scholarly journals Homogenization of daily temperature series in the European Climate Assessment & Dataset

2018 ◽  
Vol 39 (3) ◽  
pp. 1243-1261 ◽  
Author(s):  
Antonello A. Squintu ◽  
Gerard van der Schrier ◽  
Yuri Brugnara ◽  
Albert Klein Tank
Keyword(s):  
Temperature Series ◽  
Daily Temperature ◽  
Climate Assessment ◽  
European Climate ◽  
Daily Temperature Series

scholarly journals A physics-based correction model for homogenizing sub-daily temperature series

2012 ◽  
Vol 117 (D17) ◽  
pp. n/a-n/a ◽  
Author(s):  
R. Auchmann ◽  
S. Brönnimann
Keyword(s):  
Temperature Series ◽  
Daily Temperature ◽  
Correction Model ◽  
Daily Temperature Series

scholarly journals Extreme value and cluster analysis of European daily temperature series

2011 ◽  
Vol 38 (12) ◽  
pp. 2793-2804 ◽  
Author(s):  
Manuel G. Scotto ◽  
Susana M. Barbosa ◽  
Andrés M. Alonso
Keyword(s):  
Cluster Analysis ◽  
Extreme Value ◽  
Temperature Series ◽  
Daily Temperature ◽  
And Cluster Analysis ◽  
Daily Temperature Series

scholarly journals An assessment of the role of homogenization protocol in the performance of daily temperature series and trends: application to northeastern Spain

2011 ◽  
Vol 33 (1) ◽  
pp. 87-108 ◽  
Author(s):  
A. El Kenawy ◽  
J. I. López-Moreno ◽  
P. Stepanek ◽  
S. M. Vicente-Serrano
Keyword(s):  
Temperature Series ◽  
Daily Temperature ◽  
Daily Temperature Series

scholarly journals SPLIDHOM: A Method for Homogenization of Daily Temperature Observations

2011 ◽  
Vol 50 (11) ◽  
pp. 2343-2358 ◽  
Author(s):  
Olivier Mestre ◽  
Christine Gruber ◽  
Clémentine Prieur ◽  
Henri Caussinus ◽  
Sylvie Jourdain
Keyword(s):  
Time Scale ◽  
Smoothing Splines ◽  
Temperature Series ◽  
High Order ◽  
Daily Temperature ◽  
Daily Series ◽  
Nonlinear Regression Method ◽  
Daily Temperature Series ◽  
Selection Of ◽  
Daily Time

AbstractOne major concern of climate change is the possible rise of temperature extreme events, in terms of occurrence and intensity. To study this phenomenon, reliable daily series are required, for instance to compute daily-based indices: high-order quantiles, annual extrema, number of days exceeding thresholds, and so on. Because observed series are likely to be affected by changes in the measurement conditions, adapted homogenization procedures are required. Although a very large number of procedures have been proposed for adjustment of observed series at a monthly time scale, few have been proposed for adjustment of daily temperature series. This article proposes a new adjustment method for temperature series at a daily time scale. This method, called spline daily homogenization (SPLIDHOM), relies on an indirect nonlinear regression method. Estimation of the regression functions is performed by cubic smoothing splines. This method is able to adjust the mean of the series as well as high-order quantiles and moments of the series. When using well-correlated series, SPLIDHOM improves the results of two widely used methods, as a result of an optimal selection of the smoothing parameter. Applications to the Toulouse, France, temperature series are shown as a real example.

scholarly journals Comparison of homogenization methods for daily temperature series against an observation-based benchmark dataset

2020 ◽  
Vol 140 (1-2) ◽  
pp. 285-301 ◽  
Author(s):  
Antonello Angelo Squintu ◽  
Gerard van der Schrier ◽  
Petr Štěpánek ◽  
Pavel Zahradníček ◽  
Albert Klein Tank
Keyword(s):  
Temperature Series ◽  
Benchmark Dataset ◽  
Daily Temperature ◽  
Reference Series ◽  
The Czech Republic ◽  
Benchmark Datasets ◽  
Homogenization Methods ◽  
Daily Temperature Series ◽  
Almost All ◽  
Station Density

