Abstract
AbstractHigh‐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.
Highlights
Parallel measurements provide information on how changes in the observing system can affect time series
High-quality time series of meteorological observations are required for reliable assessments of climate trends
These parallel measurements allow analyzing the impact of the transition on the homogeneity of time series of several meteorological parameters
Summary
Parallel measurements provide information on how changes in the observing system can affect time series. We use parallel measurements of temperature, aggregated to daily mean values, to detect breaks and to homogenize these time series. To detect breaks in time series, differences of automatic minus manual daily mean values (difference series) are used. The R-function ‘uniseg’ (part of the R package ‘cghseg’) was originally developed for TABLE 1 Time range with parallel measurements; location and elevation (in meters), pairs of data, and number of outliers of each climate reference station (Hannak et al, 2019). The first step in the breakpoint detection procedure is the calculation of monthly mean differences between automatic and manual measurements. In a given time range around the breaks, all breaks detected by ‘uniseg’ (using the differences of automatic/manual observations minus the reference time series) are counted. With the detected breakpoints and the information about the inhomogeneous time series (manual or automatic), the data can be homogenized.
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