02 Fakultät Bau- und Umweltingenieurwissenschaften

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3

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Author: search.filters.author.Seidel, JochenSubject: search.filters.subject.624

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Now showing 1 - 5 of 5
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    Detection of the bright band with a vertically pointing k-bandradar
    (2014) Pfaff, Thomas; Engelbrecht, Alexander; Seidel, Jochen
    Quantitative 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.
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    Hydrological modelling in data sparse environment : inverse modelling of a historical flood event
    (2020) Bárdossy, András; Anwar, Faizan; Seidel, Jochen
    We 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.
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    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, András
    Das 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.
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    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, András
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    The use of personal weather station observations to improve precipitation estimation and interpolation
    (2021) Bárdossy, András; Seidel, Jochen; El Hachem, Abbas
    In 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.