Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Preprints |
| DOI | 10.5194/egusphere-2025-6349 |
Lizenz  |
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| Titel (primär) | Scalable radar-driven approach with compact gradient-boosting models for gap filling in high-resolution precipitation measurements |
| Autor | Lünenschloß, P.; Claussnitzer, A.; Schartner, T.; Brunner, M.; Houben, T.; Schäfer, D.; Bumberger, J.
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| Quelle | EGUsphere |
| Erscheinungsjahr | 2026 |
| Department | MET |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Abstract | High-frequency precipitation records are essential for hydrological
modeling, weather forecasting, and ecosystem research. Unfortunately,
they usually exhibit data gaps originating from sensor malfunctions,
significantly limiting their usability. We present a framework to
reconstruct missing data in precipitation measurements sampled at 10 min
frequency using radar-based, gauge independent, precipitation estimates
as the only predictor. We fit gradient-boosting models to the
statistical relationships between radar-based precipitation fields and
collocated rain gauges. The obtained models allow for the filling of
data gaps of arbitrary length and additionally provide confidence
interval approximations. We evaluate the method using the rain gauge
network of the German Weather Service (DWD), which roughly covers the
entirety of Germany. The results show robust performance across diverse
climatic and topographic conditions at a high level, with the
coefficient of determination averaging at around 0.7. The framework is
computationally very cheap, relying on a single CPU core only. This
makes scaling easy and integration into operational gap filling of
extensive sensor networks feasible. |
| Lünenschloß, P., Claussnitzer, A., Schartner, T., Brunner, M., Houben, T., Schäfer, D., Bumberger, J. (2026): Scalable radar-driven approach with compact gradient-boosting models for gap filling in high-resolution precipitation measurements EGUsphere 10.5194/egusphere-2025-6349 |
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