Detail publikace

Radar-Based Volumetric Precipitation Nowcasting: A 3D Convolutional Neural Network with U-Net Architecture

PAVLÍK, P. ROZINAJOVÁ, V. BOU EZZEDDINE, A.

Originální název

Radar-Based Volumetric Precipitation Nowcasting: A 3D Convolutional Neural Network with U-Net Architecture

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In recent years like in many other domains deep learning models have found their place in the domain of precipitation nowcasting. Many of these models are based on the U-Net architecture, which was originally developed for biomedical segmentation, but is also useful for the generation of short-term forecasts and therefore applicable in the weather nowcasting domain. The existing U-Net-based models use sequential radar data mapped into a 2-dimensional Cartesian grid as input and output. We propose to incorporate a third - vertical - dimension to better predict precipitation phenomena such as convective rainfall and present our results here. We compare the nowcasting performance of two comparable U-Net models trained on two-dimensional and three-dimensional radar observation data. We show that using volumetric data results in a small, but significant reduction in prediction error.

Klíčová slova

precipitation nowcasting, radar imaging, U-Net

Autoři

PAVLÍK, P.; ROZINAJOVÁ, V.; BOU EZZEDDINE, A.

Vydáno

25. 7. 2022

Nakladatel

CEUR-WS.org

Místo

Vienna

ISSN

1613-0073

Periodikum

CEUR Workshop Proceedings

Ročník

3207

Číslo

2022

Stát

Spolková republika Německo

Strany od

65

Strany do

72

Strany počet

7

URL

http://ceur-ws.org/Vol-3207/paper10.pdf

BibTex

@inproceedings{BUT179604,
  author="Peter {Pavlík} and Věra {Rozinajová} and Anna {Bou Ezzeddine}",
  title="Radar-Based Volumetric Precipitation Nowcasting: A 3D Convolutional Neural Network with U-Net Architecture",
  booktitle="Proceedings of the Second Workshop on Complex Data Challenges in Earth Observation (CDCEO 2022)",
  year="2022",
  journal="CEUR Workshop Proceedings",
  volume="3207",
  number="2022",
  pages="65--72",
  publisher="CEUR-WS.org",
  address="Vienna",
  issn="1613-0073",
  url="http://ceur-ws.org/Vol-3207/paper10.pdf"
}