UP1.6

An operational, single-polarized X-band weather radar monitors precipitation within a 20 km scan radius around Hamburg’s city center for almost eight years. This weather radar operates at an elevation angle (~3.5°) with a high temporal (30 s), range (60 m), and sampling (1°) resolution refining observations of the German nationwide C-band radars. Studies on short time periods (several months and case studies) proofs the performance of this low-cost local area weather radar. The synergy of observations of the X-band radar, vertically pointing micro rain radars, and rain gauges yields a reliable eight-year precipitation climatology with 100 m resolution. The two guiding questions of this presentation are: Is the variability of this precipitation climatology representative and not contaminated by measurement errors? Which sub-hourly precipitation characteristics can we infer from this precipitation climatology?

Several sources of radar-based errors were adjusted gradually affecting the precipitation estimate, e.g. the radar calibration, alignment, attenuation, noise, non-meteorologial echoes. Additionally, statistical relations (k-Z and Z-R relation) increase the uncertainty of the precipitation estimate. However, the deployment of additional vertically pointing micro rain radars yields drop size distributions at relevant heights, which increases the data quality effectively and assesses the statistics of the long-term precipitation observations. The resulting climatology allows studies on the spatial and temporal scale of urban precipitation. We outline the performance of the climatology, present first results on sub-hourly precipitation characteristics and discuss open issues and limitations.

This multi-year urban precipitation analysis is groundwork for further hydrological research in an urban area within the project Sustainable Adaption Scenarios for Urban Areas – Water from Four Sides of the Cluster of Excellence Climate Climatic Change, and Society (CliCCS). Future urban precipitation studies will be improved by the extension of networked observations with a second X-band weather radar site and additional micro rain radars in Hamburg measuring since the beginning of 2021.

How to cite: Burgemeister, F., Clemens, M., and Ament, F.: Multi-year 100-metre-scale urban precipitation study based on X-band radar observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-326, https://doi.org/10.5194/ems2021-326, 2021.

Urbanisation modifies the local climate and results in the so-called urban climate. Within the urban boundary layer, the average wind speed is reduced, while gustiness is increased. Buildings induce vertical winds. Heterogeneities in the rain pattern around buildings are the consequence. Human discomfort in street canyons may be one result. In addition, sealed urban surfaces lead to large rainwater run-off, which is a cause for flash floods in urban areas.

Increased computational power allows high-resolution modelling in urban areas with a horizontal resolution well below 10 m. The consideration of more meteorological processes like cloud and rain microphysics is possible. This allows us to estimate the impact of rain events, especially heavy rain events and flash floods, to urban neighbourhoods. Nevertheless, the domain size with these high-resolution models is restricted and the cloud and rain development passes the domain without full development of rain. To overcome this challenge, high-resolution information about rain events in urban areas are necessary.

In the area of Hamburg, Germany, measurements of a X-band weather radar at a 100-metre-scale and a vertically pointing micro rain radar are available for several years. These high-resolution measurement data are used to develop a forcing method for the microscale, obstacle resolving transport and stream model MITRAS (Salim et al. 2019). The forcing method samples 2D and 3D information about the rain rate to the model domain. The nudging approach adds the information about the rain rate to the top and the lateral boundaries of the model domain. Model simulations with different synoptic situations evaluate the forcing methodology.

In this contribution, the forcing method will be presented and results from different test cases in a test area in Hamburg will be shown.

Salim M.H, Schlünzen K.H., Grawe D., Boettcher M., Gierisch A.M.U., Fock B.H. (2018): The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory. Geosci. Model Dev., 11, 3427–3445, https://doi.org/10.5194/gmd-11-3427-2018.

How to cite: Boettcher, M., Burgemeister, F., Ferner, K. S., and Schlünzen, K. H.: Modelling rain heterogeneities in urban areas, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-455, https://doi.org/10.5194/ems2021-455, 2021.

One of the predicted effects of climate change in Central Europe is a growing number and increasing extremity of heavy rainfalls. Thus, it is of a great importance not only to develop best possible nowcasting methods and long-term forecasting models, but also to look closer at the structure and detailed characteristics of extreme events that have already taken place.

With this objective, the German Weather Service (DWD) has developed a Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 20 years of climatological radar data for the area of Germany.

Using hourly data of about 1 km spatial resolution, an object-oriented analysis is performed to classify spatially and timely independent rainfall events exceeding the official warning level for heavy precipitation. Events with duration between 1 and 72 hours are investigated and statistically analysed. Apart from various extremity attributes, like return period, heavy precipitation, and weather extremity indices, the catalogue is enriched with additional variables (e.g. weather type, antecedent precipitation index, population density, land cover, imperviousness degree, Topographic Position Index), providing the meteorological background and helping to estimate the possible impact, each event could provoke.

