A Comparison of Daily Temperature Averaging Methods: Spatial Variability and Recent Change for the CONUS

A Comparison of Daily Temperature Averaging Methods: Spatial Variability and Recent Change for... AbstractTraditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin) over a 24-hour period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-year normals) and recent temperature trends and changes. However, many first-order weather stations-- such as those at airports-- also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day’s temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging (i.e., [Tmax + Tmin]/2, average of 24 hourly temperature values) for 215 first-order weather stations across the conterminous United States (CONUS) the 30-year period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method ([Tmax + Tmin]/2), particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice- daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially-coherent pattern in the difference compared to the most recent part of the temperature record (2001-2015). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Climate American Meteorological Society

A Comparison of Daily Temperature Averaging Methods: Spatial Variability and Recent Change for the CONUS

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0442
D.O.I.
10.1175/JCLI-D-17-0089.1
Publisher site
See Article on Publisher Site

Abstract

AbstractTraditionally, the daily average air temperature at a weather station is computed by taking the mean of two values, the maximum temperature (Tmax) and the minimum temperature (Tmin) over a 24-hour period. These values form the basis for numerous studies of long-term climatologies (e.g., 30-year normals) and recent temperature trends and changes. However, many first-order weather stations-- such as those at airports-- also record hourly temperature data. Using an average of the 24 hourly temperature readings to compute daily average temperature has been shown to provide a more precise and representative estimate of a given day’s temperature. This study assesses the spatial variability of the differences in these two methods of daily temperature averaging (i.e., [Tmax + Tmin]/2, average of 24 hourly temperature values) for 215 first-order weather stations across the conterminous United States (CONUS) the 30-year period 1981-2010. A statistically significant difference is shown between the two methods, as well as consistent overestimation of temperature by the traditional method ([Tmax + Tmin]/2), particularly in southern and coastal portions of the CONUS. The explanation for the long-term difference between the two methods is the underlying assumption for the twice- daily method that the diurnal curve of temperature is symmetrical. Moreover, this paper demonstrates a spatially-coherent pattern in the difference compared to the most recent part of the temperature record (2001-2015). The spatial and temporal differences shown have implications for assessments of the physical factors influencing the diurnal temperature curve, as well as the exact magnitude of contemporary climate change.

Journal

Journal of ClimateAmerican Meteorological Society

Published: Nov 20, 2017

References

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