Comparison of homogenization packagesapplied to monthly series of temperature and

precipitation: the MULTITEST project

J.A. Guijarro1, J.A. Lopez1, E. Aguilar2, P. Domonkos,V.K.C. Venema3, J. Sigro2 and M. Brunet2

1 State Meteorological Agency (AEMET), Spain2 Universitat Rovira i Virgili, Tarragona, Spain

3 Meteorological Institute, University of Bonn, Germany

9th Seminar for homogenization and quality control in climatologicaldatabases and 4th conference on spatial interpolation techniques in

climatology and meteorology (Budapest, 3-7 April 2017)

Outline

Introduction

Benchmarking methodology

Temperatures

Precipitations

Miscelaneous tests

Conclusions

Future work

Introduction

I The need to homogenize observational series before itsuse to assess climate variability emerged long time agoand many methodologies, some of them implemented incomputer packages, have been developed since then.

I Action COST ES0601 HOME was very useful to promotediscussion meetings of homogenization specialists and tointer-compare the performances of their methods andsoftware developments.

I Yet, as many of these programs have been improved sincethe end of that Action, new benchmarking exercises areneeded to compare their current performances.

Introduction

The results of a preliminary comparison are still shown athttp://www.climatol.eu/DARE/testhomog.html, but the Spanishproject MULTITEST (Multiple verification of automatic softwareshomogenizing monthly temperature and precipitation series)aims at updating and improving those benchmarkingexperiments in various ways:

I More realistic temperature networksI Inclusion of precipitation networks with different climatic

characteristics (Temperate, Mediterranean and Monsoonal)I More realistic inhomogeneitiesI More tested homogenization methods

Yet only automatic procedures can be tested to achievesignificant results with a reasonable effort!

Benchmarking methodology

I Data benchmarks are composed by 100 homogeneousseries with 60 years of monthly values without any missingdata. From them, 100 tests are made by:

I Randomly sampling a subset of the series (true solution)I Applying inhomogeneities to them (problem series)I Homogenizating them (backward adjustent) by different

methods (results)I Comparing the results with the true solutions, computing

RMSE, trend differences, and other metricsI Note that as these methods are applied in an automatic

way, they are run with default settings, and their resultsmay not be as optimal as when properly tuned to eachproblem network.

Methodology

Tested homogenization programs (those that we could run incompletely automatic mode):

I Climatol 3.0 (Guijarro), with constant and variablecorrections

I ACMANT 3.0 (Domonkos), versions for temperature andprecipitation (sinusoidal and irregular seasonalities)

I MASH 3.03 (Szentimrey)I RHTestV4 (Wang & Feng), absolute and relative, with or

without quantile adjustment. (Average series were given asreference!)

I USHCN v52d (Menne & Williams)(We could not compile the current version yet)

I HOMER 2.6 (Mestre et al.), with different iterationstrategies

Temperatures: networks & inhomogeneities

Generation of master networks Tm1, Tm2 and Tm3:I 100 random points on a 4 x 3◦ lon-lat areaI Mean monthly homogenized temperatures from Valladolid

(Duero basin, Spain) acting as seed seriesI Closest point is assigned the same series plus white noise

from C · N (0,1.5)I Coefficient C = 0.18,0.30,0.65 yield three master

networks with decreasing correlation between stations,called Tm1, Tm2 and Tm3

I Series shifted to account for simulated elevation, 2◦C/100yrtrend added, and annual oscillation varied ±20%

Inhomogeneities (mode ’rs’): Random number of shifts(5/100yr) with random size from N (0,1) and sinusoidalseasonality of random amplitude from N (0,0.7)

Temperatures: Correlograms

Correlograms of the first differences of the temperaturenetworks Tm1, Tm2 and Tm3:

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0 100 200 300 400

0.2

0.4

0.6

0.8

1.0

Tm1 Correlogram of first difference series

Distance (km)

Cor

rela

tion

coef

ficie

nt ●

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0 100 200 300 400

0.2

0.4

0.6

0.8

1.0

Tm2 Correlogram of first difference series

Distance (km)

Cor

rela

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coef

ficie

nt

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0 100 200 300 400

0.2

0.4

0.6

0.8

1.0

Tm3 Correlogram of first difference series

Distance (km)

Cor

rela

tion

coef

ficie

nt

Temperatures: 5 experiments

0 100 200 300 400 500 600 700

−4

−2

02

4

Months

Shi

ft (°

C)

Random position:

0 100 200 300 400 500 600 700

−4

−2

02

4Months

Shi

ft (°

C)

Random position:

0 100 200 300 400 500 600 700

−4

−3

−2

−1

01

2

Months

Shi

ft (°

C)

Short platforms and local trends

0 100 200 300 400 500 600 700

−1

01

2

All random (no seasonality)

Months

Shi

ft (°

C)

0 100 200 300 400 500 600 700

−1.

