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An important aspect of rainfall estimation is to accurately capture extreme events. Commercial microwave links (CMLs) can complement weather radar and rain gauge data by estimating path‐averaged rainfall intensities near ground. Our aim with this paper was to investigate attenuation induced complete loss of signal (blackout) in the CML data. This effect can occur during heavy rain events and leads to missing extreme values. We analyzed 3 years of attenuation data from 4,000 CMLs in Germany and compared it to a weather radar derived attenuation climatology covering 20 years. We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for longer CMLs (e.g., >10 km) despite their increased dynamic range. Therefore, both the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Plain Language Summary: Commercial microwave links (CMLs) are used to transmit information between towers of cellphone networks. If there is rainfall along the transmission path, the signal level is attenuated. By comparing the transmitted and received signal levels, the average rainfall intensity along the path can be estimated. If the attenuation is too strong, no signal is received, no information can be transmitted and no rainfall estimate is available. This is unfavorable both for network stability and rainfall estimation. In this study, we investigated the frequency of such blackouts in Germany. How many blackouts per year are observed in a 3 year CML data set covering around 4,000 link paths and how many are expected from 20 years of weather radar data? We observed that the average CML experiences 8.5 times more blackouts than we would have expected from the radar derived climatology. Blackouts did occur more often for long CMLs, which was an unexpected finding. While only one percent of the annual rainfall amount is missed during blackouts, the probability that a blackout occurs was very high for high rain rates. Both, the hydrometeorological community and network providers can consider our analysis to develop mitigation measures. Key Points: Complete loss of commercial microwave link (CML) signals during heavy rain leads to missing rainfall extremesMagnitude of observed blackouts exceeds climatologically expected valuesUnexpectedly, longer CMLs experience more blackouts [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution. [ABSTRACT FROM AUTHOR]

This work provides an analysis of the Weisseritz 2002 flood event in Eastern Germany, which was characterized by intense upslope storm movement and associated orographic enhancement of precipitation. Examination of the event is based on the availability of weather radar observations and post-event surveys to document the peak runoff rates. The analysis is based on the use of a set of statistics that clarify the dependence existing between spatial rainfall distribution, basin morphology, and runoff response. The statistics provide a description of overall spatial rainfall organization at the catchment scale in terms of concentration and dispersion statistics and storm motion, as a function of the flow distance coordinates, i.e., distance to the outlet measured along the flow path. The statistics are based on the observation that runoff routing through branched channel networks imposes an effective averaging of spatial rainfall at equal flow distance, despite the inherent spatial variability. A spatially distributed rainfall-runoff model is used to evaluate the effects of the rainfall spatial variability and storm velocity on flood modeling at various river sections. It is shown that storm velocity exhibits rather moderate values, despite the strong kinematic characteristics of individual storm elements. Consistently with this observation, hydrologic simulations show that orographically controlled rainfall spatial variability and storm motion have an almost negligible effect on flood response modeling. The drainage network structure filters out the effects of spatial rainfall variability for storm event hydrologic response, resulting in Weisseritz exhibiting striking uniformity of response to a storm with strongly varying precipitation. Overall, this shows the value of using the rainfall statistics introduced in this paper to isolate and describe the features of rainfall space-time variability that have significant impacts on runoff simulation. [ABSTRACT FROM AUTHOR]

The demand of accurate, near-real-time radar-based quantitative precipitation estimation (QPE), which is key to feed hydrological models and enable reliable flash flood predictions, was highlighted again by the disastrous floods following after an intense stratiform precipitation field passing western Germany on 14 July 2021. Three state-of-the-art rainfall algorithms based on reflectivity Z, specific differential phase KDP, and specific attenuation A were applied to observations of four polarimetric C-band radars operated by the German Meteorological Service [DWD (Deutscher Wetterdienst)]. Due to the large vertical gradients of precipitation below the melting layer suggesting warm-rain processes, all QPE products significantly underestimate surface precipitation. We propose two mitigation approaches: (i) vertical profile (VP) corrections for Z and KDP and (ii) gap filling using observations of a local X-band radar, JuXPol. We also derive rainfall retrievals from vertically pointing Micro Rain Radar (MRR) profiles, which indirectly take precipitation gradients in the lower few hundreds of meters into account. When evaluated with DWD rain gauge measurements, those retrievals result in pronounced improvements, especially for the A-based retrieval partly due to its lower sensitivity to the variability of raindrop size distributions. The VP correction further improves QPE by reducing the normalized root-mean-square error by 23% and the normalized mean bias by 20%. With the use of gap-filling JuXPol data, the A-based retrieval gives the lowest errors followed by the Z-based retrievals in combination with VP corrections. The presented algorithms demonstrate the increased value of radar-based QPE application for warm-rain events and related potential flash flooding warnings. [ABSTRACT FROM AUTHOR]

