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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 2, pp. 336-349

Development of a new automatic method for reconstructing the intensity of tropical cyclones from multispectral satellite Earth observations using artificial neural networks

A.N. Yakusheva 1 , D.M. Ermakov 1, 2 
1 Space Research Institute RAS, Moscow, Russia
2 Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow Region, Russia
Accepted: 29.03.2024
DOI: 10.21046/2070-7401-2024-21-2-336-349
The paper presents an automatic method for reconstructing the intensity of tropical cyclones (TCs) from their satellite images, based on a convolutional neural network. To form a sample of initial data, 43688 records of TCs from the databases of the main regional national tropical cyclone monitoring centers (for brevity, “national hurricane centers”, NHC DB) and corresponding satellite images in several spectral ranges (visible, IR, microwave) were used. The collected data covers observations from 1981 to 2022 in all areas of the World Ocean, except the Indian Ocean, involved in the genesis of tropical cyclones. A study was carried out on the implementation of a neural network with the best performance, restoring the intensity of TCs using the collected volume of data. Based on the results of the study, a neural network was designed, implemented and trained, which provided (when comparing the reconstructed TC intensity value with those indicated in the NHC DB) a root mean square error of about 11.3 (11.4) nodes; the coefficient of determination is about 0.80 (0.82) depending on the combination of types of input information. It is noted that the achieved quality indicators exceed those known from the literature or are comparable to them. At the same time, an analysis of works on the development of approaches to automated assessment of TC intensity showed that all of them were carried out using significantly (orders of magnitude) smaller volumes of input information (individual water areas, single years, certain phases of TC development, etc.). Estimates of the root-mean-square error stated in the literature range from 8 to 14 knots, but the minimum level of error is achieved through special selection of “suitable” data.
Keywords: tropical cyclones, intensity retrievals, artificial neural networks, multispectral satellite observations
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