5−

1.0

−0.

50.

00.

51.

0

All random (with seasonality)

Months

Shi

ft (°

C)

Temperatures: Tm2 RMSE results

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cl1

Cl1

A3i

A3s

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm2 RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

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cl1

Cl1

A3i

A3s

MS

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RH

a

RH

A

RH

r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm2s RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

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cl1

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A3s

MS

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RH

a

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A

RH

r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm2pt RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

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cl1

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A3s

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm2r RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

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cl1

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A3s

MS

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RH

a

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r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm2rs RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

Temperatures: RMSE and trend diff. in mode ’rs’

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cl1

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A3i

A3s

MS

H

RH

a

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r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm1rs RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

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cl1

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A3i

A3s

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm2rs RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

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cl1

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A3i

A3s

MS

H

RH

a

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A

RH

r

RH

R

US

1

Hoa

Hob

Hom

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Tm3rs RMSE (°C) (Detail)

Methods

RM

SE

(°C

)

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cl1

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A3s

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hoa

Hob

Hom

−4

−2

0

2

4

Tm1rs Trend differences (°C/100 years) (Detail)

Methods

Tren

d di

ffere

nces

(°C

/100

yea

rs)

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Inh

cl1

Cl1

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A3s

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hoa

Hob

Hom

−4

−2

0

2

4

Tm2rs Trend differences (°C/100 years) (Detail)

Methods

Tren

d di

ffere

nces

(°C

/100

yea

rs)

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Inh

cl1

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MS

H

RH

a

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A

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r

RH

R

US

1

Hoa

Hob

Hom

−4

−2

0

2

4

Tm3rs Trend differences (°C/100 years) (Detail)

Methods

Tren

d di

ffere

nces

(°C

/100

yea

rs)

Precipitations

Three monthly precipitation networks were built simulatingthree different climates: Atlantic temperate (PEir),Mediterranean (PMca) and Monsoonal (PInd).

Real series from Ireland, Majorca and SW India (gridded) wererespectively used to derive variograms, gamma coefficients andfrequency of zeroes, which were used to compute theirsynthetic series by means of the R package gstat, preservingthe spatial correlation structure.

A random number of shifts (5/100yr) were introduced asfactors drawn from N (1,0.2) (in mode ’r’: no seasonalperturbation were applied to these factors)

Precipitation characteristics

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0 100 200 300 400

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1.0

Correlogram of first difference series

Distance (km)

Cor

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ficie

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0 20 40 60 800.

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Correlogram of first difference series

Distance (km)

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0 100 200 300 400 500

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0.6

0.8

1.0

Correlogram of first difference series

Distance (km)

Cor

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ficie

nt

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1 2 3 4 5 6 7 8 9 10 11 12

010

020

030

040

050

0

Monthly precipitations of PEir

Months

Pre

cipi

tatio

n (m

m)

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1 2 3 4 5 6 7 8 9 10 11 12

010

020

030

040

050

0

Monthly precipitations of PMca

Months

Pre

cipi

tatio

n (m

m)

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1 2 3 4 5 6 7 8 9 10 11 12

010

020

030

040

050

0

Monthly precipitations of PInd

Months

Pre

cipi

tatio

n (m

m)

Precipitations: RMSE and trends (’r’)●

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Inh

cl1

Cr1

Cl1

A3p

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hor

HoA

HoB

0

5

10

15

20

25

30

35

PEirr RMSE (mm) (Detail)

Methods

RM

SE

(m

m)

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Inh

cl1

Cr1

Cl1

A3p

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hor

HoA

HoB

0

5

10

15

20

25

30

35

PMcar RMSE (mm) (Detail)

Methods

RM

SE

(m

m)

