Performance comparison between deep learning and optical flow-based techniques for nowcast precipitation from radar images
Forecasting, Volume 2, Number 2, page 194-210 - june 2020
Télécharger la publication :
![forecasting-02-00011-v2.pdf [6.9Mo]](https://publications.crs4.it/images/pdf.png)
In this article, a nowcasting technique for meteorological radar images based on a generative neural network is presented. This technique’s performance is compared with state-of-the-art optical flow procedures. Both methods have been validated using a public domain data set of radar images, covering an area of about 104 km2 over Japan, and a period of five years with a sampling frequency of five minutes. The performance of the neural network, trained with three of the five years of data, forecasts with a time horizon of up to one hour, evaluated over one year of the data, proved to be significantly better than those obtained with the techniques currently in use.
Références BibTex
@Article{MM20,
author = {Marrocu, M. and Massidda, L.},
title = {Performance comparison between deep learning and optical flow-based techniques for nowcast precipitation from radar images},
journal = {Forecasting},
number = {2},
volume = {2},
pages = {194-210},
month = {june},
year = {2020},
publisher = {MDPI},
keywords = {nowcast, meteorological radar data, optical flow, deep learning},
doi = {10.3390/forecast2020011},
url = {https://publications.crs4.it/pubdocs/2020/MM20},
}