Microsoft & UCLA Introduce ClimaX: A Foundation Model for Climate and Weather Modelling

Climate change and extreme weather events have made weather and climate modelling a challenging yet crucial real-world task. While current state-of-the-art approaches tend to employ numerical models conditioned on physical information collected from the atmosphere, the development of powerful deep learning models and the increasing availability of massive climate datasets have advanced the possibility of a data-driven, general-purpose foundation model for such modelling. In the new paper ClimaX: A Foundation Model for Weather and Climate, a team from Microsoft Autonomous Systems and Robotics Research, Microsoft Research AI4Science and the University of California at Los Angeles presents ClimaX, a general-purpose deep learning foundation model for weather and climate that can be efficiently adapted for various tasks related to the Earth's atmosphere. The team set out to train a generalizable foundation model capable of handling heterogeneous datasets of different variables and providing spatiotemporal coverage based on physical groundings. They built ClimaX on a vision transformer (ViT) backbone and introduced two main architectural changes -- variable tokenization and variable aggregation -- to improve its flexibility and generality.