Experience regarding detecting inhomogeneities in temperature time series using MASH Lita Lizuma, Valentina Protopopova and Agrita Briede 6TH Homogenization.

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Presentation transcript:

Experience regarding detecting inhomogeneities in temperature time series using MASH Lita Lizuma, Valentina Protopopova and Agrita Briede 6TH Homogenization seminar Budapest, May, 2008

Data Period  Station network is dense enough for efficient homogeneity testing  There are not a big changes in operational practice of meteorological stations  Norm period ;

Data 23 data series of:  Daily mean temperature  Daily maximum temperature  Daily minumum temperature

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

METHOD MASH v3.02 Multiple Analyses of Series for Homogenization Hungarian Meteorlogical Service

Main results and fundings  All the time series contain the homogeneity breaks at least during one of the month  For some stations the multiple breaks were found  The largest detected homogeneity breaks in the mean monthly temperatures are up to ±1.0 0 C, in mean monthly maximum temperature are up to ±1.3 0 C and for mean monthly minimum temperature are up to ±1.4 0 C

Number of breaks  175 for mean monthly temperature,  218 for mean monthly maximum temperature  120 for mean monthly minimum temperature Frequency distribution of monthly mean temperature shifts

Breaks in mean summer temperature - Riga relocation Relocation and automatization

The data analyse using coccected and uncorrected time series Mean mothly temperature ( ) Riga-University

Summer mean tempeature - Riga

Number of cold days - summer

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