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Inh

cl1

Cr1

Cl1

A3p

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hor

HoA

HoB

0

5

10

15

20

25

30

35

PIndr RMSE (mm) (Detail)

Methods

RM

SE

(m

m)

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Inh

cl1

Cr1

Cl1

A3p

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hor

HoA

HoB

−40

−20

0

20

40

PEirr Trend differences (mm/100 years) (Detail)

Methods

Tren

d di

ffere

nces

(m

m/1

00 y

ears

)

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cl1

Cr1

Cl1

A3p

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hor

HoA

HoB

−40

−20

0

20

40

PMcar Trend differences (mm/100 years) (Detail)

Methods

Tren

d di

ffere

nces

(m

m/1

00 y

ears

)

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Inh

cl1

Cr1

Cl1

A3p

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

Hor

HoA

HoB

−40

−20

0

20

40

PIndr Trend differences (mm/100 years) (Detail)

Methods

Tren

d di

ffere

nces

(m

m/1

00 y

ears

)

One only simultaneous shift in 40, 70 and 100% Tm2r

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Inh

cl1

Cl1

A3s A3i

Acs Aci

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Tm2na RMSE (°C) (all inhomogeneous stations)

Methods

RM

SE

(°C

)

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Inh

cl1

Cl1

A3s A3i

Acs Aci

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

0.0

0.5

1.0

1.5

Tm2nb RMSE (°C) (all inhomogeneous stations)

Methods

RM

SE

(°C

)

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Inh

cl1

Cl1

A3s A3i

Acs Aci

MS

H

RH

a

RH

A

RH

r

RH

R

US

1

0

2

4

6

8

10

12

14

Tm2nc RMSE (°C) (all inhomogeneous stations)

Methods

RM

SE

(°C

)

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cl1

Cl1

A3s A3i

Acs Aci

MS

H

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Methods

Tren

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6

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Methods

Tren

d di

ffere

nces

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/100

yea

rs)

Precip.: Climatol RMSE vs SNHT thresholds

RMSE obtained by Climatol (with rate normalization) on theprecipitation tests with thresholds of SNHT = 5, 10, 15, 20, 25,35 and 50 :

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PMcar RMSE (mm) (Detail)

Methods

RM

SE

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PIndr RMSE (mm) (Detail)

Methods

RM

SE

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Conclusions

I The performance of the methods can vary depending onthe characteristics of both the network and theinhomogeneities

I Hence the importance of showing results from differentnetworks that can be representative of different realclimates

I Unrealistically designed experiments also help in detectingthe strengths and weaknesses of the methods

I Precipitation appears as probably being the most difficultvariable to homogenize (many zeroes and a very biasedPDF)

I The graphics displaying the results of the tests, as well asother characteristics of the software packages shown inhttp://www.climatol.eu/tt-hom/index.html, will facilitate theuser to choose the method that better suits his needs

Future work

Ongoing and future work includes:I Test the influence of non sinusoidal seasonalities in the

shiftsI Test missing data tolerance of new packagesI Try longer series with missing data mimicking those in the

HOME benchmarkI Put all results and scripts in a web page to allow

reproducibility

Example network with missing data

0 200 400 600 800 1000 1200

−2

−1

01

d2 −

d1

1920 1940 1960 1980 20005

1015

20

TEST data availability

Time

Sta

tions

Out of the scope of the MULTITEST project:

Why not take advantage of the implemented comparison scriptsto test the performances of the methods with daily data?

It would only require:I Choose homogeneous daily networks

(Those developed by R. Killick?)I Adapt the scripts to these networksI Test the methods

(Ideally developers would provide automatic scripts that readthe problem network and yield its homogenized version. In thisway, they could test different settings of their programs.)

Acknowledgements

Project MULTITEST (CGL2014-52901-P) is funded by the SpanishMinistry of Economy and Competitiveness.

Thanks to Met Eireann for for providing the Irish monthly precipitationseries that served as model to synthesize the network of AtlanticTemperate precipitations. Mallorca monthly precipitations were takenfrom AEMET data bases, and monthly precipitations from SW India,gridded at 0.5◦ resolution, were obtained from the GlobalPrecipitation Climate Center (GPCC).

Many thanks for the advice received on the settings needed to runMASH (Tamas Szentimrey) and HOMER (Gregor Vertacnik, John Colland Stefanie Gubler).