benchmark database inhomogeneous data, surrogate data and synthetic data

Benchmark database

inhomogeneous data, surrogate data and

synthetic data

Victor Venema

M e te o ro lo g ic a l

I n stitu te

B o n n

Victor Venema, [email protected], COST HOME, March 2009, Tarragona, Spain

Content

Introduction to benchmark dataset

Some results Some questions about exercise Question about future work Analyse and publish the results


Benchmark dataset1) Real (inhomogeneous) climate records

Most realistic case Investigate if various HA find the same breaks

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


Creation benchmark – Outline talk

1) Start with 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


1) Start with homogeneous data

Monthly mean temperature and precipitation Later also daily data (WG4), maybe other

variables (pressure, wind)

Homogeneous, no missing data Longer surrogates are based on multiple copies Generated networks are 100 a


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, 5, 9 or 15 Cross correlation varies as much as possible


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, <x>=1, σ=10%

Example break perturbations station

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Example break perturbations network

Year

Temperature perturbations

1920 1940 1960 1980 2000

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

-1

-0.5

0

0.5

1

1.5



Correlated break in network One break in 10 % of networks In 30 % of the station simultaneously Position random

– At least 10 % of data points on either side

Example correlated break

Year

Correlated break

1920 1940 1960 1980 2000

2

4

6

8

10

12

14

16

18

20-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0



Outliers Size

– Temperature: < 1 or > 99 percentile– Rain: < 0.1 or > 99.9 percentile

Frequency– 50 % of networks: 1 %– 50 % of networks: 3 %

Example outlier perturbations station

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000-10

-5

0

5Outliers

Example outliers network

Year

Outliers

1900 1920 1940 1960 1980 2000

2

4

6

8

10

12

14

16

18

20

-10

-5

0

5

10



Local trends (only temperature) Linear increase or decrease in one station Duration: between 30 and 60a Maximum size: Gaussian distribution, σ=0.8°C Frequency: once in 10 % of the stations


Example local trends

Year

Local trends

1900 1920 1940 1960 1980

2

4

6

8

10

12

14

16

18

20-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


6) Published on the web

Inhomogeneous data are published on the COST-HOME homepage

Everyone is welcome to download and homogenize the data

http://www.meteo.uni-bonn.de/ venema/themes/homogenisation


7) Homogenize by participants

Return homogenised data Should be in COST-HOME file format (next slide)

– For real data including quality flags

Return break detection file– BREAK– OUTLI– BEGTR– ENDTR

Multiple breaks at one data possible


Typical errors

The file format needs to be perfect! Forgetting the station-file that describes which

stations belong to the homogenised network Changing the file names in this station file to

homogeneous data files ► (Forgetting to return the files with the quality flags) The sizes of the breaks are not in the break file Please, keep directory structure of the benchmark

like it is, also for partial contributions – The only difference is the main directory

All files are tab-delimited ASCII files


COST-HOME file format – network file


Typical errors



homogeneous data files (Forgetting to return the files with the quality flags) The sizes of the breaks are not in the break file ► Please, keep directory structure of the benchmark


All files are tab-delimited ASCII files


Detected breaks file


Typical errors – see discussion



homogeneous data files (Forgetting to return the files with the quality flags) The sizes of the breaks are not in the break file Please, keep directory structure of the benchmark


All files are tab-delimited ASCII files ►


COST-HOME file format – monthly data


ContributionsParticipant Algorithm Remarks

1. José Guijarro Climatol 6 Versions with different settings

2. Péter Domonkos CM-D, MASH-D, NSHT-D

3 Versions / detection algorithms

3. Michele Brunetti Brunetti Detection Craddock based; 2 surrogate temp. networks

4. Dubravka Rasol & Olivier Mestre

PRODIGE All surrogate temp.; 13 surrogate precip. Networks

5. Matthew Menne & Claude Williams

Automated pairwise hom.

2 Versions; “all” temp. Networks (part of real #3 is missing)

6. Christine Gruber & Ingeborg Auer

HOCLIS 1 Surrogate temp. & 1 surrogate precip.

7. Gregor Vertacnik MASH All surrogate temp.

8. Petr Stepanek AnClim 1 Surrogate temp. & 1 surrogate precip.

9. Lucie Vincent Vincent 1 Surrogate temp.

10. Enric Aguilar NSHT Not in the right format yet


No. homogenised networks - algorithmTable 1. Number of homogenised networks per algorithm

Homogenisation alg. All networks Real netw. Surrogate netw. Synthetic netw.

PRODIGE 32 0 32 0

Brunetti 2 0 2 0

MASH 25 0 25 0

Vincent 1 0 1 0

HOCLIS 1 0 1 0

AnClim 2 0 2 0

Climatol A 92 12 40 40

Climatol C 92 12 40 40

Climatol D 92 12 40 40

Climatol E 92 12 40 40

Climatol F 92 12 40 40

ClimatolG01 2 0 2 0

APHa2 42 5 18 19

APHa1 42 5 18 19

CM-D 5 0 5 0

MASH-D 5 0 5 0

SNHT-D 5 0 5 0


No. homogenised networks – input data

Table 3. Summary data: Number of homogenised networks per network

Network No. networks Temp. netw. Precip. netw.

