Welcome to the AI for Weather Satellites Website

Artificial intelligence (AI) is rapidly transforming the way satellite observations are calibrated, interpreted, and assimilated into weather prediction systems.
Machine-learning (ML) methods are increasingly integrated alongside traditional physics-based models, enabling smarter use of data from the growing constellation
of weather satellites. These AI-enabled frameworks not only accelerate computationally expensive processes-such as radiative transfer calculations, retrieval inversion, and sensor bias correction-but also enhance the precision and consistency of satellite-derived geophysical products. By learning complex nonlinear relationships directly from observations and high-quality reference datasets, AI models can improve performance in challenging conditions, adapt more flexibly to new instruments, and provide robust uncertainty characterization. As a result, AI is reshaping the end-to-end satellite data pipeline, from sensor calibration and quality control to atmospheric retrievals and data assimilation into operational numerical weather prediction. This emerging paradigm enables more responsive and accurate weather and environmental applications, particularly as mission fleets expand and data volumes continue to grow. A few applications of AI for weather satellites are demonstrated here.