A Method for Monthly Detection of Inhomogeneities and Errors in Daily Maximum and Minimum Temperatures

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.