This paper evaluates convective precipitation as simulated by the convection-permitting climate model (CPM) Consortium for Small-Scale Modeling in climate mode (COSMO-CLM) (with 2.8 km grid-spacing) over Germany in the period 2001–2015. Characteristics of simulated convective precipitation objects like lifetime, area, mean intensity, and total precipitation are compared to characteristics observed by weather radar. For this purpose, a tracking algorithm was applied to simulated and observed precipitation with 5-min temporal resolution. The total amount of convective precipitation is well simulated, with a small overestimation of 2%. However, the simulation underestimates convective activity, represented by the number of convective objects, by 33%. This underestimation is especially pronounced in the lowlands of Northern Germany, whereas the simulation matches observations well in the mountainous areas of Southern Germany. The underestimation of activity is compensated by an overestimation of the simulated lifetime of convective objects. The observed mean intensity, maximum intensity, and area of precipitation objects increase with their lifetime showing the spectrum of convective storms ranging from short-living single-cell storms to long-living organized convection like supercells or squall lines. The CPM is capable of reproducing the lifetime dependence of these characteristics but shows a weaker increase in mean intensity with lifetime resulting in an especially pronounced underestimation (up to 25%) of mean precipitation intensity of long-living, extreme events. This limitation of the CPM is not identifiable by classical evaluation techniques using rain gauges. The simulation can reproduce the general increase of the highest percentiles of cell area, total precipitation, and mean intensity with temperature but fails to reproduce the increase of lifetime. The scaling rates of mean intensity and total precipitation resemble observed rates only in parts of the temperature range. The results suggest that the evaluation of coarse-grained (e.g., hourly) precipitation fields is insufficient for revealing challenges in convection-permitting simulations. [ABSTRACT FROM AUTHOR]

This study presents δ26Mg values, Mg2+ concentrations, δ18O and δ2H values of monthly collected rain and snow samples between 2014 and 2017 from north‐west Germany. The δ26Mg values of snow are similar to those of rain. Unlike δ18O and δ2H, neither δ26Mgprecipitation values nor Mg2+ concentrations display a relationship with air temperature. In contrast, δ26Mgprecipitation values and Mg2+ concentrations are correlated with precipitation amount. As previously suggested by other studies but now based on a larger data set, it is proposed that the δ26Mg values and Mg2+ concentrations are affected by the mineralogical composition and amount of airborne dust. Consequently, a small number of random precipitation samples cannot represent the complex processes influencing the δ26Mgprecipitation values. δ26Mgprecipitation can be used to differentiate between seawater aerosols, carbonate and silicate dust. Silicate dust is typified by higher Mg concentrations and δ26Mg values compared to carbonate dust. Concerning carbonates, dolostone dust has higher Mg concentrations and δ26Mg values, while limestone dust is Mg‐lean with lower δ26Mg values. Dust of anthropogenic origin in the region can come from active limestone and dolostone quarries, surface mining of siliciclastics, and agriculture on silicate‐rich soils. Although dust of this mineralogy can also be of natural origin, most natural dust consists of airborne silicates transported by wind systems from the Sahara to Europe. Higher δ26Mgprecipitation values would indicate silicate or dolostone dust influx. Lower δ26Mgprecipitation values suggest lower dust concentrations and a predominantly calcitic dust mineralogy. It appears that δ26Mgprecipitation is most probably influenced by changes in weather conditions and natural and anthropogenic dust origin. This study implies that δ26Mgprecipitation is a potential proxy for dust mineralogy and fluxes. Nevertheless, further research should include the determination of atmospheric dust mineralogy and their fractions as well as air mass back trajectory analysis to identify the source(s) of dust. [ABSTRACT FROM AUTHOR]

