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Timothy W. Owen and Kevin P. Gallo

Abstract

The United States Historical Climatology Network (HCN) serial temperature dataset is comprised of 1221 high-quality, long-term climate observing stations. The HCN dataset is available in several versions, one of which includes population-based temperature modifications to adjust urban temperatures for the “heat-island” effect. Unfortunately, the decennial population metadata file is not complete as missing values are present for 17.6% of the 12 210 population values associated with the 1221 individual stations during the 1900–90 interval. Retrospective grid-based populations, within a fixed distance of an HCN station, were estimated through the use of a gridded population density dataset and historically available U.S. Census county data. The grid-based populations for the HCN stations provide values derived from a consistent methodology compared to the current HCN populations that can vary as definitions of the area associated with a city change over time. The use of grid-based populations may minimally be appropriate to augment populations for HCN climate stations that lack any population data, and are recommended when consistent and complete population data are required. The recommended urban temperature adjustments based on the HCN and grid-based methods of estimating station population can be significantly different for individual stations within the HCN dataset.

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Thomas C. Peterson and Timothy W. Owen

Abstract

Urban heat island (UHI) analyses for the conterminous United States were performed using three different forms of metadata: nightlights-derived metadata, map-based metadata, and gridded U.S. Census Bureau population metadata. The results indicated that metadata do matter. Whether a UHI signal was found depended on the metadata used. One of the reasons is that the UHI signal is very weak. For example, population was able to explain at most only a few percent of the variance in temperature between stations. The nightlights metadata tended to classify lower population stations as rural compared to map-based metadata while the map-based metadata urban stations had, on average, higher populations than urban nightlights. Analysis with gridded population metadata indicated that statistically significant urban heat islands could be found even when quite urban stations were classified as rural, indicating that the primary signal was coming from the relatively high population sites. If ∼30% of the highest population stations were removed from the analysis, no statistically significant urban heat island was detected. The implications of this work on U.S. climate change analyses is that, if the highest population stations are avoided (populations above 30 000 within 6 km), the analysis should not be expected to be contaminated by UHIs. However, comparison between U.S. Historical Climatology Network (HCN) time series from the full dataset and a subset excluding the high population sites indicated that the UHI contamination from the high population stations accounted for very little of the recent warming.

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Kevin P. Gallo and Timothy W. Owen

Abstract

Monthly and seasonal relationships between urban–rural differences in minimum, maximum, and average temperatures measured at surface-based observation stations were compared to satellite-derived Advanced Very High Resolution Radiometer estimates of a normalized difference vegetation index (NDVI) and surface radiant temperature (T sfc). The relationships between surface- and satellite-derived variables were developed during 1989–91 and tested on data acquired during 1992–93. The urban–rural differences in air temperature were linearly related to urban–rural differences in the NDVI and T sfc. A statistically significant but relatively small (less than 40%) amount of the variation in these urban–rural differences in air temperature [the urban heat island (UHI) bias] was associated with variation in the urban–rural differences in NDVI and T sfc. A comparison of the satellite-based estimates of the UHI bias with population-based estimates of the UHI bias indicated similar levels of error. The use of satellite-derived data may contribute to a globally consistent method for analysis of the urban heat island bias.

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Thomas Reek, Stephen R. Doty, and Timothy W. Owen

It is widely known that the TD3200 (Summary of the Day Cooperative Network) database held by the National Climatic Data Center contains tens of thousands of erroneous daily values resulting from data-entry, data-recording, and data-reformatting errors. TD3200 serves as a major baseline dataset for detecting global climate change. It is of paramount importance to the climate community that these data be as error-free as possible. Many of these errors are systematic in nature. If a deterministic approach is taken, using empirically developed criteria, many if not most of these errors can be corrected or removed. A computer program utilizing Backus Normal Form structure design and a series of chain-linked tests in the form of encoded rules has been developed as a means of modeling the human subjective process of inductive data review. This objective automated correction process has proven extremely effective. A manual review and validation of 138 stations of a 1300-station subset of TD3200 data closely matched the automated correction process. Applications of this technique are expected to be utilized in the production of a nearly error-free TD3200 dataset.

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Kevin P. Gallo, Timothy W. Owen, David R. Easterling, and Paul F. Jamason

Abstract

The 1221 weather observation stations that compose the U.S. Historical Climatology Network were designated as either urban, suburban, or rural based on data from the Defense Meteorological Satellite Program Operational Linescan System (OLS). The designations were based on local and regional samples of the OLS data around the stations (OLS method). Trends in monthly maximum and minimum temperature and the diurnal temperature range (DTR) were determined for the 1950–96 interval for each of three land use/land cover (LULC) designations. The temperature trends for the OLS-derived designations of LULC were compared to similarly designated LULC based on (i) map- (Operational Navigation Charts) and population-based estimates of LULC (ONCP method), and (ii) LULC designations that resulted from of a survey of the network station operators. Although differences were not statistically significant, the DTR trends (degrees Celsius per 100 years) did differ between the LULC classes defined by the OLS method, from −0.41 for the rural stations to −0.86 for the urban stations. Trends also differed, although not significantly, between the methods used to define an LULC class, such that the trends in rural DTR varied from −0.41 for the OLS defined stations to −0.67 for the ONCP defined stations. Although the trends between classes were not significantly different, they do present some contrasts that might confound the interpretation of temperature trends when the local and regional environments associated with the analyzed stations are not considered. The general (urban, suburban, or rural) LULC associated with surface observation stations appears to be one of the factors that can influence the trends observed in temperatures and thus should be considered in the analysis and interpretation of temperature trends.

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Anthony Arguez, Imke Durre, Scott Applequist, Russell S. Vose, Michael F. Squires, Xungang Yin, Richard R. Heim Jr., and Timothy W. Owen

The National Oceanic and Atmospheric Administration (NOAA) released the 1981–2010 U.S. Climate Normals in July 2011, representing the latest decadal installment of this long-standing product line. Climatic averages (and other statistics) of temperature, precipitation, snowfall, and numerous derived quantities were calculated for ~9,800 stations operated by the U.S. National Weather Service (NWS). They include estimated normals, or “quasi normals,” for approximately 2,000 active short-record stations such as those in the U.S. Climate Reference Network. The 1981–2010 installment features several new products and methodological enhancements: 1) state-of-the-art temperature homogenization at the monthly scale, 2) extensive utilization of quality-controlled daily climate data, 3) new statistical approaches for calculating daily temperature normals and heating and cooling degree days, and 4) a comprehensive suite of precipitation, snowfall, and snow depth statistics. This paper provides a general overview of this new suite of climate normals products.

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