Articles | Volume 18, issue 5
https://doi.org/10.5194/gmd-18-1357-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-1357-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modelling rainfall with a Bartlett–Lewis process: pyBL (v1.0.0), a Python software package and an application with short records
Chi-Ling Wei
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Pei-Chun Chen
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Chien-Yu Tseng
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Ting-Yu Dai
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78705, USA
Yun-Ting Ho
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Ching-Chun Chou
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Christian Onof
Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan
Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We developed a framework to improve short-term rainfall forecasts by combining radar data with rain gauge observations. This approach reduces errors and uncertainty, giving more reliable predictions of when and where rain will fall. Such improvements are valuable for flood warnings, stormwater management, and other decisions that depend on timely and accurate rainfall information.
Chien-Yu Tseng, Li-Pen Wang, and Christian Onof
Hydrol. Earth Syst. Sci., 29, 1–25, https://doi.org/10.5194/hess-29-1-2025, https://doi.org/10.5194/hess-29-1-2025, 2025
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This study presents a new algorithm to model convective storms. We used advanced tracking methods to analyse 165 storm events in Birmingham (UK) and reconstruct storm cell life cycles. We found that cell properties like intensity and size are interrelated and vary over time. The new algorithm, based on vine copulas, accurately simulates these properties and their evolution. It also integrates an exponential shape function for realistic rainfall patterns, enhancing its hydrological applicability.
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-27, https://doi.org/10.5194/hess-2023-27, 2023
Preprint withdrawn
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Components of the hydrological cycle exhibit a “memory” in their behaviour which quantifies how long a variable would stay at high/low values. Being able to model and understand what affects it is vital for an accurate representation of the hydrological elements. In the current work, it is found that rainfall affects the fractal behaviour of groundwater levels, which implies that changes to rainfall due to climate change will change the periods of flood and drought in groundwater-fed catchments.
Y. K. Chen, Y. T. Lin, H. Y. Yen, N. H. Chang, H. M. Lin, K. H. Yang, C. S. Chen, L. P. Wang, H. K. Cheng, H. H. Wu, and J. Y. Han
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Short summary
pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett–Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
pyBL is an open-source package for generating realistic rainfall time series based on the...