Experience regarding detecting Experience regarding detecting inhomogeneities in temperature inhomogeneities in temperature

time series using MASHtime series using MASH

Lita Lizuma, Valentina Protopopova and Agrita BriedeLita Lizuma, Valentina Protopopova and Agrita Briede

6TH Homogenization seminar Budapest, 26-30 May, 2008

Data Data

Period 1950.-2006Period 1950.-2006. . Station network is dense enough for Station network is dense enough for

efficient homogeneity testingefficient homogeneity testing There are not a big changes in operational There are not a big changes in operational

practice of meteorological stations practice of meteorological stations Norm period 1961-1990; 1971-2000Norm period 1961-1990; 1971-2000

DataData

23 data series of: 23 data series of: Daily mean temperature Daily mean temperature Daily maximum temperatureDaily maximum temperature Daily minumum temperatureDaily minumum temperature

METEOROLOGICAL OBSERVATIONS NETWORK

At present meteorological observations are performed at 24 climate and synoptic and 32 precipitation stations

METHODMETHOD

MASH v3.02 MASH v3.02

Multiple Analyses of Series for Multiple Analyses of Series for Homogenization Homogenization

Hungarian Meteorlogical Service Hungarian Meteorlogical Service

Main results and fundingsMain results and fundings

AAll the time series contain the homogeneity ll the time series contain the homogeneity breaks at least during one of the monthbreaks at least during one of the month

For some stations the multiple breaks were For some stations the multiple breaks were foundfound

The largest detected homogeneity breaks in the The largest detected homogeneity breaks in the mean monthly temperatures are up to ±1.0mean monthly temperatures are up to ±1.000C, in C, in mean monthly maximum temperature are up to mean monthly maximum temperature are up to ±1.3±1.300C and for mean monthly minimum C and for mean monthly minimum temperature are up to ±1.4temperature are up to ±1.400CC

NNumber of breaksumber of breaks

175 for mean monthly temperature, 175 for mean monthly temperature, 218 for mean monthly maximum 218 for mean monthly maximum temperaturetemperature 120 for mean monthly minimum temperature120 for mean monthly minimum temperature

0

2

4

6

8

10

12

14

16

1-0

.9-0

.8-0

.7-0

.6-0

.5-0

.4-0

.3-0

.2-0

.1 0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9 1

breaks

%

Frequency distribution of monthly mean temperature shifts

Breaks in mean summer Breaks in mean summer temperature - Rigatemperature - Riga

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

19

50

19

52

19

54

19

56

19

58

19

60

19

62

19

64

19

66

19

68

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

relocation relocation

Relocation and automatization

The data analyse using coccected and uncorrected time The data analyse using coccected and uncorrected time seriesseries

Mean mothly temperature (1961-1990)Mean mothly temperature (1961-1990)Riga-University

-10

-5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10 11 12 13

month

me

an

te

mp

era

ture

, 0 C

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

Dif

ere

nc

es

, 0 C

H I diference (H-I)

Summer mean tempeature - RigaSummer mean tempeature - Riga

y = 0.0142x + 16.475

R2 = 0.0607

y = 0.0251x + 15.779

R2 = 0.161

14

15

16

17

18

19

20

1950

1953

1956

1959

1962

1965

1968

1971

1974

1977

1980

1983

1986

1989

1992

1995

1998

2001

2004

year

tem

pe

ratu

re, 0 C

Homogenazed Original Linear (Original) Linear (Homogenazed)

Number of cold days - summerNumber of cold days - summer

y = -0.0592x + 11.217

R2 = 0.0197

y = -0.1006x + 20.182

R2 = 0.0273

0

5

10

15

20

25

30

35

40

45

50

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

year

da

ys

non corrected corrected Linear (non corrected) Linear (corrected)

ConclusionConclusion

Software Software MASH v3.02 MASH v3.02 is very good and useful is very good and useful method for automatic homogenization of daily, method for automatic homogenization of daily, monthly, seasonal and yearly time series monthly, seasonal and yearly time series