42931
2019
2019
eng
1387
1402
16
12
article
Copernicus Publications
Göttingen
1
2019-04-09
2019-04-09
--
Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)
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 displace the precipitation field to the imminent future (minutes to hours) based on that motion, 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 the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). 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.
Geoscientific model development
10.5194/gmd-12-1387-2019
1991-9603
1991-959X
Universität Potsdam
PA 2019_33
1466.08
<a href="http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429333">Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 709</a>
CC-BY - Namensnennung 4.0 International
Georgy Ayzel
Maik Heistermann
Tanja Winterrath
eng
uncontrolled
machine
eng
uncontrolled
system
Geowissenschaften
open_access
Referiert
Publikationsfonds der Universität Potsdam
Open Access
Institut für Umweltwissenschaften und Geographie
42933
2019
2019
eng
16
709
postprint
1
2019-05-22
2019-05-22
--
Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)
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 displace the precipitation field to the imminent future (minutes to hours) based on that motion, 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 the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). 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.
Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
10.25932/publishup-42933
urn:nbn:de:kobv:517-opus4-429333
1866-8372
Geoscientific Model Development 12 (2019), s. 1387–1402 DOI: 10.5194/gmd-12-1387-2019
<a href="http://publishup.uni-potsdam.de/opus4-ubp/frontdoor/index/index/docId/42931">Bibliographieeintrag der Originalveröffentlichung/Quelle</a>
CC-BY - Namensnennung 4.0 International
Georgy Ayzel
Maik Heistermann
Tanja Winterrath
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
709
eng
uncontrolled
machine
eng
uncontrolled
system
Geowissenschaften
open_access
Referiert
Open Access
Institut für Umweltwissenschaften und Geographie
Universität Potsdam
https://publishup.uni-potsdam.de/files/42933/pmnr709.pdf