Abstract
At thousands of stations worldwide, the mean daily surface air temperature is estimated as a mean of the daily maximum (T max) and minimum (T min) temperatures. We use the NOAA Surface Radiation Budget Network (SURFRAD) of seven US stations with surface air temperature recorded each minute to assess the accuracy of the mean daily temperature estimate as an average of the daily maximum and minimum temperatures and to investigate how the accuracy of the estimate increases with an increasing number of daily temperature observations. We find the average difference between the estimate based on an average of the maximum and minimum temperatures and the average of 1440 1-min daily observations to be − 0.05 ± 1.56 °C, based on analyses of a sample of 238 days of temperature observations. Considering determination of the daily mean temperature based on 3, 4, 6, 12, or 24 daily temperature observations, we find that 2, 4, or 6 daily observations do not reduce significantly the uncertainty of the daily mean temperature. The bias reduction in a statistically significant manner (95% confidence level) occurs only with 12 or 24 daily observations. The daily mean temperature determination based on 24 hourly observations reduces the sample daily temperature uncertainty to − 0.01 ± 0.20 °C. Estimating the parameters of population of all SURFRAD observations, the 95% confidence intervals based on 24 hourly measurements is from − 0.025 to 0.004 °C, compared to a confidence interval from − 0.15 to 0.05 °C based on the mean of T max and T min.
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Acknowledgements
The reported research (LA-UR-17-21392) was partially supported by the Los Alamos National Laboratory, Earth and Environmental Sciences and by NOAA, Earth System Research Laboratory internal resources. The SURFRAD data used in this report are publically available at the website ftp://aftp.cmdl.noaa.gov/data/radiation/surfrad/RadFlux/
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Chylek, P., Augustine, J.A., Klett, J.D. et al. Daily mean temperature estimate at the US SURFRAD stations as an average of the maximum and minimum temperatures. Theor Appl Climatol 134, 337–345 (2018). https://doi.org/10.1007/s00704-017-2277-4
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DOI: https://doi.org/10.1007/s00704-017-2277-4