What are daily maximum and minimum temperatures in observed climatology?

Abstract

Instrumental daily maximum and minimum temperatures are reported and archived from various surface thermometers along with different average algorithms in historical and current U.S. surface climate networks. An instrumental bias in daily maximum and minimum temperatures caused by surface temperature sensors due to the different sampling rates, average algorithms, and sensor's time constants was examined using a Gaussian-distributed function of surface air temperature fluctuations in simulation. In this study, the field observations were also included to examine the effects of average algorithms used in reporting daily maximum and minimum temperatures. Compared to the longestrecorded and standard liquid-in-glass maximum and minimum thermometers, some surface climate networks produced a systematic warming (cooling) bias in daily maximum (minimum) temperature observations, thus, resulting biases made the diurnal temperature range (DTR) more biased in extreme climate studies. Our study clarified the ambiguous concepts on daily maximum and minimum temperature observations defined by the World Meteorological Organization (WMO) in terms of sensor's time constants and average lengths and an accurate description of daily maximum and minimum temperatures is recommended to avoid the uncertainties occurred in the observed climatology.

Response times of meteorological air temperature sensors

“…It is for this reason that WMO recommend (WMO, 2014, section 2.1.3.3 and Annex 1E) sampling air temperature every 5–10 s where feasible to do so, and averaging these samples to derive 60 s running means; and further that the highest and lowest (respectively) of the 60 s running average samples be logged as the day's maximum and minimum air temperatures. A consistent approach to sensor time constant and averaging time would improve consistency within and between station networks, as previously noted by Lin and Hubbard (2008), and would over time benefit the consistency of long‐term climatological records of maximum and minimum temperatures and diurnal temperature range (Thorne et al ., 2016) – albeit at the risk of introducing some inhomogeneity at changeover unless both “old” and “new” records were maintained in parallel for an overlap period. To evaluate their suitability for meteorological air temperature records, measurements of the time constants of representative commercial sensors were determined by laboratory experiment and the results compared with a theoretical model.…”

“…This is despite acknowledged recognition of the importance of sensor response time on meteorological temperature measurements, particularly maximum and minimum air temperatures, and the implications of differing sensor response times within a heterogeneous meteorological network are significant. A study by Lin and Hubbard (2008) noted instrumental biases in daily maximum and minimum air temperatures and diurnal temperature range resulting from variations in sampling rates, averaging algorithms and sensor time constants (implying degradation in between‐site comparisons, whether in real‐time or within long‐term records), and recommended that such variations be reduced as far as possible to minimise resulting uncertainties in climatological datasets. More recently, Thorne et al .…”

Metrological evaluation of the effect of the presence of a road on near‐surface air temperatures

“…• First, 10-min averaged data is much more frequently used for atmospheric science analysis because it reduces turbulence and boundary-layer issues (Lin and Hubbard, 2008;WMO, 2012). In this way, temperature spikes are smoothed and only significant ΔT i can be examined.…”

Statistical Forecast of Daily Maximum Air Temperature in Arid Areas at Summertime

“…These statistical parameters could improve the forecasts and the forecast errors exhibited less variability during summer months than they did over the rest of the year. Lin & Hubbard [11] investigated the instrumental bias in resulting from surface temperature sensors due to sampling rates and average algorithms. When comparing the climate and standard liquid-in-glass observations they found that the resulting biases made the diurnal temperature range more biased in papers on extreme climates.…”