The Catalogue is freely available via DWD’s Open Data Portal in both a tabular and spatial (GIS) format. In addition, a user friendly online Dashboard was developed to visualize the data and communicate our results to a broader audience. 

We will present the CatRaRE Catalogue and results of a comprehensive analysis of all classified heavy precipitation events that occurred in Germany between 2001 and 2020. Different time scales from diurnal to multi-annual, as well as identified spatial patterns in connection with event attributes will be illustrated. Most common weather types, favouring occurrence of detected events will be outlined. Finally, we will demonstrate selected application possibilities by combining the catalogue with other datasets (e.g. fire brigade operations).

How to cite: Walawender, E., Lengfeld, K., Winterrath, T., Weigl, E., and Becker, A.: Spatio-temporal patterns and extremity assessment of heavy rainfall events in Germany, derived from a radar-based catalogue (CatRaRE, 2001-2020), EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-461, https://doi.org/10.5194/ems2021-461, 2021.

According to the Clausius-Clapeyron equation on saturation vapour pressure a temperature increase of 1 K allows an atmospheric air mass to hold approximately 7 % more water vapour thus increasing its potential for heavy precipitation. Several published measurement studies on the relation between precipitation intensity and temperature, however, revealed an increase of even up to twofold the CC rate for short-term precipitation events. Model conceptions explain this scaling behaviour with increasing temperature by different intensification pathways of convective processes and/or a transition between stratiform and convective precipitation regimes that both can hardly be verified by point measurements alone. In this presentation, we present first results of the correlation between ambient air temperature and different attributes of the Catalogue of Radar-based Heavy Rainfall Events (CatRaRE) recently published by Deutscher Wetterdienst (DWD). This object-oriented event catalogue files and characterizes extreme precipitation events that have occurred on German territory since 2001. It is based on the high-resolution precipitation climate data set RADKLIM of DWD, i.e. contiguous radar-based reflectivity measurements adjusted to hourly station-based precipitation totals and corrected for typical measurement errors applying specific climatological correction methods. Our analysis gives new insights into potential explanations of the observed temperature scaling relating not only precipitation intensity but characteristic event properties like area, duration, and extremity indices with ambient temperature data. With this approach, extreme precipitation events can be analysed in a comprehensive way that is significant in the context of potential impact. The presented analysis moreover allows testing the hypothesis of regime changing based on objective precipitation event criteria that are typical for different precipitation types. We will briefly present the methodological background of CatRaRE with special focus on the event attributes used in the analysis of Clausius-Clapeyron scaling and give first results on the retrieved temperature dependencies of extreme precipitation events.

How to cite: Winterrath, T., Walawender, E., Lengfeld, K., Weigl, E., and Becker, A.: Temperature scaling of extreme precipitation events – an application of the new DWD event catalogue, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-452, https://doi.org/10.5194/ems2021-452, 2021.

Commercial microwave links (CMLs) have emerged as a valuable source of rainfall information that can complement existing observations. In Germany, we acquire attenuation data from 4000 CMLs with a temporal resolution of one minute. In this contribution we present our results of deriving country-wide rainfall information from these CML data and show the first long-term application of CML data for adjusting the radar rainfall field.

We present results of a large-scale analysis of our country-wide dataset for one full year (Graf et al. 2020) and compare it with the gauge adjusted radar product RADOLAN-RW from the German Weather Service and the climatologically corrected radar product RADKLIM-YW. Our analysis also compares several different methods for processing CML data, including our recent improvements for the separation of dry and rainy periods in noisy CML attenuation time series based on a convolutional neural network (Polz et al. 2020). We show seasonal and diurnal variations of the performance of CML-derived rainfall data. Promising results are achieved year-round except for periods with solid precipitation. Pearson correlations for the comparison of the hourly rainfall sums reach up to 0.7 for summer months.

Furthermore, we present results from using the CML rainfall estimates to adjust radar rainfall fields. We extended the RADOLAN-method for radar-gauge adjustment for this purpose. The path-averaged CML rainfall information is compared to the gridded radar rainfall information at the path-intersecting grids. This information is then used in addition to the adjustments derived from rain gauges. We show first results of an hourly adjustment over several months. We further discuss the envisaged operational system for this application and give an outlook on the potential for radar rainfall field adjustments with higher temporal resolutions.

Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020

Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020

How to cite: Chwala, C., Winterrath, T., Graf, M., Polz, J., and Kunstmann, H.: Country-wide CML rainfall estimation and CML-Radar combination in Germany, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-499, https://doi.org/10.5194/ems2021-499, 2021.

