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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.

Soil moisture, evapotranspiration, and other major water balance components were investigated for six Nebraska Sandhills locations during a 6 year period (1998–2004) using a hydrological model. Annual precipitation in the study period ranged from 330 to 580 mm. Soil moisture was measured continuously at 10, 25, 50, and 100 cm depth at each site. Model estimates of surface (0–30 cm), subsurface (30–91 cm), and root zone (0–122 cm) soil moisture were generally well correlated with observed soil moisture. The correlations were poorest for the surface layer, where soil moisture values fluctuated sharply, and best for the root zone as a whole. Modeled annual estimates of evapotranspiration and drainage beneath the rooting zone showed large differences between sites and between years. Despite the Sandhills’ relatively homogeneous vegetation and soils, the high spatiotemporal variability of major water balance components suggest an active interaction among various hydrological processes in response to precipitation in this semiarid region. [ABSTRACT FROM AUTHOR]

In 2004 a new aspirated surface air temperature system was officially deployed nationally in the U.S. Climate Reference Network (USCRN) commissioned by the National Oceanic and Atmospheric Administration. The primary goal of the USCRN is to provide future long-term and high-quality homogeneous observations of surface air temperature and precipitation that can be coupled to past long-term observations for the detection and attribution of present and future climate change. In this paper two precision air temperature systems are included for evaluating the new USCRN air temperature system based on a 1-yr side-by-side field comparison. The measurement errors of the USCRN temperature sensor are systematically analyzed, and the components of error attributable to the datalogger, lead wires, fixed resistors, and the temperature coefficient of the resistors are presented. Although the current configuration is adequate, a more desirable configuration of USCRN temperature sensor coupled with the datalogger is proposed as a means of further reducing the uncertainty for the USCRN temperature measurement. [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]

The biases of four commonly used air temperature sensors are examined and detailed. Each temperature transducer consists of three components: temperature sensing elements, signal conditioning circuitry, and corresponding analog-to-digital conversion devices or dataloggers. An error analysis of these components was performed to determine the major sources of error in common climate networks. It was found that, regardless of microclimate effects, sensor and electronic errors in air temperature measurements can be larger than those given in the sensor manufacturer's specifications. The root-sum-of-squares (RSS) error for the HMP35C sensor with CR10X datalogger was above 0.2°C, and rapidly increases for both lower (<-20°C) and higher temperatures (>30°C). Likewise, the largest errors for the maximum–minimum temperature system (MMTS) were at low temperatures (<-40°C). The temperature linearization error in the HO-1088 hygrothermometer produced the largest errors when the temperature was lower than -20°C. For the temperature sensor in the U.S. Climate Reference Networks (USCRN), the error was found to be 0.2° to 0.33°C over the range -25° to 50°C. The results presented here are applicable when data from these sensors are applied to climate studies and should be considered in determining air temperature data continuity and climate data adjustment models. [ABSTRACT FROM AUTHOR]

Temperature and rainfall are important climatological parameters, and knowledge of their temporal and spatial patterns is useful for researchers working in many disciplines. In this study, spatial interpolation techniques were implemented in a Geographic Information System (GIS) to study the spatial variability of climate variables (maximum air temperature, minimum air temperature, and seasonal and annual rainfall) in Nebraska. Thirty years (1971-2000) of climate data (average monthly maximum and minimum temperatures and rainfall) from 215 National Weather Service Cooperative Observer Network (COOP) weather stations distributed throughout Nebraska and surrounding states were used in the analyses. Literature suggests that there is no single preferred method of interpolation, and the selection of interpolation method is usually based on the available data, desired level of accuracy, and available resources. We analyzed three different commonly used interpolation methods (inverse distance weighted, spline, and kriging) and evaluated their performance. Overall, the summary of all statistical parameters showed no significant difference between interpolation techniques in predicting the spatial variability in 30-year climate normals. Investigation of interpolation errors at individual weather stations agreed with summary statistics. Spatial variability, in this instance, is likely smoothed due to long-term averaging of the data (30 years), resulting in similar errors for all the interpolation techniques. Subjective assessment of maps for all climate variables showed that the kriging method produced smoother maps compared to spline and inverse distance weighted. Considering the degree to which accurate spatial interpolation could be accomplished with relative ease and less bias, the spline method proves the better option. [ABSTRACT FROM AUTHOR]