Station metadata plays a critical role in the accurate assessment of climate data and eventually of climatic change, climate variability, and climate prediction. However, current procedures of metadata collection are insufficient for these purposes. This paper introduces the GeoProfile as a model for documenting and visualizing enhanced spatial metadata. In addition to traditional metadata archiving, GeoProfiles integrate meso-scale topography, slope, aspect, and land-use data from the vicinity of climate observing stations (http://kyclim.wku.edu/tmp/geoprofiles/geoprofiles_main.html). We describe how GeoProfiles are created using Geographical Information Systems (GIS) and demonstrate how they may be used to help identify measurement bias in climate observations due to undesired instrument exposures and the subsequent forcings of micro- and meso-environments. A study involving 12 COOP and US Historical Climate Network (USHCN) stations finds that undesirable instrument exposures associated with both anthropogenic and natural influences resulted in biased measurement of temperature. Differences in average monthly maximum and minimum temperatures between proximate stations are as large as 1.6 and 3.8 °C, respectively. In addition, it is found that the difference in average extreme monthly minimum temperatures can be as high as 3.6 °C between nearby stations, largely owing to the differences in instrument exposures. Likewise, the difference in monthly extreme maximum temperatures between neighboring stations are as large as 2.4 °C. This investigation finds similar differences in the diurnal temperature range (DTR). GeoProfiles helped us to identify meso-scale forcing, e.g. instruments on a south-facing slope and topography, in addition to forcing of micro-scale setting. Copyright © 2006 Royal Meteorological Society.