Quantifying past changes in extreme precipitation is crucial to understand the climate response and improve our projections. Due to the limited data availability, information about Africa is of particular interest. We combine high-resolution satellite estimates (CHIRPSv2) with an innovative approach for the detection and attribution of trends in extremes (both annual maxima and rarer events, such as the 100-year return levels) to investigate changes in daily precipitation extremes and storm structure occurred over Africa since 1981. Scale-dependence is explored by comparing trends detected at the local (0.05° resolution) and meso- (1°) scales. The statistical model was validated using a gauge-based dataset (GPCC) before application to satellite estimates. 

Roughly ~20% of the continent experienced significant (p=0.05) changes in annual maxima at both scales. Decreasing trends are observed in the central portion of the continent, and increasing trends in the Sahel and some districts in southern and eastern Africa. Storms tended to become spatially smoother, with faster decreases at the local scales (median=13% faster for annual maxima, 14% for 100-year return levels) and faster increases at the mesoscales (17% for annual maxima, 16% for 100-year return levels). The 100-year return levels increased 33% (25% at the mesoscale) faster than annual maxima and decreased 43% (45%) faster.

The model explains 89% (91% at the mesoscale) of the variance in the observed significant trends. Changing proportions between heavy and mild events explain 25% (38%) of this variance, changes in the overall intensities 13% (21%), and changes in the number of wet days 4% (12%). About ~25% of the area experienced significant trends in at least one model parameter, although no significant trend could be detected in the maxima. Censoring annual maxima, the model still explains 77% of the variance in their trends, suggesting it could be effectively used in situations in which observed/modelled extremes are not trusted.

How to cite: Marra, F. and Cattani, E.: Changes in African extreme precipitation and storm structure since 1981 revealed by satellite observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-229, https://doi.org/10.5194/ems2021-229, 2021.

In recent years the interest towards the development of limited-area atmospheric reanalysis datasets has been growing more and more. Regional reanalyses in fact, as a consequence of the restricted domain that they cover, provide a data distribution displaced on a much finer grid compared to a coarser global dataset. This permits to better resolve those patterns related to rapid and high-impact weather events, first and foremost convection. Furthermore, with a finer horizontal resolution, a consistent increase in the level of detail in the description of the orography is also gained, that is a crucial point to achieve especially in a very complex territory such as Italy. This study presents the first application of the novel regional reanalysis dataset developed at ARPAE-SIMC: the High rEsolution ReAnalysis over Italy (SPHERA). SPHERA is a high-resolution convection-permitting reanalysis over the Italian domain and the surrounding seas covering 25 years, from 1995 to 2020, at hourly temporal frequency. SPHERA is based on the non-hydrostatic limited-area model COSMO, and produced by a dynamical downscaling of the global reanalysis ERA5, developed at ECMWF. A nudging data assimilation scheme is applied in order to steer the model outcomes towards the surface and upper-air observations. All the available conventional observations have been used.

The added value of SPHERA in representing severe-weather and convective events is evident from its preliminar validation, which was performed on the multidecadal period against various datasets of surface observations, joined with the comparison against the global reanalysis ERA5. In fact, a clear advantage of SPHERA on its driver ERA5 is found for the detection of events with moderate to intense daily and sub-daily rainfalls, which are characterized by a strong seasonal and geographical component, that is further investigated. We report also the preliminary sensitivity analysis on the dimension of the box used to operate the upscaling for the validation of SPHERA, a process necessary to reduce the errors caused by geographical mismatches between observed and simulated events localizations, which are particularly frequent in case of strongly-localized and rapid processes. Furthermore, in order to give a quantitative evaluation of the performance of the new reanalysis in particular conditions, the results of the simulations for specific case studies involving the occurrence of severe-precipitation events in recent years was performed, focusing on events having different dynamical genesis, but interrelated by the important damages they caused. From this analysis, for which also a comparison with other regional reanalyses is performed, the advantage of SPHERA in representing the most intense rainfall occurrences, in terms of location, intensity and timing, clearly emerges.

How to cite: Giordani, A., Cerenzia, I., Paccagnella, T., and Di Sabatino, S.: The new Italian regional reanalysis SPHERA: benefits of the convection-permitting resolution in detecting severe-weather events, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-134, https://doi.org/10.5194/ems2021-134, 2021.

Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Today, the GPCC holds data from more than 123,000 stations, about three quarters of them having long time series.

The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as national meteorological and hydrological services from all over the world.  In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.

Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.

In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA).

With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version.

All gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.

How to cite: Rustemeier, E., Schneider, U., Ziese, M., Finger, P., and Becker, A.: Updated gridded datasets version 2020 provided by the Global Precipitation Climatology Centre (GPCC), EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-494, https://doi.org/10.5194/ems2021-494, 2021.

