Estimating the Urban Bias of Surface Shelter Temperatures Using Upper-Air and Satellite Data. Part II: Estimation of the Urban Bias

Abstract

A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

Using subsurface temperatures to derive the spatial extent of the land use change effect

“…In areas with vegetation, the NDVI is normally greater than 0.03 for vacant spaces with bare soil, seasonal vegetation and agricultural classes. Higher NDVI values are associated with high-density green areas that commonly occur in mountain forests (Epperson and Davis, 1995). Fig.…”

“…To analyze urban land cover/vegetation cover characteristics, the Normalized Difference Vegetation Index (NDVI) (Eq. (8)), one of the most commonly adopted urban-rural landscape classification techniques, was employed (Epperson and Davis, 1995;Weng et al, 2004;Hung et al, 2006). The Panchromatic band at a 15-m spatial resolution was used with a pan-sharpening technique to obtain a higher resolution classification map.…”

Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island

“…Ground-based thermal remote sensing (and aircraft-based thermography at a low enough elevation to resolve streets, roofs and walls) permits definition of yet another UHI, namely that for the ground surface. Hall et al (1999); Hoyano et al (1999); Murakami and Mochida (1989): Smith et al (2001) Urban air pollution dispersion Grant and Wong (1999); Hanna and Chang (1992); Kotake and Sano (1981); Taha (1997) Urban design and planning de Schiller and Evans (1996); Oke ( , 1988a; Scherer et al (1999) Biometeorology and human comfort in towns Burt et al (1982); de Assis and Frota (1999); Pearlmutter et al (1999) Road climatology in towns Shao and Lister (1995); Shao et al (1994) Energy conservation issues Bretz et al (1998); Rosenfeld et al (1995); Sailor (1998); Simpson and McPherson (1998) Historic and cultural preservation Camuffo et al (1999) Global warming research Epperson et al (1995); Hansen et al (2001); Jones et al (1989); Kukla et al (1986) Though surface temperatures show some similar spatial and temporal patterns to those for air temperatures, this correspondence is not exact. In particular, under calm, clear, nocturnal conditions, they generally display a much stronger dependence on microscale site characteristics, especially sky view factor reduction brought about by street geometry, than do simultaneously evaluated air temperatures (Bärring et al, 1985;Eliasson, 1990Eliasson, -91, 1996a.…”

Remote Sensing of Heat-Related Health Risks: The Trend Toward Coupling Socioeconomic and Remotely Sensed Data

“…The UHI effect is one of the oldest known issues related to urban climatology as was initially noted in the late 1800s in London, UK (Epperson et al. 1995; Gallo and Owen 1999; Lo et al.…”