-
ORI GIN AL PA PER
ROCADA: a gridded daily climatic datasetover Romania (19612013)
for nine meteorologicalvariables
Alexandru Dumitrescu1 Marius-Victor Birsan1
Received: 14 June 2014 / Accepted: 9 April 2015 Springer
Science+Business Media Dordrecht 2015
Abstract Daily records of nine meteorological variables covering
the interval 19612013were used in order to create a
state-of-the-art homogenized climatic dataset over Romania at a
spatial resolution of 0.1. All meteorological stations with full
data records, as well as stationswith up to 30 % missing data, were
used for the following variables: air pressure (150
stations); minimum, maximum, and average air temperature (150
stations); soil temperature
(127 stations); precipitation (188 stations); sunshine hours
(135 stations); cloud cover (104
stations); relative humidity (150 stations). For each parameter,
the data series were first
homogenized with the software MASH (Multiple Analysis of Series
for Homogenization);
then, the data series were gridded by means of the software MISH
(Meteorological Inter-
polation based on Surface Homogenized Data). The datasets are
freely available on request on
the PANGAEA data portal
(doi.pangaea.de/10.1594/PANGAEA.833627).
Keywords ROCADA Climatic dataset Gridded data Homogenization
Spatial interpolation MASH MISH Romania
1 Introduction
The realization and availability of high-quality climatic data
are essential for the realistic
assessment of the impacts of climate variability and change on a
region. The creation of
gridded datasets by means of interpolation techniques using the
local data values (i.e.,
Electronic supplementary material The online version of this
article (doi:10.1007/s11069-015-1757-z)contains supplementary
material, which is available to authorized users.
& Marius-Victor [email protected];
[email protected]
Alexandru [email protected];
[email protected]
1 Department of Climatology, Meteo Romania (National
Meteorological Administration), Sos.Bucuresti-Ploiesti 97, 013686
Bucharest, Romania
123
Nat HazardsDOI 10.1007/s11069-015-1757-z
-
weather stations records) allows the estimation of the
climatological characteristics for
locations or areas where measurements are not available (Sluiter
2012). Gridded data are
essential for evaluating the performance of regional climate
models, and they serve as
input data for spatially distributed agrometeorological and
hydrological models (e.g.,
Tveito et al. 2006; Birsan 2013).
Within the framework of the project CARPATCLIM (Climate of the
Carpathian Re-
gion), funded by the EU Joint Research Centre, the national
meteorological services from
nine countries (Romania, Hungary, Ukraine, Slovakia, Serbia,
Poland, Czech Republic,
Croatia and Austria) joined efforts in order to build a daily
gridded climatology of the
Carpathian Mountains region, with Romania covering over
one-third of the area. The
project represented a good opportunity to update the data
inventory (together with their
gaps, quality and homogeneity), and its scientific usefulness
has already been proved by
several studies (e.g., Lakatos et al. 2013; Spinoni et al. 2014;
Birsan et al. 2014a; Cheval
et al. 2014a).
Here, we present the realization and evaluation of a daily
gridded climatic dataset over
Romania (ROCADAROmanian ClimAtic DAtaset), for nine
meteorological variables,
and spanning over the period 19612013. We applied the same
homogenization and
gridding software package used in the CARPATCLIM project, due to
its tested perfor-
mance, reliability and speed. A comparison with the E-OBS
(Haylock et al. 2008) and
APHRODITE (Yatagai et al. 2012) datasets and with raw time
series from some inde-
pendent weather stations (i.e., stations that were not involved
in the creation of any of the
two gridded datasets) is also presented.
Several recent studies have dealt with the climatic changes over
Romania, regarding not
only the increase in precipitation and air temperature extremes
(e.g., Ionita et al. 2012;
Busuioc et al. 2014; Rimbu et al. 2014; Stefanescu et al. 2014),
but also the terrestrial
stilling (Birsan et al. 2013), seasonal or annual changes in
relative humidity, cloud cover,
number of sunshine hours (Dumitrescu et al. 2014; Marin et al.
2014), the snow pack
decrease (Birsan and Dumitrescu 2014a; Micu 2009), as well as
changes in streamflow
regime (Birsan et al. 2012, 2014; Birsan 2015; Ionita et al.
2014; Ionita 2015),
evapotranspiration (Croitoru et al. 2013) or drought (Cheval et
al. 2014b). These studies
demonstrate the importance of making available a
quality-controlled, daily climatic dataset
covering the entire Romania and extending over a long period of
time.
2 Location and data
Covering an area of 238 391 km2, Romania is the largest country
in southeastern Europe.
The terrain is fairly equally distributed between mountainous
(Carpathians), hilly and
lowland territories. Elevation varies from sea level to 2544
m.a.s.l. It has a continental
temperate climate with various influences: oceanic in the
central and western parts, con-
tinental in the east and Mediterranean in the south. The
agricultural land covers 62 % of
Romania, while forested areas occupy about one-third of the
country (e.g., Balteanu et al.
2010).