Recent advances demonstrate the benefits of radar-derived specific attenuation at horizontal polarization (AH) for quantitative precipitation estimation (QPE) at S and X band. To date the methodology has, however, not been adapted for the widespread European C-band radars such as those installed in the network of the German Meteorological Service (DWD, Deutscher Wetterdienst). Simulations based on a large dataset of drop size distributions (DSDs) measured over Germany are performed to investigate the DSD dependencies of the attenuation parameter αH for the AH estimates. The normalized raindrop concentration (Nw) and the change of differential reflectivity (ZDR) with reflectivity at horizontal polarization (ZH) are used to categorize radar observations into regimes for which scan-wise optimized αH values are derived. For heavier continental rain with ZH > 40 dBZ, the AH-based rainfall retrieval R(AH) is combined with a rainfall estimator using a substitute of specific differential phase ( K DP * ). We also assess the performance of retrievals based on specific attenuation at vertical polarization (AV). Finally, the regime-adapted hybrid QPE algorithms are applied to four convective cases and one stratiform case from 2017 to 2019, and compared to DWD's operational Radar-Online-Aneichung (RADOLAN) RW rainfall product, which is based on Zh only but adjusted to rain gauge measurements. For the convective cases, our hybrid retrievals outperform the traditional R(Zh) and pure R(AH/V) retrievals with fixed αH/V values when evaluated with gauge measurements and outperform RW when evaluated by disdrometer measurements. Potential improvements using ray-wise αH/V and segment-wise applications of the ZPHI method along the radials are discussed. [ABSTRACT FROM AUTHOR]

Extreme convective precipitation is expected to increase with global warming. However, the rate of increase and the understanding of contributing processes remain highly uncertain. We investigated characteristics of convective rain cells like area, intensity, and lifetime as simulated by a convection‐permitting climate model in the area of Germany under historical (1976–2005) and future (end‐of‐century, RCP8.5 scenario) conditions. To this end, a tracking algorithm was applied to 5‐min precipitation output. While the number of convective cells is virtually similar under historical and future conditions, there are more intense and larger cells in the future. This yields an increase in hourly precipitation extremes, although mean precipitation decreases. The relative change in the frequency distributions of area, intensity, and precipitation sum per cell is highest for the most extreme percentiles, suggesting that extreme events intensify the most. Furthermore, we investigated the temperature and moisture scaling of cell characteristics. The temperature scaling drops off at high temperatures, with a shift in drop‐off towards higher temperatures in the future, allowing for higher peak values. In contrast, dew point temperature scaling shows consistent rates across the whole dew point range. Cell characteristics scale at varying rates, either below (mean intensity), at about (maximum intensity and area), or above (precipitation sum) the Clausius–Clapeyron rate. Thus, the widely investigated extreme precipitation scaling at fixed locations is a complex product of the scaling of different cell characteristics. The dew point scaling rates and absolute values of the scaling curves in historical and future conditions are closest for the highest percentiles. Therefore, near‐surface humidity provides a good predictor for the upper limit of for example, maximum intensity and total precipitation of individual convective cells. However, the frequency distribution of the number of cells depending on dew point temperature changes in the future, preventing statistical inference of extreme precipitation from near‐surface humidity. [ABSTRACT FROM AUTHOR]

The interaction effects of different fertilisation regimes and weather variability on crop yield is a challenge that requires long-term investigation. Therefore, yield data for spring barley (SB) in an agricultural long-term field experiment, established in 1963 in Müncheberg, northeast Germany, were analysed to reveal the effects of 21 fertiliser regimes and different weather conditions on SB yields. SB yields were significantly affected by fertilisation regimes (11%), annual weather conditions (55%) and their interaction effect (8%). Mineral N fertilization decreased overall yield variability across seasons as compared to no fertilization and organic fertilization regimes showed higher yield variability. A suitable combined application of mineral nitrogen and organic fertiliser was found to be an effective way to produce higher SB yields than the application of either mineral nitrogen or organic fertiliser alone. A Bayesian linear regression model showed total precipitation during the growing season (April–July) positively affected on SB yields when high mineral N was supplied. At the early growth stage, a precipitation rate (March) and temperature (April or sowing day) negatively affected on SB yield. [ABSTRACT FROM AUTHOR]

