Searching similar weather maps using convolutional autoencoder and satellite images

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.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

A convolutional autoencoder model is proposed to help weather forecasters analyze current weather status by finding similar weather maps from the past based on latent features extraction. Similarity between images is measured using metrics like mean squared error and structural similarity, and case studies are conducted to visualize the results. The paper demonstrates the usefulness of searching similar weather maps for all forecasters in analyzing and predicting the weather.