The construction of the gridded dataset was realized using daily
time series with full
data record over 19612013, as well as some additional data
series with at least 70 % data
completeness (for a better spatial coverage), for nine
meteorological variables, namely air
pressure (mb): 150 stations; minimum, maximum and average air
temperature (C): 150stations; soil temperature (C): 127 stations;
precipitation (mm): 188 stations and rain
Nat Hazards
123
-
gauges; number of sunshine hours (h): 135 stations; cloud cover
(scale 110): 104 stations;
relative humidity (%): 150 stations.
All time series were extracted from the climatic database of the
Romanian National
Meteorological Administration (Meteo Romania). The related
weather stations are located
at elevations ranging from 1 to 2506 m.a.s.l. and have a good
coverage both spatially and
with respect to elevation (Fig. 1). The stations characteristics
and their data record com-
pleteness for each parameter are listed in Table 1.
3 Methods
For each parameter, the station data series were homogenized
with the software MASH
v3.03 (Multiple Analysis of Series for Homogenization),
formulated and developed by
Szentimrey (1999). MASH is a relative homogenization method that
makes no a priori
assumption regarding the data homogeneity, and it uses an
exhaustive searching scheme to
detect the most probable break and shift points in the data
series from each weather station.
Within MASH, data completion and quality control are performed
automatically. The
distribution of the examined meteorological element is taken
into account for using an
additive model (e.g., for temperature) or a multiplicative one
(e.g., for precipitation), while
corrections are applied to the inhomogeneous series until no
break is found. The ho-
mogenization of daily data uses the parameterization results
obtained from monthly data
homogenization (Szentimrey 2008, 2011).
In a study comparing several widely used homogenization methods,
Costa and Soares
(2009) found the MASH method to be one of the most comprehensive
procedures for
homogenization. The quality of the homogenized data series is
evaluated by the joint
Fig. 1 Spatial distribution of meteorological station involved
in this work
Nat Hazards
123
-
Table
1L
ist
of
the
stat
ion
sin
vo
lved
inth
ean
aly
sis,
incl
ud
ing
thei
rst
atio
nco
de,
loca
tio
n,
elev
atio
nan
dco
mp
lete
nes
so
fth
ed
ata
reco
rdfo
ral
lp
aram
eter
s
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
1A
dam
clis
i4
08
80
00
16
0.4
12
00
27
.96
71
34
4.0
88
62
15
7.1
2A
dju
d6
06
70
51
10
0.5
11
64
12
7.1
71
81
46
.10
50
21
03
.3
3A
lex
and
ria
35
95
21
00
00
01
02
5.3
54
37
43
.97
82
97
4.0
4A
rad
60
81
21
00
00
01
02
1.3
55
22
46
.13
38
51
15
.6
5A
rdu
sat
73
93
22
7
2
3.3
66
67
47
.65
00
01
60
.5
6A
ries
eni
62
82
46
5
2
2.7
66
67
46
.46
66
78
63
.9
7A
vra
mIa
ncu
-Var
furi
62
22
50
2
2
2.8
33
33
46