Climate signal maps can be used to identify regions where robust climate changes can be derived from an ensemble of climate change simulations. Here, robustness is defined as a combination of model agreement and the significance of the individual model projections. Climate signal maps do not show all information available from the model ensemble, but give a condensed view in order to be useful for non-climate scientists who have to assess climate change impact during the course of their work. Three different ensembles of regional climate projections have been analyzed regarding changes of seasonal mean and extreme precipitation (defined as the number of days exceeding the 95th percentile threshold of daily precipitation) for Germany, using climate signal maps. Although the models used and the scenario assumptions differ for the three ensembles (representative concentration pathway (RCP) 4.5 vs. RCP8.5 vs. A1B), some similarities in the projections of future seasonal and extreme precipitation can be seen. For the winter season, both mean and extreme precipitation are projected to increase. The strength, robustness and regional pattern of this increase, however, depends on the ensemble. For summer, a robust decrease of mean precipitation can be detected only for small regions in southwestern Germany and only from two of the three ensembles, whereas none of them projects a robust increase of summer extreme precipitation. [ABSTRACT FROM AUTHOR]

Temporal and spatial variations of stable oxygen (18O) and hydrogen (2H) isotope measurements in precipitation act as important proxies for changing hydro-meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of δ18O in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann-Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend-free pre-whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We examined 3 years of measured daily values of all major water budget components (precipitation P, potential evapotranspiration PET, actual evapotranspiration ET, and runoff R) and volumetric soil water content θ of a small, forested catchment located in the west of Germany. The spatial distribution of θ was determined from a wireless sensor network of 109 points with 3 measurement depths each; ET was calculated from eddy-covariance tower measurements. The water budget was dominantly energy limited, with ET amounting to approximately 90% of PET, and a runoff ratio R/P of 56%. P, ET, and R closed the long-term water budget with a residual of 2% of precipitation. On the daily time scale, the residual of the water budget was larger than on the annual time scale, and explained to a moderate extent by θ ( R2 = 0.40). Wavelet analysis revealed subweekly time scales, presumably dominated by unaccounted fast-turnover storage terms such as interception, as a major source of uncertainty in water balance closure. At weekly resolution, soil water content explained more than half ( R2 = 0.62) of the residual. By means of combined empirical orthogonal function and cluster analysis, two slightly different spatial patterns of θ could be identified that were associated with mean θ values below and above 0.35 cm3/cm3, respectively. The timing of these patterns as well as the varying coherence between PET, ET, and soil water content responded to changes in water availability, including a moderate response to the European drought in spring 2011. [ABSTRACT FROM AUTHOR]

Reproducing the tree cover changes throughout the Holocene is a challenge for land surface–atmosphere models. Here, results of a transient Holocene simulation of the coupled climate–carbon cycle model, CLIMBER2-LPJ, driven by changes in orbital forcing, are compared with pollen data and pollen-based reconstructions for several regions of Eurasia in terms of changes in tree fraction. The decline in tree fraction in the high latitudes suggested by data and model simulations is driven by a decrease in summer temperature over the Holocene. The cooler and drier trend at the eastern side of the Eurasian continent, in Mongolia and China, also led to a decrease in tree cover in both model and data. In contrast, the Holocene trend towards a cooler climate in the continental interior (Kazakhstan) is accompanied by an increase in woody cover. There a relatively small reduction in precipitation was likely compensated by lower evapotranspiration in comparison to the monsoon-affected regions. In general the model-data comparison demonstrates that climate-driven changes during the Holocene result in a non-homogeneous pattern of tree cover change across the Eurasian continent. For the Eifel region in Germany, the model suggests a relatively moist and cool climate and dense tree cover. The Holzmaar pollen record agrees with the model for the intervals 8–3 ka and 1.7–1.3 ka BP, but suggests great reduction of the tree cover 3–2 ka and after 1.3 ka BP, when highly developed settlements and agriculture spread in the region. [ABSTRACT FROM AUTHOR]

Simulations of orographic precipitation over the low mountain ranges of southwestern Germany and eastern France with two different physics-based linear precipitation models are presented. Both models are based on 3D airflow dynamics from linear theory and consider advection of condensed water and leeside drying. Sensitivity studies for idealized conditions and a real case study show that the amount and spatial distribution of orographic precipitation is strongly controlled by characteristic time scales for cloud and hydrometeor advection and background precipitation due to large-scale lifting. These parameters are estimated by adjusting the model results on a 2.5-km grid to observed precipitation patterns for a sample of 40 representative orography-dominated stratiform events (24 h) during a calibration period (1971--80). In general, the best results in terms of lowest rmse and bias are obtained for characteristic time scales of 1600 s and background precipitation of 0.4 mm h−−1. Model simulations of a sample of 84 events during an application period (1981--2000) with fixed parameters demonstrate that both models are able to reproduce quantitatively precipitation patterns obtained from observations and reanalyses from a numerical model [[Consortium for Small-scale Modeling (COSMO)]]. Combining model results with observation data shows that heavy precipitations over mountains are restricted to situations with strong atmospheric forcings in terms of synoptic-scale lifting, horizontal wind speed, and moisture content. [ABSTRACT FROM AUTHOR]

