Biases in mean temperatures due to differing times of daily maximum and minimum temperature observation cause problems in evaluation of temporal and spatial anomalies in temperature and derived degree day values. These biases were examined using six years (1973–78) of digitized hourly temperature data taken at Oneonta, New York. An annual mean temperature difference of 2.5°F is noted between means computed with the 0600 LST and 1500 IST observation times, with individual monthly differences as high as 4.4°F. Maximum seasonal degree day biases were 743 heating degree days (HDD) (10.2%), 169 cooling degree days (CDD) (43.3%), and 299 growing degree days (GDD) (14.3%).

A modified version of the Blackburn method for adjusting mean temperature data for observation time bias is presented. The modified method involves adjusting data to a “true” mean obtained by averaging all hourly temperature values for the 24-hour period ending at midnight, rather than adjusting to the midnight standard observational mean obtained by averaging the maximum and minimum values over the same period. The adjustments are applied to mean temperatures from stations with different observation times in the region around Oneonta, resulting in spatial analysis fields which are believed to be more representative than those using the published data. This suggests that application of such an adjustment scheme results in a more homogeneous climatological data set.

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