Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data
Journal
35th AAAI Conference on Artificial Intelligence, AAAI 2021
Journal Volume
17A
Pages
14721-14728
Date Issued
2021
Author(s)
Abstract
Analyzing big geophysical observational data collected by multiple advanced sensors on various satellite platforms promotes our understanding of the geophysical system. For instance, convolutional neural networks (CNN) have achieved great success in estimating tropical cyclone (TC) intensity based on satellite data with fixed temporal frequency (e.g., 3 h). However, to achieve more timely (under 30 min) and accurate TC intensity estimates, a deep learning model is demanded to handle temporally-heterogeneous satellite observations. Specifically, infrared (IR1) and water vapor (WV) images are available within every 15 minute period, while passive microwave rain rate (PMW) is available about every 3 hours. Meanwhile, the visible (VIS) channel is severely affected by noise and sunlight intensity, making it difficult to be utilized. Therefore, we propose a novel framework that combines generative adversarial network (GAN) with CNN. The model utilizes all data during the training phase including VIS and PMW information and eventually uses only the high-frequent IR1 and WV data for providing intensity estimates during the predicting phase. Experimental results demonstrate that the hybrid GAN-CNN framework achieves comparable precision to the state-of-the-art models, while possessing the capability of increasing the maximum estimation frequency from 3 hours to less than 15 minutes. Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Other Subjects
Convolutional neural networks; Deep learning; Generative adversarial networks; Geophysics; Hurricanes; Satellites; Tropical cyclone; Tropics; Advanced sensors; Convolutional neural network; Geophysical systems; Intensity estimation; Observational data; Real- time; Satellite data; Satellite platforms; Tropical cyclone intensity; Water vapour; Frequency estimation
Type
conference paper