.36
66
79
23
.2
8A
vra
men
i8
05
65
72
9
28
.72
92
92
92
92
6.9
50
00
48
.08
33
32
34
.3
9B
acau
63
56
58
02
00
00
02
6.9
14
07
46
.53
21
51
85
.3
10
Bai
a-M
are
740330
01
00
10
023.4
9324
47.6
6121
196.8
11
Bai
lest
i4
01
32
10
00
00
00
23
.33
26
24
4.0
29
51
56
.2
12
Bai
soar
a6
34
32
20
30
00
0
23
.31
18
24
6.5
35
77
13
19.3
13
Ban
loc
52
31
08
01
00
00
02
1.1
37
97
45
.38
30
58
0.1
14
Bar
aolt
60
55
37
0
00
16
02
5.5
97
40
46
.08
10
45
06
.9
15
Bas
esti
72
83
10
8
2
3.1
66
67
47
.46
66
71
89
.6
16
Bec
het
34
73
57
00
00
06
02
3.9
45
69
43
.79
00
63
9.3
17
Ber
zeas
ca4
39
15
72
8
28
.22
8
2
82
1.9
50
00
44
.65
00
08
4.7
18
Bac
les
42
83
07
11
51
.82
26
22
3.1
14
58
44
.47
67
23
13
.2
19
Bar
lad
61
47
40
00
00
01
02
7.6
45
58
46
.23
13
61
60
.4
20
Bis
trit
a7
08
43
00
00
01
00
24
.51
54
54
7.1
49
42
37
1.1
21
Bla
j6
11
35
50
13
0.2
01
60
23
.93
67
74
6.1
78
82
27
5.8
22
Bo
ita
53
84
16
01
20
.41
65
02
4.2
73
18
45
.65
31
84
84
.8
23
Bo
iuM
are
72
43
35
8
2
3.5
83
33
47
.40
00
04
26
.6
24
Bo
rod
65
92
36
19
1
8.9
19
19
19
19
22
.59
16
14
6.9
93
92
33
4.7
25
Bo
tiza
74
04
09
8
2
4.1
50
00
47
.66
66
75
06
.4
Nat Hazards
123
-
Table
1co
nti
nued
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
26
Bo
tosa
ni
74
16
40
00
00
00
02
6.6
47
04
47
.73
60
51
22
.1
27
Bo
zov
ici
45
52
00
6
5.7
61
11
06
22
.00
77
44
4.9
18
65
25
0.1
28
Bra
ila
51
27
55
25
2
62
62
72
52
62
7.9
21
19
45
.20
69
91
2.1
29
Bra
sov
54
25
32
00
00
00
02
5.5
27
72
45
.69
61
35
38
.4
30
Bu
cure
sti
Afu
mat
i4
30
61
31
31
71
4.7
15
13
14
15
26
.21
42
94
4.5
00
39
81
.5
31
Bu
cure
sti
Ban
easa
43
06
08
02
00
02
02
6.0
79
76
44
.51
08
29
0.9
32
Bu
cure
sti
Fil
aret
42
56
06
02
0.2
0
00
26
.09
53
24
4.4
12
36
85
.9
33
Bu
zau
50
96
49
00
00
30
02
6.8
53
00
45
.13
29
19
0.0
34
Bu
zias
53
91
36
3
2
1.6
00
00
45
.65
00
01
24
.5
35
Cac
ica
738554
8
25.9
0000
47.6
3333
477.1
36
Cal
afat
359257
05
00
00
022.9
4757
43.9
8525
61.5
37
Cal
aras
i4
12
72
10
30
00
00
27
.33
98
64
4.2
06
43
17
.1
38
Car
acal
40
64
21
06
00
10
02
4.3
58
81
44
.10
04
41
10
.2
39
Car
anse
bes
52
52
15
00
00
00
02
2.2
26
84
45
.41
75
62
14
.4
40
Cea
hla
uT
oac
a6
56
55
56
65
.56
6
62
5.9
51
51
46
.97
77
61
82
3.1
41
Cea
hla
u-S
at7
02
55
62
8
27
.82
82
82
92
82
5.9
33
33
47
.03
33
37
09
.9
42
Ch
isin
euC
ris
63
21
30
17
11
73
12
1.5
43
00
46
.51
89
49
2.9
43
Cam
pen
i(B
istr
a)6
22
30
34
5
.15
17
5
23
.04
19
54
6.3
64
10
59
5.6
44
Cam
pin
a5
17
54
50
19
0.4
11
21
02
5.7
53
95
45
.14
44
84
77
.5
45
Cam
pu
lun
g5
17
50
70
30
03
40
25
.03
81
44
5.2
75
15
68
4.7
46
Clu
j-N
apoca
647334
00
00
01
023.5
7299
46.7
7799
404.2
47
Cam
pu
lun
gM
old
ov
enes
c7
32
53
42
5
24
.52
5
28
25
25
.56
66
74
7.5
33
33
64
1.4
48
Co
jocn
a6
45
35
09
23
.83
33
34
6.7
50
00
33
5.2
49
Co
nst
anta
41
38
38
00
00
16
16
02
8.6
46
38
44
.21
40
91
7.8
50
Co
rug
ea4
44
82
00
1
.64
50
12
8.3
43
58
44
.73
47
02
21
.3
Nat Hazards
123
-
Table
1co
nti
