Geostationary Satellite Imagery

Geostationary satellite imagery provides high-frequency observations of Earth's surface and atmosphere, crucial for weather forecasting and climate monitoring. Current research focuses on leveraging deep learning, particularly convolutional neural networks and transformers, to improve the extraction of information from this imagery, including tasks like precise tropical cyclone center location, high-resolution radar reflectivity estimation, and automated cloud type classification. These advancements enable more accurate and timely predictions of high-impact weather events, improved climate modeling, and enhanced monitoring of environmental phenomena like sea fog, ultimately benefiting both scientific understanding and societal preparedness.