Comparison of homogenization packages applied to monthly series of temperature and precipitation: the MULTITEST project J.A. Guijarro 1 , J.A. L´ opez 1 , E. Aguilar 2 , P. Domonkos, V.K.C. Venema 3 , J. Sigr´ o 2 and M. Brunet 2 1 State Meteorological Agency (AEMET), Spain 2 Universitat Rovira i Virgili, Tarragona, Spain 3 Meteorological Institute, University of Bonn, Germany 9 th Seminar for homogenization and quality control in climatological databases and 4 th conference on spatial interpolation techniques in climatology and meteorology (Budapest, 3-7 April 2017)
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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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Tm1 Correlogram of first difference series
Distance (km)
Cor
rela
tion
coef
ficie
nt ●
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Tm2 Correlogram of first difference series
Distance (km)
Cor
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coef
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nt
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0.4
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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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1
Hoa
Hob
Hom
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3.0
Tm2 RMSE (°C) (Detail)
Methods
RM
SE
(°C
)
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US
1
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Hob
Hom
0.0
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2.0
2.5
3.0
Tm2s RMSE (°C) (Detail)
Methods
RM
SE
(°C
)
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1
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2.5
3.0
Tm2pt RMSE (°C) (Detail)
Methods
RM
SE
(°C
)
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US
1
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2.0
2.5
3.0
Tm2r RMSE (°C) (Detail)
Methods
RM
SE
(°C
)
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1
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Hom
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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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US
1
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Hom
0.0
0.5
1.0
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2.0
2.5
3.0
Tm1rs RMSE (°C) (Detail)
Methods
RM
SE
(°C
)
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MS
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US
1
Hoa
Hob
Hom
0.0
0.5
1.0
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2.0
2.5
3.0
Tm2rs RMSE (°C) (Detail)
Methods
RM
SE
(°C
)
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MS
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1
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Hom
0.0
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2.5
3.0
Tm3rs RMSE (°C) (Detail)
Methods
RM
SE
(°C
)
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MS
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r
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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
A3i
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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cl1
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MS
H
RH
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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0.2
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1.0
Correlogram of first difference series
Distance (km)
Cor
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tion
coef
ficie
nt
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0 20 40 60 800.
00.
20.
40.
60.
81.
0
Correlogram of first difference series
Distance (km)
Cor
rela
tion
coef
ficie
nt
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Correlogram of first difference series
Distance (km)
Cor
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tion
coef
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
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Precipitations: RMSE and trends (’r’)●
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Inh
cl1
Cr1
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MS
H
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r
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R
US
1
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PEirr RMSE (mm) (Detail)
Methods
RM
SE
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MS
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A
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r
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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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cl1
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MS
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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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cl1
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A3p
MS
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RH
a
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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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a
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A
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r
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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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cl1
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Cl1
A3p
MS
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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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cl1
Cl1
A3s A3i
Acs Aci
MS
H
RH
a
RH
A
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r
RH
R
US
1
0.0
0.2
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0.6
0.8
1.0
1.2
1.4
Tm2na RMSE (°C) (all inhomogeneous stations)
Methods
RM
SE
(°C
)
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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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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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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
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01
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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).
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