nued
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
51
Co
tnar
i7
22
65
70
00
00
20
26
.92
74
14
7.3
58
67
27
4.7
52
Cra
iov
a4
14
35
20
00
00
00
23
.86
84
64
4.3
10
60
18
5.9
53
Cru
cea
(Co
nst
anta
)4
32
81
47
28
.23
33
34
4.5
33
33
91
.8
54
Cu
ntu
51
82
31
0
0.4
0
0
22
.50
30
54
5.3
00
81
14
65.4
55
Cu
rtea
de
Arg
es5
09
44
10
00
00
00
24
.67
12
84
5.1
79
17
45
3.2
56
Dal
ga
42
57
02
26
2
6.3
27
27
28
27
27
.03
33
34
4.4
16
67
45
.0
57
Ded
ule
sti
50
04
32
07
00
9
02
4.5
71
79
45
.01
62
95
51
.2
58
Dej
70
93
52
07
00
00
02
3.9
00
50
47
.12
82
62
36
.6
59
Dev
a5
53
25
40
00
01
00
22
.90
04
64
5.8
65
04
24
1.0
60
Do
bra
(Alb
a)5
46
33
97
23
.65
00
04
5.7
66
67
50
7.3
61
Do
roh
oi
75
66
25
28
2
8.6
29
29
29
29
26
.41
66
74
7.9
33
33
22
3.5
62
Dro
bet
a-T
urn
uS
ever
in438238
00
00
00
022.6
2765
44.6
2673
60.1
63
Dra
gas
ani
44
44
17
03
00
00
02
4.2
38
71
44
.66
57
62
63
.5
64
Du
mb
rav
eni
61
44
36
04
0.1
09
20
24
.59
31
84
6.2
28
25
32
0.0
65
Du
mit
ra7
13
42
88
24
.46
66
74
7.2
16
67
35
5.0
66
Fag
aras
55
14
59
01
20
.20
00
02
4.9
36
72
45
.83
63
64
29
.3
67
Fal
tice
ni
72
86
20
1
29
.83
02
9
29
26
.33
33
34
7.4
66
67
26
2.2
68
Fau
rei
50
57
19
25
2
4.5
25
25
25
25
27
.31
66
74
5.0
83
33
38
.7
69
Fet
esti
42
27
51
8
99
17
99
27
.84
04
84
4.3
91
78
52
.7
70
Fo
csan
i5
41
71
23
0
29
.83
0
30
30
27
.20
12
94
5.6
87
88
49
.1
71
Fo
roti
c5
14
13
58
21
.58
33
34
5.2
33
33
13
8.1
72
Fu
nd
ata
52
85
18
00
00
5
02
5.2
73
27
45
.43
19
11
33
0.7
73
Fu
nd
ule
a4
28
63
20
16
00
00
02
6.5
25
05
44
.45
32
36
5.0
74
Gal
ati
53
08
01
00
00
01
02
8.0
33
93
45
.47
33
06
7.1
75
Gar
nic
44
51
48
10
21
.80
00
04
4.7
50
00
48
7.6
Nat Hazards
123
-
Table
1co
nti
nued
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
76
Giu
rgiu
35
25
57
00
00
61
02
5.9
34
22
43
.87
54
72
3.2
77
Go
rgo
va
51
19
12
0
00
02
02
9.1
58
27
45
.17
71
10
.6
78
Gri
vit
a4
45
71
80
00
03
00
27
.29
59
94
4.7
40
96
43
.8
79
Gu
rah
on
t6
17
22
09
9
.71
01
71
11
02
2.3
34
90
46
.27
95
11
62
.2
80
Hau
zest
i5
43
20
95
22
.15
00
04
5.7
16
67
29
9.7
81
Har
sov
a4
41
75
70
0
.71
32
12
7.9
65
08
44
.69
20
03
2.0
82
Ho
lod
64
62
07
13
14
13
.11
31
71
41
32
2.1
13
87
46
.78
89
01
55
.8
83
Hu
edin
65
13
05
3
4.4
51
29
42
3.0
34
12
46
.85
76
55
57
.7
84
Hu
si6
41
80
32
8
27
.82
82
82
82
82
8.0
50
00
46
.68
33
31
35
.0
85
Iasi
71
07
36
00
00
00
02
7.6
30
08
47
.17
10
69
5.0
86
Ieze
r7
37
43
90
50
09
0
24
.65
07
34
7.6
02
83
17
90.3
87
Igh
iu6
09
33
17
23
.51
66
74
6.1
50
00
28
6.0
88
Into
rsu
raB
uza
ulu
i5
41
60
10
00
02
02
02
6.0
58
30
45
.66
85
56
97
.8
89
Jid
vei
61
44
06
1
2
4.1
00
00
46
.23
33
32
63
.9
90
Jose
ni
64
25
40
45
3.8
47
44
25
.51
41
74
6.7
06
08
75
1.6
91
Juri
lovca
44
68
53
0
0.4
00
40
28
.87
79
34
4.7
66
41
31
.9
92
Lac
auti
55
16
21
00
00
0
02
6.3
77
08
45
.82
40
11
77
4.2
93
Lu
go
j5
41
15
40
50
01
10
02
1.9
34
63
45
.68
67
31
21
.4
94
Mai
can
esti
53
07
29
28
2
7.8
28
2
82
