RainGAN: A Conditional Rain Fields Generator

Hai Victor Habi, Hagit Messer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Rain fields' simulation is an important tool for several research fields and applications. However, most simulations are based on a naive model that cannot capture complex spatial distribution. In this work, we present RainGAN, a generative model that enables a generation of a realistic, complex rain field that is conditioned on user parameters such as max peak, number of peaks, etc. In addition, we construct a dataset of typical rain fields that are based on radar measurement and have been utilized in the training process. We conducted several experiments and demonstrate the generator quality using both numerical and visual results.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages529-532
Number of pages4
ISBN (Electronic)9780738146720
DOIs
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021 - Tel Aviv, Israel
Duration: 1 Nov 20213 Nov 2021

Publication series

Name2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021

Conference

Conference2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021
Country/TerritoryIsrael
CityTel Aviv
Period1/11/213/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • CNN
  • GAN
  • Rain simulator

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