Homogenization of Temperature Series via Pairwise Comparisons

Menne, Matthew J.; Williams, Claude N.

doi:10.1175/2008jcli2263.1

Cited by 288 publications

(316 citation statements)

References 39 publications

(66 reference statements)

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“…This uses the pairwise homogenisation algorithm from Menne and Williams Jr. (2009) with monthly mean values as well as monthly mean diurnal ranges (tem- T 2851 4404 282 117 13 2 0 8 Td 2723 4481 271 131 15 1 4 51 SLP 2025 3884 579 376 111 48 51 T 4238 1947 318 394 270 293 195 22 Td 3722 1857 302 534 425 459 336 42 SLP 6941 569 68 40 21 15 12 11 ws 5161 1081 361 393 307 218 123 33 Climatological outliers check T 1162 5789 400 188 80 29 25 4 Td 741 6016 476 3283 101 36 20 4 Spike check T 2414 5138 78 34 7 4 2 0 Td 894 6662 79 33 6 1 2 0 SLP 2582 5019 38 27 4 4 …”

Section: Neighbour Checksmentioning

confidence: 99%

“…The initial data release (v1.0.0.2011f) covered 1973-2011, with annual updates occurring during the early part of each calendar year; the latest update was to v1.0.4.2015f in July 2016. A homogeneity assessment was carried out on v1.0.2.2013f by Dunn et al (2014) using the pairwise homogenisation algorithm (PHA, Menne and Williams Jr., 2009). As HadISD contains sub-daily data, and the PHA assesses the homogeneity using monthly mean values, the adjustments returned by PHA were not applied to the data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Expanding HadISD: quality-controlled, sub-daily station data from 1931

Dunn

Willett

Parker

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. HadISD is a sub-daily, station-based, qualitycontrolled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as NetCDF files at http://www.metoffice.gov.uk/hadobs/hadisd and updated annually.

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Section: Neighbour Checksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Expanding HadISD: quality-controlled, sub-daily station data from 1931

Dunn

Willett

Parker

et al. 2016

Geosci. Instrum. Method. Data Syst.

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show abstract

“…For these reasons, the processes of the climate data homogeneity test and correction have become an important basic work. There are many kinds of homogenization methods at present [26][27][28][29]. Although their results are varied, the homogeneity of corrected data series has already increased.…”

Section: Introductionmentioning

confidence: 99%

The Spatial and Temporal Variation of Temperature in the Qinghai-Xizang (Tibetan) Plateau during 1971–2015

Yang

Wang

2017

Atmosphere

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Abstract:The Tibetan Plateau (TP), which is well known as "The Third Pole", is of great importance to climate change in East Asia, and even the whole world. In this paper, we selected the monthly temperature (including the monthly mean and the maximum and minimum temperature) during 1971-2015 from 88 meteorological stations on the TP. The data were tested and corrected by using Penalized Maximal F Test (PMFT) based on RHtest. Afterwards, based on the Mann-Kendall test, we analyzed the seasonal and time-interval characteristics on each station in detail. The results show that the TP has experienced significant warming during 1971-2015. When comparing the selected elements, the warming rate of minimum temperature (T min ) is the largest, the mean temperature (T mean ) comes second, and the maximum temperature (T max ) is the smallest. The warming trends in four seasons are significant, and the highest warming rate occurs in winter. The warming trend on the TP has a prominent spatial difference, with a large warming rate on the eastern parts and a small one on the central regions. In different seasons, the warming trends on the TP have different characteristics in the time interval. Since 1998, the warming rate in spring increased markedly, spring has displaced winter as the season with the highest warming rate recently.

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“…A new automatic homogenisation method, USHCN (Menne and Williams Jr., 2009), was published for homogenising huge temperature datasets, as found in the United States. The detection part of USHCN includes the early version of SNHT, cutting algorithm, Bayesian-based decisions about the form of inhomogeneities, i.e.…”

Section: The Home Periodmentioning

confidence: 99%

The historical pathway towards more accurate homogenisation

et al. 2012

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Abstract. In recent years increasing effort has been devoted to objectively evaluate the efficiency of homogenisation methods for climate data; an important effort was the blind benchmarking performed in the COST Action HOME (ES0601). The statistical characteristics of the examined series have significant impact on the measured efficiencies, thus it is difficult to obtain an unambiguous picture of the efficiencies, relying only on numerical tests. In this study the historical methodological development with focus on the homogenisation of surface temperature observations is presented in order to view the progress from the side of the development of statistical tools. The main stages of this methodological progress, such as for instance the fitting optimal step-functions when the number of change-points is known (1972), cutting algorithm (1995), CaussinusLyazrhi criterion (1997), are recalled and their effects on the quality-improvement of homogenisation is briefly discussed. This analysis of the theoretical properties together with the recently published numerical results jointly indicate that, MASH, PRODIGE, ACMANT and USHCN are the best statistical tools for homogenising climatic time series, since they provide the reconstruction and preservation of true climatic variability in observational time series with the highest reliability. On the other hand, skilled homogenizers may achieve outstanding reliability also with the combination of simple statistical methods such as the Craddock-test and visual expert decisions. A few efficiency results of the COST HOME experiments are presented to demonstrate the performance of the best homogenisation methods.

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Homogenization of Temperature Series via Pairwise Comparisons

Cited by 288 publications

References 39 publications

Expanding HadISD: quality-controlled, sub-daily station data from 1931

Expanding HadISD: quality-controlled, sub-daily station data from 1931

The Spatial and Temporal Variation of Temperature in the Qinghai-Xizang (Tibetan) Plateau during 1971–2015

The historical pathway towards more accurate homogenisation

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