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Valid data are required to make climate assessments and to make climate-related decisions. The objective of this paper is threefold: to introduce an explicit treatment of Type I and Type II errors in evaluating the performance of quality assurance procedures, to illustrate a quality control approach that allows tailoring to regions and subregions, and to introduce a new spatial regression test. Threshold testing, step change, persistence, and spatial regression were included in a test of three decades of temperature and precipitation data at six weather stations representing different climate regimes. The magnitude of thresholds was addressed in terms of the climatic variability, and multiple thresholds were tested to determine the number of Type I errors generated. In a separate test, random errors were seeded into the data and the performance of the tests was such that most Type II errors were made in the range of ±1°C for temperature, not too different from the sensor field accuracy. The study underscores the fact that precipitation is more difficult to quality control than temperature. The new spatial regression test presented in this document outperformed all the other tests, which together identified only a few errors beyond those identified by the spatial regression test. [ABSTRACT FROM AUTHOR]

A new U.S. Climate Reference Network (USCRN) was officially and nationally commissioned by the Department of Commerce and the National Oceanic and Atmospheric Administration in 2004. During a 1-yr side-by-side field comparison of USCRN temperatures and temperatures measured by a maximum–minimum temperature system (MMTS), analyses of hourly data show that the MMTS temperature performed with biases: 1) a systematic bias–ambient-temperature-dependent bias and 2) an ambient-solar-radiation- and ambient-wind-speed-dependent bias. Magnitudes of these two biases ranged from a few tenths of a degree to over 1°C compared to the USCRN temperatures. The hourly average temperatures for the USCRN were the dependent variables in the development of two statistical models that remove the biases due to ambient temperature, ambient solar radiation, and ambient wind speed in the MMTS. The model performance was examined, and the results show that the adjusted MMTS data were substantially improved with respect to both systematic bias and the bias associated with ambient solar radiation and ambient wind speed. In addition, the results indicate that the historical temperature datasets prior to the MMTS era need to be further investigated to produce long-term homogenous times series of area-average temperature. [ABSTRACT FROM AUTHOR]