homogeneous  No missing data  Longer surrogates are based on multiple copies  Generated networks are 100 a

6. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 1) Start with inhomogeneous data  Distribution –Years with breaks are removed –Mean of section between breaks is adjusted to global mean  Spectrum –Longest period without any breaks in the stations –Surrogate is divided in overlapping sections –Fourier coefficients and phases are adjusted for every small section –No adjustments on large scales!

7. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Surrogates from inhomogeneous data

8. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 2) Multiple surrogate realisations  Multiple surrogate realisations –Temporal correlations –Station cross-correlations –Empirical distribution function  Annual cycle removed before, added at the end  Number of stations between 5 and 20  Cross correlation varies as much as possible

9. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 5) Insert inhomogeneities in stations  Independent breaks  Determined at random for every station and time  5 breaks per 100 a  Monthly slightly different perturbations  Temperature –Additive –Size: Gaussian distribution, σ=0.8°C  Rain –Multiplicative –Size: Gaussian distribution, =1, σ=10%

10. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Example break perturbations station

11. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Example break perturbations network

12. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 5) Insert inhomogeneities in stations  Correlated break in network  One break in 50 % of networks  In 30 % of the station simultaneously  Position random –At least 10 % of data points on either side

13. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Example correlated break

14. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 5) Insert inhomogeneities in stations  Outliers  Size –Temperature: 99 percentile –Rain: 99.9 percentile  Frequency –50 % of networks: 1 % –50 % of networks: 3 %

15. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Example outlier perturbations station

16. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Example outliers network

17. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 5) Insert inhomogeneities in stations  Local trends (only temperature)  Linear increase or decrease in one station  Duration: 30, 60a  Maximum size: 0.2 to 1.5 °C  Frequency: once in 10 % of the stations  Also for rain?

18. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Example local trends

19. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 6) Published on the web  Inhomogeneous data will be published on the COST-HOME homepage  Everyone is welcome to download and homogenize the data  http://www.meteo.uni-bonn.de/ mitarbeiter/venema/themes/homogenisation

20. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 7) Homogenize by participants  Return homogenised data  Should be in COST-HOME file format (next slide)  Return break detections –BREAK –OUTLI –BEGTR –ENDTR  Multiple breaks at one data possible

21. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary 7) Homogenize by participants  COST-HOME file format: http://www.meteo.uni-bonn.de/ venema/themes/homogenisation/costhome_fileformat.pdf  For benchmark & COST homogenisation software  New since Vienna: –Stations files include height –Many clarifications

22. Victor Venema, Victor.Venema@uni-bonn.de, COST HOME, Mai 2008, Budapest, Hungary Work in progress  Preliminary benchmark: http://www.meteo.uni-bonn.de/ venema/themes/homogenisation/  Write report on the benchmark dataset  More input data  Set deadline for the availability benchmark  Deadline for the return of the homogeneous data  Agree on the details of the benchmark  Daily data: other, realistic, fair inhomogeneities