Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting - Sorbonne Université Access content directly
Journal Articles Remote Sensing Year : 2021

Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting

Abstract

Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk 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.
Fichier principal
Vignette du fichier
2012.05015.pdf (1.06 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03112093 , version 1 (15-01-2021)

Identifiers

Cite

Vincent Bouget, Dominique Béréziat, Julien Brajard, Anastase Alexandre Charantonis, Arthur Filoche. Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing, 2021, 13 (2), pp.246. ⟨10.3390/rs13020246⟩. ⟨hal-03112093⟩
241 View
163 Download

Altmetric

Share

Gmail Facebook X LinkedIn More