82
7.4
83
33
45
.50
00
01
5.5
95
Man
gal
ia3
49
83
50
15
00
46
02
8.5
88
98
43
.81
64
72
.1
96
Mar
aus
63
52
00
6
2
2.0
00
00
46
.58
33
32
11
.6
97
Mar
cule
sti
42
57
30
30
2
9.9
30
30
30
30
27
.50
00
04
4.4
16
67
34
.9
98
Med
gid
ia4
15
81
60
00
09
00
28
.25
28
84
4.2
43
56
71
.5
99
Mie
rcure
aC
iuc
62
25
44
02
00
00
02
5.7
74
17
46
.37
15
86
68
.1
100
Mie
rsig
653150
4
21.8
3333
46.8
8333
120.8
Nat Hazards
123
-
Table
1co
nti
nued
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
10
1M
oin
esti
62
76
29
6
2
6.4
83
33
46
.45
00
05
67
.6
10
2M
old
ova
Vec
he
44
41
27
61
95
.56
12
96
21
.63
46
14
4.7
22
85
78
.9
10
3N
egre
sti
(Vas
lui)
65
07
27
91
29
.49
99
92
7.4
43
70
46
.83
83
31
23
.1
10
4O
cna
Su
gat
ag7
47
35
62
32
.92
21
2
23
.94
21
44
7.7
77
37
50
4.2
10
5O
clan
d6
10
52
69
25
.43
33
34
6.1
66
67
54
9.3
10
6O
do
rhei
ul
Sec
uie
sc6
18
51
81
1
.62
14
62
25
.29
33
44
6.2
97
09
49
7.2
10
7O
hab
a-M
atn
ic5
28
20
57
22
.08
33
34
5.4
66
67
22
2.6
10
8O
nce
sti
62
87
16
27
2
6.7
27
2
72
72
7.2
66
67
46
.46
66
72
02
.0
10
9O
radea
70
31
56
00
00
00
02
1.8
97
55
47
.03
60
21
32
.5
11
0O
ravit
a5
02
14
10
00
03
10
21
.71
18
44
5.0
38
96
25
7.5
11
1P
acli
sa5
34
25
32
3
22
.72
3
23
23
22
.88
33
34
5.5
66
67
38
2.1
11
2P
ades
(Apa
Nea
gra
)5
01
25
20
80
02
29
02
2.8
61
05
44
.99
71
42
60
.4
11
3P
alti
nis
53
93
57
01
00
.41
10
0
23
.93
40
04
5.6
57
51
14
29.6
11
4P
anci
u5
55
70
59
27
.08
33
34
5.9
16
67
26
9.2
11
5P
arin
cea
62
97
07
7
2
7.1
16
67
46
.48
33
32
82
.6
11
6P
aran
g5
23
32
80
70
.10
21
0
23
.46
46
24
5.3
87
69
14
74.7
11
7P
atar
lag
ele
51
96
22
0
0.4
01
92
02
6.3
70
80
45
.32
49
62
85
.6
11
8P
etro
san
i5
25
32
30
10
01
00
02
3.3
78
25
45
.40
66
16
06
.2
11
9P
iatr
aN
eam
t6
56
62
10
00
.10
32
02
6.3
90
90
46
.93
40
23
59
.8
12
0P
ietr
oas
ele
50
26
08
25
2
5.4
25
25
27
25
26
.57
72
04
5.0
97
35
25
2.9
12
1P
ites
ti4
52
45
20
00
00
00
24
.86
74
64
4.8
49
29
32
3.3
12
2P
loie
sti
45
76
00
00
00
00
02
5.9
88
99
44
.95
60
91
79
.1
12
3P
oia
na
Sta
mp
ei7
19
50
74
4
.14
48
42
5.1
36
04
47
.32
49
29
11
.8
12
4P
olo
vra
gi
51
13
49
01
00
.10
06
02
3.8
10
15
45
.16
58
75
32
.4
12
5P
otl
og
i4
35
53
57
25
.58
33
34
4.5
83
33
13
7.8
Nat Hazards
123
-
Table
1co
nti
nued
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
12
6P
red
eal
53
05
35
00
00
12
02
5.5
85
10
45
.50
64
61
08
3.5
12
7R
adau
ti7
51
55
50
18
00
11
02
5.8
91
85
47
.83
80
13
86
.3
12
8R
agla
-Cet
ate
70
44
37
8
2
4.6
16
67
47
.06
66
74
67
.1
12
9R
arau
72
75
27
25
2
4.6
25
25
25
.56
82
14
7.4
50
19
15
45.6
13
0R
amn
icu
Sar
at5
23
70
30
80
00
20
27
.04
01
64
5.3
90
75
14
0.5
13
1R
amn
icu
Val
cea
50
64
22
00
00
02
02
4.3
80
98
45
.08
91
92
30
.9
13
2R
om
an6
55
65
00
18
00
01
02
6.9
13
39
46
.96
94
62
18
.9
13
3R
osi
ori
de
Ved
e4
07
50
00
20
00
10
24
.98
00
34
4.1
07
74
10
3.1
13
4R
us
71
63
35
10
23
.58
33
34
7.2
66
67
28
9.8
13
5R
usc
aM
onta
na
(Co
vas
na)
53
42
28