All 624 371 253

Real 70 40 30

Surrogate 316 193 123

Surrogate #1 29 17 12

Surrogate ~#1 287 176 111

Synthetic 238 138 100

Synthetic #1 12 7 5

Synthetic ~#1 226 131 95


Mean no. outliers per stationTable 21. Mean number of outliers per station for every algorithm


PRODIGE 0.0 NaN 0.0 NaN

Brunetti 3.4 NaN 3.4 NaN

MASH 16.1 NaN 16.1 NaN

Vincent 0.0 NaN 0.0 NaN

HOCLIS 6.0 NaN 6.0 NaN

AnClim 5.5 NaN 5.5 NaN

Climatol A 3.6 0.2 4.0 4.2

Climatol C 88.5 56.4 94.8 91.9

Climatol D 54.4 34.7 58.0 56.7

Climatol E 54.2 31.8 60.7 54.4

Climatol F 43.8 33.2 47.8 42.9

ClimatolG01 4.4 NaN 4.4 NaN

APHa2 1.9 0.0 2.1 2.2

APHa1 1.9 0.0 2.1 2.2

CM-D 15.7 NaN 15.7 NaN

MASH-D 15.5 NaN 15.5 NaN

SNHT-D 15.3 NaN 15.3 NaN


Mean no. breaks per stationTable 22. Mean number of breaks per station for every algorithm


PRODIGE 2.7 NaN 2.7 NaN

Brunetti 5.0 NaN 5.0 NaN

MASH 4.6 NaN 4.6 NaN

Vincent 0.0 NaN 0.0 NaN

HOCLIS 2.8 NaN 2.8 NaN

AnClim 1.2 NaN 1.2 NaN

Climatol A 1.3 0.8 1.5 1.3

Climatol C 1.5 0.8 1.6 1.6

Climatol D 1.4 1.0 1.5 1.4

Climatol E 1.6 0.9 1.8 1.6

Climatol F 1.5 1.2 1.7 1.4

ClimatolG01 1.2 NaN 1.2 NaN

APHa2 1.8 1.2 2.1 1.7

APHa1 1.7 1.0 1.9 1.6

CM-D 4.6 NaN 4.6 NaN

MASH-D 3.9 NaN 3.9 NaN

SNHT-D 3.2 NaN 3.2 NaN


Homogenising the exercise Tab-delimited files: also space-delimited?

– Mixture of strings and numbers Data quality files only for real data section Do we want to use the Diurnal Temperature

Range (DTR)?– Not useful for surrogate and synthetic data!– If we do, everyone should do it

End or begin uncorrected?– Compute statistics independent of absolute level?

Filling missing values part exercise? Human quality control or raw algorithm output? Homogenise all or homogenisable networks, times


Contributions – who is missing?Participant Algorithm Remarks

1. José Guijarro Climatol 6 Versions with different settings

2. Péter Domonkos CM-D, MASH-D, NSHT-D

3 Versions / detection algorithms

3. Michele Brunetti Brunetti Detection Craddock based; 2 surrogate temp. networks

4. Dubravka Rasol & Olivier Mestre

PRODIGE All surrogate temp.; 13 surrogate precip. Networks

5. Matthew Menne & Claude Williams

Automated pairwise hom.

2 Versions; “all” temp. Networks (part of real #3 is missing)

6. Christine Gruber & Ingeborg Auer

HOCLIS 1 Surrogate temp. & 1 surrogate precip.

7. Gregor Vertacnik MASH All surrogate temp.

8. Petr Stepanek AnClim 1 Surrogate temp. & 1 surrogate precip.

9. Lucie Vincent Vincent 1 Surrogate temp.

10. Enric Aguilar NSHT Not in the right format yet


Analysing the results What measures define a well homogenised

dataset?– Real data vs. data with known truth

Ensemble mean for real data?

– Breaks Position, hit rate size distribution detection probability as function of size

– Data itself Root mean square error (RMSE) RMSE (without outliers) RMSE (bias corrected) Uncertainty in the network mean trend

How to study which components are best?


Deadline(s)

Agreed on 09/2009, September this year Multiple deadlines

– For example: synthetic data, real data, surrogate data– After deadline the truth can be revealed– After deadline the other contributions can be

revealed(?)– Start earlier analysing the results– For example: May, July, September

Bologna, 25 – 26 May, EGU, 19 – 24 April


Articles Articles

– Overview COST Action & benchmark with very basic analysis results Performance difference between synthetic (Gaussian, white

noise) and surrogate data How to deal multiple contributions per algorithm? Do we have references to all algorithms?

– What should the others be about Analysing results, which components are best

Who will organise, coordinate it?– Not everyone should do the same analysis– How to subdivide the work?

After deadline: sensitivity analysis

benchmark database inhomogeneous data, surrogate data and synthetic data

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