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The article examines the impact of different averaging algorithms on daily maximum and minimum temperature. The authors found out that some surface climate networks produced a systematic warming or cooling bias in daily maximum or minimum temperature observation which led to resulting biases making the diurnal temperature range more biased in extreme climate studies, in comparison to the longest recorded and standard liquid-in-glass maximum and minimum thermometers. They recommend providing an accurate description of daily maximum and minimum temperatures to prevent the uncertainties in the observed climatology.

The effect of missing data can result in errors that exhibit temporal and spatial patterns in climatological and meteorological research applications. Many climate related tools perform best with a serially complete dataset (SCD). To support the National Agricultural Decision Support System (NADSS), a SCD with no missing data values for daily temperature and precipitation for the United States was developed using a self-calibrating data quality control (QC) library. The library performs two primary functions: identifies outliers and provides estimates to replace missing data values and outliers. This study presents the development of the SCD and the QC library in detail. An in-depth evaluation in terms of root mean square error (RMSE) and mean absolute error (MAE) for the SCD for the period of 1975 - 2004 is provided. The study shows an impressively low average RMSE in the range of 2.27 to 3.58°F for temperature and 0.07 to 0.23 inch for precipitation for the whole country for 30 years. The goal of this study is to enhance drought risk assessment and environmental risk analysis. [ABSTRACT FROM AUTHOR]

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]