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Performance comparison between deep learning and optical flow-based techniques for nowcast precipitation from radar images

Marino Marrocu, Luca Massidda
Forecasting, Volume 2, Number 2, page 194-210 - june 2020
Download the publication : forecasting-02-00011-v2.pdf [6.9Mo]  
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.

BibTex references

@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          = {http://publications.crs4.it/pubdocs/2020/MM20},
}

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» Marino Marrocu
» Luca Massidda