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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-166
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 10 Sep 2018

Model description paper | 10 Sep 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

Georgy Ayzel1, Maik Heistermann1, and Tanja Winterrath2 Georgy Ayzel et al.
  • 1Institute for Earth and Environmental Sciences, University of Potsdam, Potsdam, Germany
  • 2Deutscher Wetterdienst, Department of Hydrometeorology, Offenbach, Germany

Abstract. Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images, and then to extrapolate that motion to the imminent future (minutes to hours), assuming that the intensity of the features remains constant (Lagrangian persistence). In that context, optical flow has become one of the most popular tracking techniques. Yet, the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step, and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (rainymotion) for precipitation nowcasting is written in Python programming language, and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library may serve as a tool for providing fast, free and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.

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