The impacts of climate change are now being witnessed. The impacts are intensifying and predicting accurately the strength and behavior of typhoons has become challenging. A team of researchers have come up with a solution. They have developed a technology that utilizes the real-time satellite data and deep learning capabilities. The forecast of typhoons is claimed to be nearly accurate.
The research was led by Professor Jungho Im from the Department of Civil, Urban, Earth and Environmental Engineering at UNIST. They have come up with a deep learning-based prediction model that combines data from geostationary weather satellites. The prediction results with numerical model outputs. The approach is innovatiing and known as Hybrid-Convolutional Neural Networks (Hybrid-CNN). The forecast accuracy of tropical cyclone (TC) intensity consists of lead times of 24, 48, and 72 hours.
It is a Hybrid-CNN model and represents a significant advancement over traditional methods. It does not rely on lengthy manual analysis of satellite data by forecasters. The model effectively reduces the uncertainties and enables more precise as well as reliable typhoon forecasting.
The researchers employed transfer learning to estimate TC intensity by using data from the Communication, Ocean, and Meteorological Satellite (COMS) and the GEO-KOMPSAT-2A (GK2A). It is an AI-powered system and can visualize as well as quantify the intensity estimation process automatically.
Potential impact of the research cannot be overstated. Forecasters now can get faster and more accurate typhoon predictions. The technology is learned to play a crucial role in disaster preparedness. Professor Im emphasized the significance of it saying that their deep learning-based typhoon prediction framework will enable forecasters to develop quick and effective measures.
