Searching similar weather maps using convolutional autoencoder and satellite images

Heewoong Ahn, Sunhwa Lee, Hanseok Ko, Meejoung Kim, Sung Won Han, Junhee Seok

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A weather forecaster predicts the weather by analyzing current weather map images generated by a satellite. In this analyzing process, the accuracy of the prediction depends highly on the forecaster's experience which is needed to recollect similar weather maps from the past. In an attempt to help forecasters to obtain empirical data and analyze the current weather status, this paper proposes a convolutional autoencoder model to find weather maps from the past that are similar to a current weather map by extracting the latent features of each image. To measure the similarity between each pair of images, metrics including mean squared error and structural similarity were used and case studies for searching similar satellite images were conducted and visualized. The paper also demonstrates that searching similar weather maps can be useful guidance to all forecasters when analyzing and predicting the weather.

Original languageEnglish
Pages (from-to)69-75
Number of pages7
JournalICT Express
Volume9
Issue number1
DOIs
Publication statusPublished - 2023 Feb

Bibliographical note

Funding Information:
This research was supported by a grant from the National Research Foundation of Korea ( NRF-2022R1A2C2004003 ) as well as a grant from Korea Meteorological Administration ( Q171471 ). We thank KMA for assistance with this research and for providing ECMWF dataset.

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • Convolutional autoencoder
  • Deep learning
  • Unsupervised learning
  • Weather map retrieval

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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