4
2
2.4
66
67
45
.56
66
73
79
.3
13
6S
acu
ieni
72
22
05
02
00
.10
02
02
2.0
95
80
47
.34
44
71
13
.6
13
7S
arm
asu
64
54
10
19
1
8.9
19
19
19
19
24
.16
14
04
6.7
47
94
37
3.3
13
8S
atu
Mar
e7
48
25
30
30
.30
00
02
2.8
88
78
47
.72
17
71
20
.2
13
9S
ebes
(Alb
a)5
57
33
40
40
05
00
23
.54
30
94
5.9
64
53
25
4.9
14
0S
emen
ic5
07
15
80
70
00
0
22
.05
71
24
5.1
81
73
14
27.0
14
1S
fan
tuG
heo
rghe
(Del
ta)
45
49
36
0
00
10
0
29
.60
05
24
4.8
96
87
0.7
14
2S
fan
tuG
heo
rghe
(Mu
nte
)5
52
54
82
8
28
.22
82
92
92
82
5.8
03
71
45
.87
19
25
25
.6
14
3S
ibiu
54
84
09
00
0.1
00
00
24
.09
29
44
5.7
89
70
44
4.2
14
4S
igh
etu
lM
arm
atie
i7
58
35
50
0
.10
01
02
3.9
05
88
47
.93
95
72
73
.2
14
5S
inai
a(1
50
0)
52
35
30
00
00
02
5.5
15
71
45
.35
52
61
48
5.5
14
6S
ann
ico
lau
Mar
e6
04
03
70
80
00
00
20
.60
31
64
6.0
71
63
80
.3
14
7S
olo
nt
63
36
33
5
2
6.5
50
00
46
.55
00
03
91
.5
14
8S
ov
eja
60
06
40
7
2
6.6
66
67
46
.00
00
05
25
.8
14
9S
per
mez
eu7
18
41
07
24
.16
66
74
7.3
00
00
32
8.9
15
0S
tei
63
22
29
02
00
.41
01
02
2.4
68
09
46
.52
83
22
80
.6
Nat Hazards
123
-
Table
1co
nti
nued
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
15
1S
toic
eni
(Tar
gu
Lap
us)
73
23
53
6
2
3.8
83
33
47
.53
33
35
30
.2
15
2S
toln
aS
avad
isla
64
23
24
9
2
3.4
00
00
46
.70
00
06
11
.1
15
3S
toln
ici
43
64
47
0
00
41
02
4.7
91
53
44
.56
28
82
08
.2
15
4S
uce
ava
73
96
15
00
00
52
02
6.2
41
96
47
.63
32
83
58
.6
15
5S
uli
na
50
99
40
00
00
0
02
9.7
60
45
45
.14
86
93
15
6S
um
urd
ucu
65
23
26
9
2
3.4
00
00
46
.86
66
74
98
.1
15
7S
up
uru
De
Jos
72
82
47
19
1
8.8
19
22
26
19
22
.78
53
14
7.4
55
36
15
7.4
15
8T
arcu
51
52
31
01
0.4
05
0
22
.53
43
44
5.2
81
17
21
51.9
15
9T
ebea
61
02
44
7
7.5
89
14
82
2.7
27
70
46
.16
97
62
73
.6
16
0T
ecu
ci5
51
71
60
60
03
10
27
.41
03
64
5.8
41
95
59
.2
16
1T
arg
uN
eam
t7
14
62
30
0
.20
02
02
6.3
80
59
47
.21
24
33
84
.8
16
2T
arg
uO
cna
61
76
37
01
60
00
00
26
.64
25
94
6.2
72
96
24
4.0
16
3T
arg
uJi
u5
02
31
70
00
00
00
23
.26
08
84
5.0
40
96
20
1.2
16
4T
argu
Logre
sti
453344
06
00
00
023.7
1024
44.8
7842
267.4
16
5T
arg
uM
ure
s6
32
43
20
00
00
00
24
.53
53
34
6.5
33
68
30
8.2
16
6T
arg
uS
ecu
iesc
60
06
08
01
00
01
02
6.1
16
87
45
.99
32
45
69
.6
16
7T
ibuca
nii
De
Su
s7
07
63
19
26
.51
66
74
7.1
16
67
40
4.3
16
8T
ilea
gd
70
42
12
5
2
2.2
00
00
47
.06
66
71
72
.7
16
9T
imis
oar
a5
46
11
50
00
00
00
21
.25
93
64
5.7
71
46
89
.2
17
0T
arg
ov
iste
45
65
26
00
00
30
02
5.4
27
26
44
.92
99
12
97
.1
17
1T
ites
ti5
26
42
31
0
27
.82
8
2
82
4.3
83
33
45
.43
33
36
40
.0
17
2T
itu
43
95
34
41
83
.74
74
42
5.5
80
74
44
.65
32
01
55
.5
17
3T
op
lita
65
55
22
0
0.7
10
11
25
.36
15
34
6.9
26
64
66
2.8
17
4T
op
oru
40
15
39
10
25
.65
00
04
4.0
16
67
86
.9
17
5T
ulc
ea5
11
84
90
40
01
62
02
8.8
25
69
45
.19
08
41
.8
Nat Hazards
123
-
Table
1co
nti
nued
Nr
Sta
tio
nn
ame
IDP
PC
CA
PR
HS
HT
ST
AL
ON
(E
)L
AT
(N
)E
levat
ion
[m.a
.s.l
.]
