02 Fakultät Bau- und Umweltingenieurwissenschaften

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

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Institute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungAuthor: search.filters.author.Seidel, Jochen

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Now showing 1 - 5 of 5
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    Technical note: Space-time statistical quality control of extreme precipitation observations
    (2022) El Hachem, Abbas; Seidel, Jochen; Imbery, Florian; Junghänel, Thomas; Bárdossy, András
    Information 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.
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    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, Christoph
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    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, Lotte
    The 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.
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    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, Christian
    In 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.
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    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, András
    This 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.