@inproceedings{ca6628e99ec040b199abfd3da80f8386,
title = "RainGAN: A Conditional Rain Fields Generator",
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.",
keywords = "CNN, GAN, Rain simulator",
author = "Habi, {Hai Victor} and Hagit Messer",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; null ; Conference date: 01-11-2021 Through 03-11-2021",
year = "2021",
doi = "10.1109/COMCAS52219.2021.9629000",
language = "אנגלית",
series = "2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "529--532",
booktitle = "2021 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems, COMCAS 2021",
address = "ארצות הברית",
}