17
6T
uln
ici
55
56
40
5
22
.62
3
26
23
26
.66
66
74
5.9
16
67
54
0.0
17
7T
urd
a6
35
34
72
4
44
64
23
.79
28
44
6.5
83
39
42
4.2
17
8T
urn
uM
agu
rele
34
64
52
00
00
24
02
4.8
79
77
43
.76
04
23
1.2
17
9U
rzic
eni
44
36
39
01
00
.10
02
02
6.6
58
56
44
.72
19
55
7.9
18
0V
arad
iaD
eM
ure
s6
02
21
30
0
.21
13
02
2.1
52
42
46
.01
94
91
47
.8
181
Vas
lui
639744
00
00
00
027.7
1583
46.6
4624
109.8
18
2V
arfu
lO
mu
52
75
27
00
00
3
02
5.4
58
26
45
.44
61
42
47
8.3
18
3V
idel
e4
17
53
00
11
00
30
02
5.5
38
54
44
.28
32
61
03
.7
18
4V
izir
u5
00
74
32
9
29
.22
9
29
29
27
.71
66
74
5.0
00
00
15
.2
18
5V
lad
easa
18
00
64
62
47
06
11
10
0
22
.79
57
94
6.7
59
56
18
33.9
18
6V
lad
esti
(Arg
es)
51
14
54
4
2
4.9
00
00
45
.18
33
35
06
.1
18
7V
oin
easa
52
53
58
21
12
.53
32
3.9
68
55
45
.41
15
07
39
.1
18
8Z
alau
71
13
05
03
00
40
02
3.0
48
36
47
.19
52
83
01
.6
Reg
ardin
gai
rte
mp
erat
ure
,th
ep
erce
nta
ges
of
dat
are
cord
com
ple
ten
ess
are
sim
ilar
toth
em
inim
um
,m
axim
um
and
aver
age
dat
ase
ries
PP
pre
cipit
atio
n,CC
clo
ud
cov
er,AP
air
pre
ssu
re,RH
rela
tiv
eh
um
idit
y,SH
sun
shin
eh
ou
rs,TS
soil
tem
per
atu
re,TA
air
tem
per
ature
Nat Hazards
123
-
comparative mathematical examination of the original and the
homogenized series systems
(Costa and Soares 2009). The good performance of MASH was also
demonstrated in a
recent study dealing with benchmarking of homogenization
algorithms (Venema et al.
2012), within the COST (European Cooperation in Science and
Technology) Action
Fig. 2 Multiannual means (19612013) for each parameter. The
bottom right map shows the locations ofthe six independent stations
used in the quantitative analysis
Nat Hazards
123
-
ES0601: Advances in homogenization methods of climate series: an
integrated approach
(HOME).
The gridding was based on the software MISH (Meteorological
Interpolation based on
the Surface Homogenized Data Basis), version 1.03, developed by
Szentimrey and Bihari
(2004, 2005). The main advantage of MISH over most
geostatistical methodswhere the
predictors refer to a single realization in timeconsists in
statistically estimating trend
differences and covariances using long-term climatological time
series. Therefore, when
these parameters are known, they provide much more information
than when using only
the predictors of a single realization. The MISH method
(Szentimrey and Bihari 2007) is
especially developed to incorporate information from time series
in the interpolation
procedure and is capable to include ancillary data. The software
consists of two parts: (1)
the modeling part and (2) the interpolation one. The modeling
system for statistical climate
parameters is based on the long homogenized data series and
supplementary model vari-
ables. As in MASH, the interpolation system contains an additive
(e.g., for temperature) or
multiplicative (e.g., for precipitation) model and an
interpolation formula that can be used
depending on the climate elements. The interpolation system is
applied to the results of the
modeling system. The interpolation error or representativity is
also modeled (Tveito et al.
2006).
4 Results and discussion
It is worth mentioning that, beyond their advanced theoretical
background, both MASH
(homogenization) and MISH (interpolation) programs performed
admirably in terms of
reliability, speed and user requirements. After applying the two
programs to each of the
nine meteorological variables, the output data were converted to
NetCDF (network
Common Data Form) format, a CSV (comma-separated values) version
of the dataset
being also available. The dataset managed to capture well the
orographical effects for all
parameters, as Fig. 2 shows.
A quantitative analysis of the ROCADA dataset has been carried
out by comparing the
precipitation and temperature data from six independent stations
(Table 2)by inde-
pendent stations we refer to stations that were not involved in
the construction of the
gridded datasets; their location is shown in Fig. 2bottom left
(for soil temperature, only
four out of six stations had available data). The time series
from these stations were
compared with the time series extracted from the corresponding
grid cells, from ROCADA,
E-OBS and APHRODITE datasets.
Table 2 Meteorological stations used for comparing the raw data
records with the time series from theircorresponding pixels from
E-OBS and ROCADA gridded datasets
Name Stationcode
Latitude(N)
Longitude(E)
Altitude(m.a.s.l.)
Time seriescoverage
Alba Iulia 15280 46.0639 23.5634 252 19792013
Gura Portitei 15428 44.6901 29.0005 4 19862013
Obarsia Lotrului 15297 45.4355 23.6308 1348 19762013
Stana de Vale 15118 46.6898 22.6234 1108 19792013
Targu Lapus 15047 47.4396 23.8722 375 19872013
Tarnaveni (Bobohalma) 15165 46.3600 24.2259 525 19872013
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Figure 3 shows the comparison of the aforementioned time series.
As it could be seen,
the ROCADA dataset comes closer to the raw station data. The
temporal variability of
precipitation is well estimated in all three datasets,
especially at lower altitudes. At Stana
de Vale weather station, located in the mountains, the
precipitation values are highly
underestimated by both gridded datasets, ROCADA providing the
closest resemblance
with the observations.
In order to illustrate how closely the interpolation methods
resemble the observations,
Taylor diagrams were also used, as they are able to
simultaneously represent three coef-
ficients in one graph (Taylor 2001): standard deviations of
measured and computed data as
the radial distance from the graph origin, centered RMS
difference indicating the distance
from the point observed located on x-axis, and the correlation
coefficient given by the
Fig. 3 Comparison of the precipitation time series from ROCADA,
E-OBS, APHRODITE and independentmeteorological stations at six
locations, for January (top) and July (bottom)
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angle between the x-axis and the position vector. Figure 4 shows
the Taylor diagram for
precipitation data extracted at station locations from the three
gridded datasets (the entire
data series were considered when building the diagrams).
