Building long homogeneous temperature series across Europe: a new approach for the blending of neighboring series

Squintu, Antonello A.; van der Schrier, Gerard; van den Besselaar, Else J.M.; Cornes, Richard C. ORCID:; Klein Tank, Albert M. G.. 2020 Building long homogeneous temperature series across Europe: a new approach for the blending of neighboring series. Journal of Applied Meteorology and Climatology, 59 (1). 175-189.

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Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series which may affect trend estimates. In this paper an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, around 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva), on cases where blending is complex (Rheinstetten) and on series which are completed by adding relevant portions of GTS synoptic data (Siauliai). Finally, a trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized data-set will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded data-sets, and will be available for users on

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 1558-8424
Date made live: 16 Jan 2020 16:11 +0 (UTC)

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