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  • Author or Editor: Matthew J. Menne x
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Matthew J. Menne
and
Claude E. Duchon

Abstract

Two statistical tests are described that can be used to detect potential inhomogeneities and errors in daily temperature observations. These tests, based on neighbor comparisons, differ from existing inhomogeneity tests by evaluating daily rather than monthly or annual observations and by focusing on a very short record length. Standardized difference series one month in length are formed between a candidate station, whose daily temperature time series is being evaluated, and a number of neighboring stations. These series, called D-series, approximate white noise when a candidate is like its neighbors and are other than white noise when the candidate is unlike its neighbors. Two white noise tests are then applied to the D-series in order to detect potential problems at the candidate station: a cross-correlation test and a lag 1 (1-day) autocorrelation test. Examples of errors and inhomogeneities detected through the application of the two tests on observations from the National Weather Service's Cooperative Observer Network are provided. These tests were designed specifically to detect inhomogeneities in an operational environment, that is, while data are being routinely processed. When a potential inhomogeneity is identified, timely action can be taken and feedback given, if necessary, to station field managers to prevent further corruption of the data record. While examples are provided using observations from the Cooperative Observer Network, these tests may be used in any temperature observation network with sufficient station density to provide a pool of neighboring stations.

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Matthew J. Menne
,
Imke Durre
,
Russell S. Vose
,
Byron E. Gleason
, and
Tamara G. Houston

Abstract

A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic bias).

Daily updates are provided for many of the station records in GHCN-Daily. The dataset is also regularly reconstructed, usually once per week, from its 20+ data source components, ensuring that the dataset is broadly synchronized with its growing list of constituent sources. The daily updates and weekly reprocessed versions of GHCN-Daily are assigned a unique version number, and the most recent dataset version is provided on the GHCN-Daily website for free public access. Each version of the dataset is also archived at the NOAA/National Climatic Data Center in perpetuity for future retrieval.

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