Detecting Convective Clouds in Geostationary Satellite

Detecting convective clouds is crucial for weather forecasting and climate studies. In his work, William Clemens, a Machine Learning Scientist at dida, leverages Convolutional Neural Networks (CNNs) to analyze geostationary satellite data for this purpose. CNNs are particularly adept at image recognition tasks, making them suitable for identifying the complex patterns and structures characteristic of convective clouds. Clemens's approach likely involves training the CNNs on large datasets of satellite imagery labeled with the presence of convective clouds, enabling the model to learn the distinguishing features of these clouds.