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Remote Sensing
Volume 12
Issue 1
10.3390/rs12010108
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Article

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

by
Juhyun Lee
1,
Jungho Im
1,*,
Dong-Hyun Cha
1,
Haemi Park
2 and
Seongmun Sim
1
1
School of Urban & Environmental Engineering in Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
2
Institute of Industrial Science in the University of Tokyo, A building, 4 Chome-6-1 Komaba, Meguro City, Tokyo 153-8505, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 108; https://doi.org/10.3390/rs12010108
Received: 25 November 2019 / Revised: 23 December 2019 / Accepted: 25 December 2019 / Published: 28 December 2019
(This article belongs to the Special Issue Tropical Cyclones Remote Sensing and Data Assimilation)
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Abstract

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs. View Full-Text
Keywords: tropical cyclones; multispectral imaging; 2D/3D convolutional neural networks tropical cyclones; multispectral imaging; 2D/3D convolutional neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style

Lee, J.; Im, J.; Cha, D.-H.; Park, H.; Sim, S. Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data. Remote Sens. 2020, 12, 108. https://doi.org/10.3390/rs12010108

AMA Style

Lee J, Im J, Cha D-H, Park H, Sim S. Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data. Remote Sensing. 2020; 12(1):108. https://doi.org/10.3390/rs12010108

Chicago/Turabian Style

Lee, Juhyun, Jungho Im, Dong-Hyun Cha, Haemi Park, and Seongmun Sim. 2020. "Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data" Remote Sensing 12, no. 1: 108. https://doi.org/10.3390/rs12010108

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