Analyzing the impact of automatization using parallel daily mean temperature series including breakpoint detection and homogenization

Abstract

High-quality time series of meteorological observations are required for reliable assessments of climate trends. To analyze inhomogeneities in time series, parallel measurements can be used. Germany's national meteorological service DWD (Deutscher Wetterdienst) operates a network of climate reference stations. At these stations, manual and automatic observations have been taken in parallel. These parallel measurements therefore allow analyzing the impact of the transition on the homogeneity of time series of several meteorological parameters. Here, we present results for temperature. The differences between automatic and manual measurements are tested on breakpoints caused by instrumental defects or changes in the measurement conditions. The time series are highly correlated such that small breaks can be identified. The detected breakpoints are verified against metadata if available. In the case of no available metadata information, a procedure is suggested to identify the inhomogeneous time series (manual or automatic time series). Afterwards, the time series are homogenized. The homogenized time series are used to analyze the impact of changing the observing system from manual to automatic measurements on daily mean temperature.

Homogenization of German daily and monthly mean temperature time series

“…As described in Hannak et al (2020), the correction factor is the difference of the mean deviation between the time series under study and the reference series of the training period (recent segment) and the break segment. The correction factor is determined individually for each month of the year.…”

“…If ‘uniseg’ is not able to detect a break in this time range using daily data, the break date of using monthly data is used for further steps. An example is illustrated in Figure 3 (see Hannak et al, 2020).…”

“…Changes in the geographical position of the station (station relocation), measurement environment (e.g., growing trees), measurement practice (e.g., observation times), measurement systems (e.g., automatization) and used formulas and time ranges (e.g., to calculate daily mean or daily extreme values) can potentially result in breakpoints in long time series (WMO-No.1245, 2020. One example of an inhomogeneous temperature time series is Hohenpeißenberg, analysed and homogenized in the study of Winkler (2009).…”

“…These investigations conclude that in Germany, there is no risk of inhomogeneities in long time series of daily mean temperature caused by the automatization. To study the effect of breakpoints and homogenization during the time period of parallel measurements, Hannak et al (2020) homogenized the parallel temperature measurements and compared the results to the study of Kaspar et al (2016). The results are comparable, the breaks during the parallel measurement period (about 10 years of data) compensate each other and the mean differences between automatic and manual measurements are close to zero.…”

Response times of meteorological air temperature sensors

“…In Europe, Hannak et al . (2020) examined the variable impact of site automation using parallel daily mean temperature series. In addition, the bringing together of meteorology and metrology groups within Europe to work on areas of common importance within recent years has, and continues to bring, both clarity and benefits to meteorological metrology (Merlone et al ., 2015) and a forensic examination of environmental extremes (Merlone et al ., 2019).…”