Development of a Quality-Controlled and Homogenised Long-Term Daily Maximum and Minimum Air Temperature Network Dataset for Ireland

Carla Mateus; Aaron Potito

Development of a Quality-Controlled and Homogenised Long-Term Daily Maximum and Minimum Air Temperature Network Dataset for Ireland

Carla Mateus | Aaron Potito

Accurate long-term daily maximum and minimum air temperature series are needed to assess the frequency, intensity, distribution, and duration of extreme climatic events. However, quality control and homogenisation procedures are required to minimise errors and inhomogeneities in climate series before the commencement of climate data analysis. A semi-automatic quality control procedure consisting of climate consistency, internal consistency, day-to-day step-change, and persistency tests was applied for 12 long-term series registered in Ireland from 1831–1968, Armagh Observatory (Northern Ireland) from 1844–2018, and for 21 short-term series dating to the mid-19th century. There were 976,786 observations quality-controlled, and 27,854 (2.9%) values flagged. Of the flagged records, 98.5% (n = 27,446) were validated, 1.4% (n = 380) corrected and 0.1% (n = 28) deleted. The historical long-term quality-controlled series were merged with the modern series quality-controlled by Met Éireann and homogenised using the software MASHv3.03 in combination with station metadata for 1885–2018. The series presented better homogenisation outcomes when homogenised as part of smaller regional networks rather than as a national network. The homogenisation of daily, monthly, seasonal, and annual series improved for all stations, and the homogenised records showed stronger correlations with the Central England long-term temperature series.

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