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.

Keywords

• Convolutional neural networks
• hybrid model
• tropical cyclone intensity estimation
• infrared imagery