Timothy W. Owen

Satellite-Based Adjustments for the Urban Heat Island Temperature Bias

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 (Tsfc). 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 Tsfc. 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 Tsfc. 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 sate...

Temperature Trends of the U.S. Historical Climatology Network Based on Satellite-Designated Land Use/Land Cover

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, from20.41 for the rural stations to 20.86 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 20.41 for the OLS defined stations to20.67 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.

Global rural temperature trends

Using rural/urban land surface classifications derived from maps and satellite observed nighttime surface lights, global mean land surface air temperature time series were created using data from all weather observing stations in a global temperature data base and from rural stations only. The global rural temperature time series and trends are very similar to those derived from the full data set. Therefore, the well-known global temperature time series from in situ stations is not significantly impacted by urban warming.

Urban Heat Island Assessment: Metadata Are Important

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 ...

Assessment of Urban Heat Islands: A Multi-Sensor Perspective for the Dallas-Ft. Worth, USA Region

Abstract Data acquired during the early to mid‐1990s by several satellite‐sensor systems were combined in an assessment of the urban heat‐island effect for the Dallas‐Fort Worth, TX region of the United States. Normalized difference vegetation index and radiant surface temperature were computed from NOAA‐AVHRR data. Two measures of the anthropogenic light emitted by urban‐related surface features were available from the DMSP‐OLS. Landsat MSS data were used to provide estimates of the predominant land cover within the grid cells associated with the NOAA‐AVHRR and DMSP‐OLS data. The multi‐sensor analysis of the environment associated with seven climate observation stations in the Dallas‐Ft. Worth region provided a methodology for characterization of the stations as “urban”; or “rural.”; Three of the seven stations examined were identified through this analyis as “urban.”; The information provided by a single sensor, while valuable, was clearly enhanced by the use of the multiple sensors included in this study.

Monthly fractional green vegetation cover associated with land cover classes of the conterminous USA

The land cover classes developed under the coordination of the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) have been analyzed for a study area that includes the Conterminous United States and portions of Mexico and Canada. The 1-km resolution data have been analyzed to produce a gridded data set that includes within each 20-km grid cell: 1) the three most dominant land cover classes, 2) the fractional area associated with each of the three dominant classes, and 3) the fractional area covered by water. Additionally, the monthly fraction of green vegetation cover (fgreen) associated with each of the three dominant land cover classes per grid cell was derived from a 5-year climatology of 1-km resolution NOAA-AVHRR data. The variables derived in this study provide a potential improvement over the use of monthly fgreen linked to a single land cover class per model grid cell.

Updated Population Metadata for United States Historical Climatology Network Stations

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 m...

Characteristics of seasonal vegetation cover in the conterminous USA

A data set of the fractional green vegetation cover (FGREEN) for the Conterminous USA was evaluated for regional and seasonal variation. The value of FGREEN was derived monthly for the three most dominant land cover classes per 20 km by 20 km grid cell within the study area. At this grid cell resolution (comprised of 400 1-km pixels), 97 percent of the grid cells included three or fewer land cover classes. FGREEN was found to vary regionally due to local land cover and climate variations. FGREEN was found significantly different between one or more of the land cover classes, for one or more months, in 58 percent of the grid cells included in the study. Monthly FGREEN values for the land cover classes vary sufficiently between the land cover classes to warrant monthly FGREEN data for each of the one to three most dominant land cover classes per grid cell.