To determine return values at various return periods for extreme daily precipitation events over complex orography, an appropriate threshold value and distribution function are required. The return values are calculated using the peak-over-threshold approach in which only a reduced sample of precipitation events exceeding a predefined threshold is analyzed. To fit the distribution function to the sample, the L-moment method is used. It is found that the deviation between the fitted return values and the plotting positions of the ranked precipitation events is smaller for the kappa distribution than for the generalized Pareto distribution. As a second focus, the ability of regional climate models to realistically simulate extreme daily precipitation events is assessed. For this purpose the return values are derived using precipitation events exceeding the 90th percentile of the precipitation time series and a fit of a kappa distribution. The results of climate simulations with two different regional climate models are analyzed for the 30-yr period 1971–2000: the so-called consortium runs performed with the climate version of the Lokal Modell (referred to as the CLM-CR) at 18-km resolution and the Regional Model (REMO)–Umweltbundesamt (UBA) simulations at 10-km resolution. It was found that generally the return values are overestimated by both models. Averaged across the region the overestimation is higher for REMO–UBA compared to CLM-CR. [ABSTRACT FROM AUTHOR]

The isotopic composition of air-borne sulphur was investigated in Saxony, Southeast Germany - a region with formerly very high atmospheric SO2 concentrations. In addition, data from various authors were compiled for different Saxonian locations, spanning from 1992 to 2004, i.e., a time of decreasing SO2 concentrations in the atmosphere. There were no obvious temporal changes in the mean δ34S value of bulk precipitation. However, the variability of monthly mean δ34S values decreased. The mean sulphur isotope composition of sulphate from bulk precipitation after the year 2000 converges in Saxony towards 4-5‰, with similar values for different locations. Mean values of different forms of sulphur show the following enrichment order: δ34S of SO2 < δ34S of weathering crusts ≤δ34S of sulphate from bulk precipitation ≤δ34S of dust. Judging from local differences on sulphate crusts and corresponding isotope values of sources, the δ34S value of SO2 as well as for crusts mainly reflects local point sources. The mean δ34S value of bulk precipitation represents more regionally well-mixed SO2 sources and is therefore an ideal tool for monitoring regional atmospheric change. [ABSTRACT FROM AUTHOR]

On the basis of stable oxygen isotopes (δ18O), the summer sea-surface salinity of the German Bight, southeastern North Sea, was determined for the past 800 years. In this near-coastal area, salinity is mainly dependent on the freshwater input of the Elbe River discharging its large catchment, which covers an area of 149000 km2 of central Europe. Therefore, a proxy for Elbe River discharge was reconstructed at the same time, and consequently the δ18O record is also mirroring variations in precipitation within the entire drainage basin. Significant variations in these palaeoenvironmental variables are linked to climatic changes. [ABSTRACT FROM AUTHOR]

The precipitation diurnal cycle (PDC) varies with the season and location. Its link to large-scale weather circulations has been studied in different regions. However, comparable information is lacking for Central Europe. Two decades of hourly precipitation data were combined with records of objective weather patterns over Germany, focusing on the general atmospheric wind directions (WD). The PDC is characterized by the frequency and the average amount of hourly precipitation. The precipitation frequency generally has two peaks: one in the morning and the other in the afternoon. The morning peak of the precipitation amount is small compared to that of the afternoon peak. Remarkably, WD has a prominent influence on the PDC. Days with southwesterly WD have a high afternoon peak and a lower morning peak, while days with northwesterly WD have a high morning peak and a lower afternoon peak. Furthermore, the seasonal variations of PDC are dominated by the seasonal frequency of WD classes. This study presents a general overview of the PDC in Germany with regard to its variation with seasonality, geographical location, elevation, and WD. [ABSTRACT FROM AUTHOR]