Classification and Estimation of Typhoon Intensity from Geostationary Meteorological Satellite Images Based on Deep Learning

Abstract

In this paper, a novel typhoon intensity classification and estimation network (TICAENet) is constructed to recognize typhoon intensity. The TICAENet model is based on the LeNet-5 model, which uses weight sharing to reduce the number of training parameters, and the VGG16 model, which replaces a large convolution kernel with multiple small kernels to improve feature extraction. Satellite cloud images of typhoons over the Northwest Pacific Ocean and the South China Sea from 1995–2020 are taken as samples. The results show that the classification accuracy of this model is 10.57% higher than that of the LeNet-5 model; the classification accuracy of the TICAENet model is 97.12%, with a classification precision of 97.00% for tropical storms, severe tropical storms and super typhoons. The mean absolute error (MAE) and root mean square error (RMSE) of the samples estimation in 2019 are 4.78 m/s and 6.11 m/s, and the estimation accuracy are 18.98% and 20.65% higher than that of the statistical method, respectively. Additionally, the model takes less memory and runs faster due to the weight sharing and multiple small kernels. The results show that the proposed model performs better than other methods. In general, the proposed model can be used to accurately classify typhoon intensity and estimate the maximum wind speed by extracting features from geostationary meteorological satellite images.

Keywords

• deep learning
• typhoon intensity
• satellite cloud images
• deep convolutional neural network