EGU21-11990
https://doi.org/10.5194/egusphere-egu21-11990
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

Anastase Charantonis1,2, Vincent Bouget3, Dominique Béréziat3, Julien Brajard4, and Arthur Filoche3
Anastase Charantonis et al.
  • 1Laboratoire d’océanographie et du climat (LOCEAN); Paris, France
  • 2École nationale supérieure d’informatique pour l’industrie et l’entreprise (ENSIIE); Évry, France
  • 3Sorbonne Université, CNRS, Laboratoire d’Informatique de Paris 6; Paris, France
  • 4Nansen Environmental and Remote Sensing Center (NERSC); Bergen, Norway

Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., Béréziat, D., Brajard, J., Charantonis, A., & Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015

How to cite: Charantonis, A., Bouget, V., Béréziat, D., Brajard, J., and Filoche, A.: Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11990, https://doi.org/10.5194/egusphere-egu21-11990, 2021.