The study was conducted within the scope of the Hessian KLIMPRAX projects, which deal with local and regional impacts of anthropogenic climate change (HLNUG, 2018). The PLUVIO-OTT datasets of 47 stations of the regional water authority of Hesse (HLNUG) and 79 stations of the German Weather Service (DWD) were combined for the first time and analysed for the period 2000 to 2016.

Figure 1Map of Europe, showing the study area of Hesse, a state in Germany, its topography (altitude in meters above sea level) and the spatial distribution of all 126 measuring stations within this study.

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Figure 2Mean seasonal cycle of daily maximum values; 1 min peaks (a) and 15 min peaks (b), averaged over all years (2000 to 2016) and all stations (DWD: blue, HLNUG: red); moving averages (30 d) and 95 % confidence intervals (shaded).

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First, all precipitation days with a daily sum of 0.1 mm or more were extracted from the measuring period. In addition to the accumulated 24 h sum, the daily maximum was determined for every precipitation day at every station (e.g., the highest 1 min sum and the highest 15 min sum that occurred on that day). In order to compare the average climatologies of daily maxima, DWD und HLNUG data were examined separately (Fig. 2). Subsequently, all individual station values were merged together, resulting in a dataset of 245 427 precipitation days (117 680 of them during the summer half year). Based on this overall distribution, percentile-based threshold values – namely the 90th, 95th and 99th percentile – were calculated in terms of both daily sums and daily 15 min maxima.

Figure 3Percentage of high-intensity precipitation classes (> 90th/95th/99th percentile = P90/P95/P99) in relation to the total number of precipitation days (summed up over all stations and the whole measuring period 2000–2016) for daily 15 min peak values (above) and daily sums (below) during the summer half year (April to September), sorted according to different circulation patterns (GWL).

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In the next step, the data was sorted according to the large-scale state of the atmosphere that prevailed on the respective precipitation days. We employed atmospheric circulation patterns categorised according to Werner and Gerstengabe (2010), who distinguish between 29 different “Großwetterlagen” (GWL; see Fig. 3). Every precipitation day recorded at every station used in this study was allocated to one of those GWL in order to identify GWL prone to heavy precipitation.

Figure 4Relative exceedance frequency of the 90th percentile (P90) of daily 15 min peak values (left) and daily sums (right) for different air mass inflow directions: West (a), covering the GWL subtypes WA, WZ, WS and WW, and South (b), covering the GWL subtypes SA, SZ, TB and TRW; spatial distribution with nearest-neighbour interpolation.

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The exceptionally high number and good spatial distribution of available stations (Fig. 1) allowed to produce maps by interpolation using the nearest-neighbour method. After cartographical visualisation (Fig. 4), statements about the topography's impact on precipitation distribution could be derived.

The first part of the study aims to clarify whether the PLUVIO-OTT weighting technology, applied by two different measuring networks, provides plausible values that can be combined to one homogeneous dataset. In this context, the comparability of extreme values is of particular interest as this study focuses on intense precipitation events characterised by high precipitation sums within a short time period. Therefore, the average climatology of daily maxima (1 min) was calculated separately for HLNUG and DWD stations to be able to compare them with each other (Fig. 2a).

Both annual cycles show a reasonable maximum in summer due to the increased occurrence of convective rainfall at higher temperatures. However, large deviations between HLNUG and DWD data become apparent especially during the summer months. On average the DWD dataset contains significantly higher peaks than the HLNUG dataset.

This divergence cannot solely be explained by location factors, but must have further, technical causes. Most likely the software settings used by DWD and HLNUG are not exactly the same: Depending on these settings, the instrument automatically smoothens the recorded data (e.g., by averaging over several minutes). This method shall prevent measurement errors caused, e.g., by artificial irrigation or animals, but at the same time true precipitation peaks may be underestimated. In exchange with DWD staff, software-controlled smoothing during data logging turned out to be the most likely explanation why the HLNUG dataset contains significantly fewer/lower peak values than the DWD dataset (Thomas Deutschländer and Thomas Junghänel, personal communication, 2017). In order to be able to combine the two datasets, they are both aligned with each other by adding up the 1 min data to precipitation sums over several minutes. It turned out that beyond a duration level of 15 min the difference between the datasets gets negligibly small (Fig. 2b), which is why 15 min sums are used for all further calculations.

In the second part of the study, the influence of atmospheric circulation on heavy precipitation and threshold exceedances in Hesse is investigated. Figure 3 shows the percentage of three threshold exceedance classes (> 90th/95th/99th percentile) in relation to the total number of precipitation days (≥ 0.1 mm) allocated to different atmospheric circulation patterns (GWL) during the summer half year. If there is an above-average percentage of threshold exceedance during a certain state of the atmospheric circulation, the corresponding GWL is assumed to have an enhancing impact on summer precipitation intensity, either concerning short-term peaks (Fig. 3, above) or daily sums (Fig. 3, below).

For example, the percentage of heavy daily precipitation is particularly high during summerly TM days, when low pressure systems stagnate over Central Europe (Fig. 3, below). Also, warm air mass inflow from southern and eastern directions has a strong positive effect on precipitation intensity, especially concerning the short-term peaks (Fig. 3, above).

The resilience of the results for each GWL can be estimated by considering the total number of precipitation days listed in Fig. 3: For example, the rare occurrence of summer precipitation during some meridional circulation patterns (such as SEA or SA) led to a very sparse data basis – the corresponding results are therefore less significant than the results of GWL connected to a high amount of precipitation days (such as WZ, TRM, TRW).

As the total quantity of all stations is considered in Fig. 3, a high proportion of threshold exceedances could mean that high values were measured very frequently at certain stations and/or at many stations simultaneously. To get an impression of the spatial distribution, the exceedance frequency of the 90th percentile is interpolated over the whole study area in Fig. 4 (be aware that in contrast to Fig. 3 data of the whole year and not just the summer months are considered here). The maps reveal that maritime moisture advected by westerlies is released particularly at the mountain slopes, thus there is a strong correlation between intense daily precipitation and Hessian topography during westerly air mass inflow (Fig. 4a, right). On the contrary, southern air mass inflow leads to rather randomly distributed convective events that are much less affected by topography – they appear on the map of 15 min peaks (Fig. 4b, left).

Finally, it is noteworthy that trough conditions over Central Europe (TRM, often associated with the “Vb-Wetterlage”) are not very relevant for heavy summer precipitation in the study area (Fig. 3). Instead, there is a much higher probability of extreme daily and sub-daily precipitation sums if the trough's core is found over Western Europe (TRW, Fig. 3).

The high-resolution precipitation data of the German Weather Service can be accessed via the DWD Climate Data Center (https://cdc.dwd.de/portal/, last access: 23 May 2019). The HLNUG data are not freely accessible in one-minute resolution as used in this paper, but can be purchased from the HLNUG on request.

AB was responsible for data evaluation, creation of figures and interpretation of results, while AH scientifically supported those acitivities. Both authors conducted the manuscript in its published form.

The authors declare that they have no conflict of interest.

This article is part of the special issue “18th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018”. It is a result of the EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018, Budapest, Hungary, 3–7 September 2018.

This open-access publication was funded
by Johannes Gutenberg University Mainz.

This paper was edited by Rasmus Benestad and reviewed by Eirik Forland and one anonymous referee.