AbstractHomogenization of daily temperature series is a fundamental step for climatological analyses. In the last decades, several methods have been developed, presenting different statistical and procedural approaches. In this study, four homogenization methods (together with two variants) have been tested and compared. This has been performed constructing a benchmark dataset, where segments of homogeneous series are replaced with simultaneous measurements from neighboring homogeneous series. This generates inhomogeneous series (the test set) whose homogeneous version (the benchmark set) is known. Two benchmark datasets are created. The first one is based on series from the Czech Republic and has a high quality, high station density, and a large number of reference series. The second one uses stations from all Europe and presents more challenges, such as missing segments, low station density, and scarcity of reference series. The comparison has been performed with pre-defined metrics which check the statistical distance between the homogenized versions and the benchmark. Almost all homogenization methods perform well on the near-ideal benchmark (maximum relative root mean square error (rRMSE): 1.01), while on the European dataset, the homogenization methods diverge and the rRMSE increases up to 1.87. Analyses of the percentages of non-adjusted inhomogeneous data (up to 39%) and substantial differences in the trends among the homogenized versions helped identifying diverging procedural characteristics of the methods. These results add new elements to the debate about homogenization methods for daily values and motivate the use of realistic and challenging datasets in evaluating their robustness and flexibility.

Homogenization of Daily Temperature Data

2017 ◽  
Vol 30 (3) ◽  
pp. 985-999 ◽  
Author(s):  
Anuradha P. Hewaarachchi ◽  
Yingbo Li ◽  
Robert Lund ◽  
Jared Rennie
Keyword(s):  
Temperature Series ◽  
Daily Temperature ◽  
Statistical Characteristics ◽  
Efficient Manner ◽  
Daily Data ◽  
Computational Speed ◽  
Daily Series ◽  
Data Points ◽  
South Haven ◽  
Daily Temperature Series

Abstract This paper develops a method for homogenizing daily temperature series. While daily temperatures are statistically more complex than annual or monthly temperatures, techniques and computational methods have been accumulating that can now model and analyze all salient statistical characteristics of daily temperature series. The goal here is to combine these techniques in an efficient manner for multiple changepoint identification in daily series; computational speed is critical as a century of daily data has over 36 500 data points. The method developed here takes into account 1) metadata, 2) reference series, 3) seasonal cycles, and 4) autocorrelation. Autocorrelation is especially important: ignoring it can degrade changepoint techniques, and sample autocorrelations of day-to-day temperature anomalies are often as large as 0.7. While daily homogenization is not conducted as commonly as monthly or annual homogenization, daily analyses provide greater detection precision as they are roughly 30 times as long as monthly records. For example, it is relatively easy to detect two changepoints less than two years apart with daily data, but virtually impossible to flag these in corresponding annually averaged data. The developed methods are shown to work in simulation studies and applied in the analysis of 46 years of daily temperatures from South Haven, Michigan.

The study of some daily temperature series by spectral methods

2011 ◽  
Vol 126 (3) ◽  
Author(s):  
V. Malvestuto ◽  
M. C. Beltrano ◽  
O. Testa
Keyword(s):  
Spectral Methods ◽  
Temperature Series ◽  
Daily Temperature ◽  
Daily Temperature Series

scholarly journals Time Structure of Observed, GCM-Simulated, Downscaled, and Stochastically Generated Daily Temperature Series

2001 ◽  
Vol 14 (20) ◽  
pp. 4047-4061 ◽  
Author(s):  
Radan Huth ◽  
Jan Kyselý ◽  
Martin Dubrovský
Keyword(s):  
Temperature Series ◽  
Time Structure ◽  
Daily Temperature ◽  
Daily Temperature Series

scholarly journals Building Long Homogeneous Temperature Series across Europe: A New Approach for the Blending of Neighboring Series

2020 ◽  
Vol 59 (1) ◽  
pp. 175-189 ◽  
Author(s):  
Antonello A. Squintu ◽  
Gerard van der Schrier ◽  
Else J. M. van den Besselaar ◽  
Richard C. Cornes ◽  
Albert M. G. Klein Tank
Keyword(s):  
Temperature Series ◽  
European Continent ◽  
Mean Values ◽  
Climate Assessment ◽  
European Climate ◽  
Spatial Consistency ◽  
Combining Data ◽  
Temperature Variable ◽  
Trend Assessment ◽  
Climate Analysis

ABSTRACTLong 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 that 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 (ECA&D, 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, and approximately 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, Switzerland), on cases in which blending is complex (Rheinstetten, Germany), and on series that are completed by adding relevant portions of Global Telecommunications System synoptic data (Siauliai, Lithuania). 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 dataset will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded datasets and it will be available online for users (https://www.ecad.eu).

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