02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
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Item Open Access Detection of the bright band with a vertically pointing k-bandradar(2014) Pfaff, Thomas; Engelbrecht, Alexander; Seidel, JochenQuantitative precipitation estimation based on weather radar data suffers from a variety of errors. During stratiform events, a region of enhanced reflectivity, called the bright band, leads to large positive biases in the precipitation estimates when compared with ground measurements. The identification of the bright band is an important step when trying to correct weather radar data for this effect. In this study we investigate three different methods to identify the bright band from profiles measured by a vertically pointing K-Band Micro Rain Radar (MRR). The first tries to fit a piecewise linear function to the profile. The bright band characteristics are then derived from the fitted function parameters. The second uses only reflectivity information, while the third makes additional use of the falling velocity, which is also measured by the MRR. This last method shows the greatest skill in identifying the bright band height, followed by the function fit and the pure reflectivity methods. A comparison with data from a scanning radar shows that the height estimated in this way corresponds well with the bright band features observed in the radar scan.Item Open Access Technical note: Space-time statistical quality control of extreme precipitation observations(2022) El Hachem, Abbas; Seidel, Jochen; Imbery, Florian; Junghänel, Thomas; Bárdossy, AndrásInformation about precipitation extremes is of vital importance for many hydrological planning and design purposes. However, due to various sources of error, some of the observed extremes may be inaccurate or false. The purpose of this investigation is to present quality control of observed extremes using space–time statistical methods. To cope with the highly skewed rainfall distribution, a Box–Cox transformation with a suitable parameter was used. The value at the location of a potential outlier is estimated using the surrounding stations and the calculated spatial variogram and compared to the suspicious observation. If the difference exceeds the threshold of the test, the value is flagged as a possible outlier. The same procedure is repeated for different temporal aggregations in order to avoid singularities caused by convection. Detected outliers are subsequently compared to the corresponding radar and discharge observations, and finally, implausible extremes are removed. The procedure is demonstrated using observations of sub-daily and daily temporal resolution in Germany.Item Open Access Hydrological modelling in data sparse environment : inverse modelling of a historical flood event(2020) Bárdossy, András; Anwar, Faizan; Seidel, JochenWe dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.Item Open Access Mobile measurement techniques for local and micro-scale studies in urban and topo-climatology(2016) Seidel, Jochen; Ketzler, Gunnar; Bechtel, Benjamin; Thies, Boris; Philipp, Andreas; Böhner, Jürgen; Egli, Sebastian; Eisele, Micha; Herma, Felix; Langkamp, Thomas; Petersen, Erik; Sachsen, Timo; Schlabing, Dirk; Schneider, ChristophItem Open Access Grundlagenbericht Niederschlags-Simulator (NiedSim3)(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2017) Müller, Thomas; Mosthaf, Tobias; Gunzenhauser, Sarah; Seidel, Jochen; Bárdossy, AndrásDas Programmsystem NiedSim3 (Niederschlags-Simulation) ist ein stochastischer Generator, mit dem für einen beliebigen, frei wählbaren Punkt in einer Modellregion Niederschlagszeitreihen erzeugt werden können, deren statistische Eigenschaften denen des natürlichen Niederschlags an diesem Ort entsprechen.Item Open Access Grundlagenbericht Niederschlags-Simulator (NiedSim3)(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2017) Müller, Thomas; Mosthaf, Tobias; Gunzenhauser, Sarah; Seidel, Jochen; Bárdossy, AndrásItem Open Access Technical note: a guide to using three open-source quality control algorithms for rainfall data from personal weather stations(2024) El Hachem, Abbas; Seidel, Jochen; O'Hara, Tess; Villalobos Herrera, Roberto; Overeem, Aart; Uijlenhoet, Remko; Bárdossy, András; de Vos, LotteThe number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.Item Open Access Precipitation characteristics at two locations in the tropical Andes by means of vertically pointing micro-rain radar observations(2019) Seidel, Jochen; Trachte, Katja; Orellana-Alvear, Johanna; Figueroa, Rafael; Célleri, Rolando; Bendix, Jörg; Fernandez, Ciro; Huggel, ChristianIn remote areas with steep topography, such as the Tropical Andes, reliable precipitation data with a high temporal resolution are scarce. Therefore, studies focusing on the diurnal properties of precipitation are hampered. In this paper, we investigated two years of data from Micro-Rain Radars (MRR) in Cuenca, Ecuador, and Huaraz, Peru, from February 2017 to January 2019. This data allowed for a detailed study on the temporal precipitation characteristics, such as event occurrences and durations at these two locations. Our results showed that the majority of precipitation events had durations of less than 3 h. In Huaraz, precipitation has a distinct annual and diurnal cycle where precipitation in the rainy season occurred predominantly in the afternoon. These annual and diurnal cycles were less pronounced at the site in Cuenca, especially due to increased nocturnal precipitation events compared to Huaraz. Furthermore, we used a fuzzy logic classification of fall velocities and rainfall intensities to distinguish different precipitation types. This classification showed that nightly precipitation at both locations was predominantly stratiform, whereas (thermally induced) convection occurred almost exclusively during the daytime hours.Item Open Access The use of personal weather station observations to improve precipitation estimation and interpolation(2021) Bárdossy, András; Seidel, Jochen; El Hachem, AbbasIn this study, the applicability of data from private weather stations (PWS) for precipitation interpolation was investigated. Due to unknown errors and biases in these observations, a two-step filter was developed that uses indicator correlations and event-based spatial precipitation patterns. The procedure was tested and cross validated for the state of Baden-Württemberg (Germany). The biggest improvement is achieved for the shortest time aggregations.Item Open Access Probabilistic downscaling of EURO-CORDEX precipitation data for the assessment of future areal precipitation extremes for hourly to daily durations(2025) El Hachem, Abbas; Seidel, Jochen; Bárdossy, AndrásThis work presents a methodology to inspect the changing statistical properties of precipitation extremes with climate change. Data from regional climate models for the European continent (EURO-CORDEX 11) were used. The use of climate model data first requires an inspection of the data and a correction of the biases of the meteorological model. Corrections to the biases of the point precipitation data and those of the spatial structure were both performed. For this purpose, a quantile–quantile transformation of the point precipitation data and a spatial recorrelation method were used. Once corrected for bias, the data from the regional climate model were downscaled to a finer spatial scale using a stochastic method with equally probable outcomes. This allows for the assessment of the corresponding uncertainties. The downscaled fields were used to derive area–depth–duration–frequency (ADDF) curves and areal reduction factors (ARFs) for selected regions in Germany. The estimated curves were compared to those derived from a reference weather radar dataset. While the corrected and downscaled data show good agreement with the observed reference data over all temporal and spatial scales, the future climate simulations indicate an increase in the estimated areal rainfall depth for future periods. Moreover, the future ARFs for short durations and large spatial scales increase compared to the reference value, while for longer durations the difference is smaller.