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Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 2: Estimation of the urban biasA 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 (ECMWF) 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 x 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 deg x 1 deg 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 (COOP) Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network (GHCN). 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. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available.
Document ID
19950047979
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Epperson, David L.
(North Carolina State University Raleigh, NC, United States)
Davis, Jerry M.
(North Carolina State University Raleigh, NC, United States)
Bloomfield, Peter
(North Carolina State University Raleigh, NC, United States)
Karl, Thomas R.
(NOAA/NESDIS/National Climatic Data Center Asheville, NC, United States)
Mcnab, Alan L.
(NOAA/NESDIS/National Climatic Data Center Asheville, NC, United States)
Gallo, Kevin P.
(NOAA/NESDIS/National Climatic Data Center Asheville, NC, United States)
Date Acquired
August 16, 2013
Publication Date
February 1, 1995
Publication Information
Publication: Journal of Applied Meteorology
Volume: 34
Issue: 2
ISSN: 0894-8763
Subject Category
Meteorology And Climatology
Accession Number
95A79578
Distribution Limits
Public
Copyright
Other

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