A Bayes Factor Model for Detecting Artificial Discontinuities via Pairwise Comparisons

Zhang, Jun; Zheng, Wei; Menne, Matthew J.

doi:10.1175/jcli-d-12-00052.1

Cited by 5 publications

(2 citation statements)

References 28 publications

(42 reference statements)

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“…Bayesian and wavelet approaches to monthly temperature homogenization also exist (e.g. Li et al ; Zhang et al ). ACMANTv2.1, a recent online tool for homogenizing precipitation or temperature at the monthly level (Domonkos ), is available at <http://http://www.c3.urv.cat/members/softpeter.html>.…”

Section: Statistical Processingmentioning

confidence: 99%

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

Oswald

Dupigny‐Giroux

2015

Geography Compass

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Increased recognition of potential future climate changes has sparked an urgency in understanding the various linkages among weather, climate and sectors of society. Furthermore, the need for improving climate models is also paramount. Meteorological observations are critical for accomplishing both. A deeper knowledge of the origin, nuances and availability of observation-based climate data can lead to better analyses and validations. In this article, we review the suite of current meteorological observing networks across the continental U.S. In addition, we discuss both the statistical procedures that are used to construct climate data products from observing records, and the climate data products themselves. The focus here is on datasets with daily to hourly time steps, with an emphasis on gridded climate data products of spatial resolutions under 15 km. Ultimately, this paper is geared towards climatologists as well as those outside of the climate field, who need data to empower near-surface atmospheric science research at the local scale. Presently, new observing networks supported by massive volunteer communities are potential game changers, yet the public still lacks both daily precipitation data suitable for high-resolution and/or long-term analyses and rigorous validation of high-resolution gridded climate data products.

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Section: Statistical Processingmentioning

confidence: 99%

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

Oswald

Dupigny‐Giroux

2015

Geography Compass

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“…Large inhomogeneities are easier to detect than small ones so assessment could be split into inhomogeneity size categories (e.g. Zhang et al, 2012). This information is of importance to algorithm developers.…”

Section: Developing An Assessment System That Meets All Needsmentioning

confidence: 99%

A framework for benchmarking of homogenisation algorithm performance on the global scale

Willett

Williams

Jolliffe

et al. 2014

Geosci. Instrum. Method. Data Syst.

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Abstract. The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly mean land surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global-scale synthetic analogues to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real-world data do not afford us). Hence, algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Trends in Extreme U.S. Temperatures

Lee

Lund

2014

Journal of Climate

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This paper develops trend estimation techniques for monthly maximum and minimum temperature time series observed in the 48 conterminous United States over the last century. While most scientists concur that this region has warmed on aggregate, there is no a priori reason to believe that temporal trends in extremes and averages will exhibit the same patterns. Indeed, under minor regularity conditions, the sample partial sum and maximum of stationary time series are asymptotically independent (statistically). Previous authors have suggested that minimum temperatures are warming faster than maximum temperatures in the United States; such an aspect can be investigated via the methods discussed in this study. Here, statistical models with extreme value and changepoint features are used to estimate trends and their standard errors. A spatial smoothing is then done to extract general structure. The results show that monthly maximum temperatures are not often greatly changing-perhaps surprisingly, there are many stations that show some cooling. In contrast, the minimum temperatures show significant warming. Overall, the southeastern United States shows the least warming (even some cooling), and the western United States, northern Midwest, and New England have experienced the most warming.

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A Bayes Factor Model for Detecting Artificial Discontinuities via Pairwise Comparisons

Cited by 5 publications

References 28 publications

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

A framework for benchmarking of homogenisation algorithm performance on the global scale

Trends in Extreme U.S. Temperatures

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