A CNN-Based Hybrid Model for Tropical Cyclone Intensity Estimation in Meteorological Industry

Abstract

Accurate estimation of tropical cyclone (TC) intensity is the key to understanding and forecasting the behavior of TC and is crucial for initialization in forecast models and disaster management in the meteorological industry. TC intensity estimation is a challenge because it requires domain knowledge to manually extract TC cloud structure features and form various sets of parameters obtained from satellites. In this paper, a novel hybrid model is proposed based on convolutional neural networks (CNNs) for TC intensity estimation with satellite remote sensing. According to the intensity of TCs, we divide them into three types and use three different models for intensity regression, respectively. The results show that the use of piecewise thinking can improve the model's fitting speed on small samples. A classification network is provided to classify unlabeled TC samples before TC regression, whose results would determine which regression network to estimate these samples. Finally, the estimation values are sent to the backpropagation (BP) neural network to fit the suitable intensity values. Experimental results demonstrate that our model achieves high accuracy and low root-mean-square error (RMSE up to 8.91 kts) by just using inferred images. INDEX TERMS Convolutional neural networks, hybrid model, tropical cyclone intensity estimation, infrared imagery.

A literature review on satellite image time series forecasting: Methods and applications for remote sensing

“…A CNN performs a mathematical operation known as convolution, which uses kernels or filters on images, and produces high-level feature maps. Moreover, Tian et al (2020) described that CNN could detect features and determine the best prediction model, and Yu et al (2020) use a convolutional selfattention mechanism to forecast photovoltaic power generation. Also, Wen et al (2020) study different DL models (VGGNet, ResNet, and DenseNet) for the global horizontal irradiance.…”

“…A CNN-based hybrid model for tropical cyclone intensity estimation in meteorological industry (Tian et al, 2020).…”

A Novel Tropical Cyclone Size Estimation Model Based on a Convolutional Neural Network Using Geostationary Satellite Imagery

“…The CNN used in this study was composed of several layers that continuously extracted abstract features from the input data to perform regression or classification tasks by matching these features with the target of the study [33][34][35]. Each layer consisted of several neurons that computed weighted combinations of input data [35].…”

“…Recently, convolutional neural networks (CNNs), an artificial intelligence deep learning technique [28], have been used in TC studies in conjunction with satellite-based TC image analysis [29][30][31][32][33][34][35][36]. However, despite the importance of TC size, most previous studies utilized CNNs only to estimate the TC intensity and tracks [29][30][31][32][33][34][35][36].…”

“…Recently, convolutional neural networks (CNNs), an artificial intelligence deep learning technique [28], have been used in TC studies in conjunction with satellite-based TC image analysis [29][30][31][32][33][34][35][36]. However, despite the importance of TC size, most previous studies utilized CNNs only to estimate the TC intensity and tracks [29][30][31][32][33][34][35][36]. This study aimed to address this research gap by proposing an objective TC size estimation technique using a CNN model and geostationary satellite-based multispectral images from the western North Pacific (WNP).…”