Generally, the E-OBS and
ROCADA datasets show reasonable performance in all cases. ROCADA
has a better
accuracy in terms of standard deviation, correlation and
root-mean-square error. However,
both datasets underestimate the amount of observed
precipitation, as emphasized by the
temporal variability plots from Fig. 3.
Regarding the mean air temperature, the time series plots (Fig.
5) show a much closer
resemblance between the station data and the time series
extracted from the gridded
datasets. As in the case of precipitation, the ROCADA dataset
managed to better reproduce
the local variability. The Taylor diagram (Fig. 6) shows that
both gridded datasets per-
formed well in all cases, with ROCADA having a better accuracy
with respect to all three
indicators of the diagramalthough, for two stations, the results
were extremely close for
the two datasets.
However, it is worth mentioning that these results could be
strongly influenced by the
fact that, within ROCADA, all available precipitation data
records with less than 30 %
missing values were used, while in the E-OBS dataset only 28
Romanian weather stations
were involved (www.ecad.eu/).
For the other parameters (i.e., minimum and maximum air
temperature, cloud cover,
number of sunshine hours, air pressure, relative humidity, soil
temperature), comparison
plots between ROCADA and six independent stations (four in case
of soil temperature) are
available as electronic supplementary material.
Fig. 4 Taylor diagrams of the precipitation time series from
ROCADA, E-OBS and independentmeteorological stations at six
locations
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The comparison charts show that at the two mountain stations
situated above
1100 m.a.s.l. (Stana de Vale and Obarsia Lotrului), the minimum
air temperature and the
number of sunshine hours are both overestimated by ROCADA, while
the maximum air
temperature, air pressure and relative humidity are
underestimated. Cloud cover is well
reproduced for all sites. The same goes for soil temperature;
however, it has to be men-
tioned that there are no observations for soil temperature at
stations above 1100 m.a.s.l.
These differences that occur at higher elevations are mainly due
to the interpolation
method and to the averaging at the dimension of the grid cell.
While we cannot expect a
perfect fit of a time series from a weather station over the
data from the related grid cell
Fig. 5 Comparison of the mean air temperature time series from
ROCADA, E-OBS and independentmeteorological stations at six
locations, for January (top) and July (bottom)
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(averaged over 0.1 9 0.1 degrees), it is obvious that, at higher
elevations, the values
derived from ROCADA are less reliable in most cases.
5 Conclusions and future work
We presented the realization of a high-resolution, daily gridded
climatic dataset for the
Romanian territory, for nine meteorological variables, covering
the period 19612013. The
data were quality-controlled, homogenized and spatially
interpolated using state-of-the-art
methods implemented in MASH and MISH software packages. The
dataset proved to
capture well the orography and is suitable for studies on
climatic variability, climate-
related hazards, as well as for spatially distributed
agro/bio-meteorological and hydro-
logical models. The dataset is freely available on request at
doi.pangaea.de/10.1594/
PANGAEA.833627 (Birsan and Dumitrescu 2014b) and at
www.euro4m.eu.
Of course, any gridded dataset-based interpolation of local
weather station has its
limitations, at least because the values of a given parameter
are averaged over a grid cell.
We are aware that, due to low network station density, the data
series might be over-
smoothed (Daly 2006; Ensor and Robeson 2008; Hofstra et al.
2009), leading to a flat-
tening of the extreme values, particularly concerning
precipitation at higher elevations
(e.g., Cheval et al. 2011).
Future work on the dataset concerns five main issues: increasing
the length of the time
period; adding new parameters to the dataset (e.g., snow depth,
wind speed); adding
Fig. 6 Taylor diagrams of the mean air temperature time series
from ROCADA, E-OBS and independentmeteorological stations at six
locations
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metadata within the homogenization process; increasing the
spatial resolution for some
parameters (first of all for precipitation); testing new spatial
interpolation methods.
Acknowledgments Dr. Tamas Szentimrey (Hungarian Meteorological
Service) is kindly acknowledgedfor his help on the homogenization
and interpolation software. We acknowledge the E-OBS dataset from
theEU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and
the data providers in the ECA&Dproject (http://www.ecad.eu).
The APHRODITE dataset was downloaded from
www.chikyu.ac.jp/precip.We thank the two anonymous referees for
their comments and suggestions, which led to an overall
im-provement in the original manuscript. This work has been
realized within the framework of the EU-FP7project EURO4 M, code:
242093 (www.euro4m.eu).
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ROCADA: a gridded daily climatic dataset over Romania
(1961--2013) for nine meteorological
variablesAbstractIntroductionLocation and dataMethodsResults and
discussionConclusions and future workAcknowledgmentsReferences
![B.I.S.P. Dumitrescu [SEM 2]](https://img.dokumen.tips/doc/110x75/557201514979599169a145eb/bisp-dumitrescu-sem-2.jpg)
