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

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. Germany's national meteorological service (Deutscher Wetterdienst) operates a network of climate reference stations where manual and automatic observations have been taken in parallel. The differences of daily mean temperature data between automatic and manual measurements are tested on breakpoints and homogenized if possible. The homogenized time series are used to analyze the impact of changing the observing system from manual to automatic measurements with the result of no effect on the mean differences between the automatic and manual temperature measurements.