The WegenerNet Feldbach Region is a unique weather and climate observation network comprising 155 hydrometeorological stations measuring temperature, humidity, precipitation, and at particular locations wind speed and direction as well as other parameters, in a tightly spaced grid within a core area of 22 km x 16 km centered near the city of Feldbach (46.93°N, 15.90°E), in southeastern Austria.

With about one station every two square-km (area of about 300 square-km in total), and each station with 5-min time sampling, the network provides fully automated regular measurements since January 2007.

In 2020, the station network was expanded by three major new components, expanding it from a 2D ground station hydrometeorological network into a 3D open-air laboratory for climate change research at very high resolution.  These new atmospheric 3D-observation components consist of:

1. A polarimetric X-band Doppler weather radar for studying precipitation parameters in the troposphere above the ground network, such as rain rate, hydrometeor classification, Doppler velocity, and approximate drop size distribution and number: it can provide 3D volume data (at about 1 km x 1 km horizontal and 500 m vertical resolution and 2.5 min time sampling) for moderate to strong precipitation. Together with the dense ground network, this allows detailed studies of heavy precipitation events with high resolution and accuracy.

2. A radiometer pair consisting of two azimuth- and elevation-steerable radiometers: (1) a microwave atmospheric-profiling radiometer with built-in auxiliary infrared radiometer for vertical profiling of temperature, humidity, and cloud liquid water in the troposphere above the WegenerNet area (with about 100 m to 1 km vertical resolution and 5 to 10 min time sampling), also capable of measuring cloud-base heights, vertically integrated water vapor (IWV), and slant IWV along line-of-sight paths towards Global Navigation Satellite System (GNSS) satellites, and (2) a complementary infrared cloud structure radiometer at similar spatiotemporal sampling for further refining gridded cloud-base height calculations and enabling multi-layer cloud-field reconstruction over the WegenerNet area, providing 3D cloud-field (multi-layered cloud fraction) estimates.

3. A water-vapor-mapping high-resolution GNSS station network named GNSS-StarNet, comprising six ground stations and spatially forming two star-shaped subnets across the WegenerNet area (one with about 10 km interstation distance and one embedded with about 5 km interstation distance), for providing slant IWV, vertical IWV, and precipitable water, among other parameters, at 2.5 to 15 min time sampling.

The new components, together with the existing ground network, provide a unique setup for studying extreme meteorological events such as heavy precipitation, hailstorms, droughts, and heat waves at very high resolution. We will present the up-to-date status of the WegenerNet and highlight recent uses in precipitation, hydrology and climate-related studies.

How to cite: Fuchsberger, J., Kirchengast, G., Foelsche, U., Bichler, C., and Galovic, R.: The WegenerNet 3D Open-Air Laboratory for Climate Change Research: A unique facility for high-resolution precipitation studies, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-58, https://doi.org/10.5194/ems2021-58, 2021.

Weather radars measure rainfall in altitude whereas hydro-meteorologists are mainly interested in rainfall at ground level. During their fall, drops are advected by the wind which affects the location of the measured field. In this study, we investigate the fall of rain drops in a turbulent wind field between an height of 1500m and the ground.

The equation governing a rain drop motion relates the acceleration to the forces of gravity and buoyancy along with the drag force. The latter depends non-linearly on the instantaneous relative velocity between the drop and the local wind; which yields to complex behaviour. In this work, the drag force is expressed in a standard way with the help of a drag coefficient, which is itself determined according to a Reynolds number. Corrections accounting for the oblateness of drops greater than 1-2 mm are implemented. Such corrections are validated through comparison of retrieved “terminal fall velocity” (i.e. without wind) with commonly used relationships in the literature.

An explicit numerical scheme is implemented to solve this equation for 3+1D turbulent wind field, and hence analyse the temporal evolution of the velocities and trajectories of rain drops during their fall. Two types of wind inputs are used : (i) Four months of 100 Hz 3D sonic anemometers data. (ii) Numerical simulations of space-time varying wind carried out with the help of Universal Multifractals which are a framework that has been widely used to characterize and simulate geophysical fields extremely variable over a wide range of scales such as wind.

The behaviour of drop velocities is then characterized through temporal multifractal analysis. It notably enables to highlight a scale, depending on the drop size, below which turbulent eddies have a limited impact on their motion. Finally the dispersion on the ground of drops all starting at the same location is quantified and consequences on rainfall remote sensing with radars discussed.

Authors acknowledge the RW-Turb project (supported by the French National Research Agency - ANR-19-CE05-0022), for partial financial support.

How to cite: Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Where are rainfall drops falling in a turbulent wind field ?, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-430, https://doi.org/10.5194/ems2021-430, 2021.