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Demarée, G. R.; Lachaert, Pieter-Jan; Verhoeve, T.; Thoen, Erik

doi:10.1023/a:1014931211466

Cited by 30 publications

(3 citation statements)

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“…In the early instrumental period, it is not always possible to find a satisfactory solution, or even another record. In the literature, several long precipitation series were obtained by combining records taken at different sites in the same geographic area and/or at different levels from the ground, although this method opens issues concerning the homogeneity of the resulting series (Wales-Smith 1971;Craddock 1976Craddock , 1979Craddock and Wales-Smith 1977;Wigley et al 1984;Wigley and Jones 1987;Demarée et al 2002;Alcoforado et al 2012;Burt and Ferranti 2012;Todd et al 2015;Murphy et al 2018;Burt and Burt 2019). The choice of the most suitable interpolation method depends on the number of neighbouring stations available for use and their correlation with the particular station where data are being reconstructed (Caldera et al 2016).…”

Section: The 3rd Method: Use Of a Contemporary Record Of A Nearby Loc...mentioning

confidence: 99%

A comparison between different methods to fill gaps in early precipitation series

et al. 2022

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The aim of this work is to analyse and compare different methodologies to fill gaps in early precipitation series, and to evaluate which time resolution is reachable, i.e. monthly or daily one. The following methods are applied and tested to fill the 1764–1767 gap in the precipitation series of Padua: (1) using a relationship between monthly amounts and frequencies; (2) transforming a daily log with visual observations into numerical values through analysis, classification, and calibration; (3) substituting the missing values with an instrumental record from a nearby, contemporary station in the same climatic area. To apply the second method, the descriptions reported in the Morgagni Logs are grouped in 37 classes and transformed into numerical values, using for calibration the observed amounts in the Poleni record over the 24-year common period. As a third method, the series of Temanza and Pollaroli in Venice is used to fill the gap, and the application of a factor scale based on the ratio Padua/Venice tempted. The results of these three methods are discussed and commented.

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Section: The 3rd Method: Use Of a Contemporary Record Of A Nearby Loc...mentioning

confidence: 99%

A comparison between different methods to fill gaps in early precipitation series

et al. 2022

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“…[4] While the methods used to homogenize monthly and annual data are well established, only a limited number of publications address the challenge of daily temperature data correction and of other spatially highly correlated climate parameters [e.g., Demarée et al, 2002;Maugeri et al, 2002;Vincent et al, 2002;Brandsma and Können, 2006;Brunet et al, 2006]. In general these techniques use highly correlated neighboring station series to correct candidate station values and are therefore applicable only in areas with high station density.…”

Section: Introductionmentioning

confidence: 99%

Homogenization of daily maximum temperature series in the Mediterranean

et al. 2009

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[1] Homogenization of atmospheric variables to detect and attribute past and present climate trends and to predict scenarios of future meteorological extreme events is a crucial issue for the reliability of analysis results. Here we present a quality control and new homogenization method (PENHOM) based on a penalized log likelihood procedure and a nonlinear model applied to 174 daily summer maximum temperature series in the Greater Mediterranean Region covering the last 50-100 years. The break detection method does not rely on homogeneous reference stations and was chosen owing to the lack of metadata available. The correction procedure allows the higher-order moments of the candidate distribution to be corrected, which is important if the homogenized series are to be used to quantify temperature extremes. Both procedures require a set of highly correlated neighboring stations to correct climate series reliably. After carrying out the homogeneity procedure, 84% of all time series were found to contain at least one artificial breakpoint. Time series of the eastern Mediterranean (one breakpoint in 24 years on average) show significantly more breakpoints than do series of the Western Basin (one breakpoint in 36 years on average). The mean adjustment (standard error) of all daily summer maximum temperatures is +0.03°C (±0.38°C) for the western Mediterranean, +0.16°C (±0.52°C) for the central Mediterranean, and +0.19°C (±0.30°C) for the eastern Mediterranean, indicating a reduced increase in mean summer daytime temperature compared to that detected by analyzing raw data. The adjustments for higher-order moments were not uniform. Most significant mean changes due to homogenization were detected for both: the hottest (+0.15°C ± 0.66°C) and coldest decile (À0.83°C ± 1.28°C) compared to the raw data in the central Mediterranean. This study demonstrates that homogenization of daily temperature data is necessary before any analysis of temperaturerelated extreme events such as heat waves, cold spells, and their impacts on human health, agriculture, and ecosystems can be studied.

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“…In the Netherlands, the De Bilt series, starting already in 1735, is still in operation, but other even older series exist in the Netherlands that have been tested and corrected (van Engelen and Geurts, 1985), and early observations from central Belgium, starting 1767, have also been homogenized (Demarée et al, 2002). In Germany, the Hohenpeissenberg series, which is of particular interest as it is in the countryside free from urbane heat island influence, goes back to 1781 (Winkler, 2009).…”

Section: Introductionmentioning

confidence: 99%

The Oslo temperature series 1837-2012: homogeneity testing and temperature analysis

et al. 2014

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A 175 years long homogenized composite record of monthly mean temperatures is presented for Oslo, the capital of Norway. The early raw data have been digitised and quality controlled, and monthly means have been calculated. Some early original observations carried out in a Wild screen were found to be spuriously high because of inappropriate sheltering from sunlight. These spurious temperatures were not used in the composite record, but alternative temperatures measured by thermometers placed outside windows at the Astronomical Observatory were used instead. No inhomogeneity was detected in the latter series after adding an instrument correction of +0.3 ∘ C, but the start year of the correction remains uncertain. The more recent part of the composite record used the long-term series (1937 to present) from Blindern in Oslo, the premises of The Norwegian Meteorological Institute. Two small inhomogeneities were detected in the Blindern series, possibly caused by a weak urban heat island effect or growing/cutting of trees. The study revealed that the annual mean temperature has increased by 1.5 ∘ C in the period 1838-2012. The most pronounced increase in annual temperature occurred during the last 50 years, and in the early 20th century that ended with a local maximum in the 1930s. The temperature has increased significantly in all seasons; however, the temperature increase in summer was less than a half of that in winter and spring, which were the seasons with largest increase. In addition the monthly mean temperature of the coldest month in each year has increased two times faster than the warmest one. The most significant temperature variations were associated to ∼ 5-year time scales in its early part, but since 1930 and up to present, the dominant time scales were 10-20 years.

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Cited by 30 publications

References 0 publications

A comparison between different methods to fill gaps in early precipitation series

A comparison between different methods to fill gaps in early precipitation series

Homogenization of daily maximum temperature series in the Mediterranean

The Oslo temperature series 1837-2012: homogeneity testing and temperature analysis

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