IBM said its AI model offers a flexible, scalable way to address a variety of challenges related to short-term weather as well as long-term climate projection

IBM's new AI model for weather and climate applications

by · RTE.ie

IBM has launched a new artificial intelligence (AI) model for a variety of weather and climate use cases.

Developed by IBM, NASA, and Oak Ridge National Laboratory, the new system will be available as an open-source model to the scientific, developer, and business communities.

IBM said the model offers a flexible, scalable way to address a variety of challenges related to short-term weather as well as long-term climate projection.

Potential applications include creating targeted forecasts based on local observations, detecting and predicting severe weather patterns, improving the spatial resolution of global climate simulations, and improving how physical processes are represented in numerical weather and climate models.

In one experiment, the foundation model accurately reconstructed global surface temperatures from a random sample of only 5% original data, suggesting a broader application to problems in data assimilation.

The model was pre-trained on 40 years of Earth observation data from NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2).

IBM said that the model has a unique architecture which allows it to be fine-tuned to global, regional, and local scales making it suitable for a range of weather studies.

The foundation model is available for download from the AI platform Hugging Face.

Dr Juan Bernabe-Moreno, Director of IBM Research Europe and IBM's Accelerated Discovery Lead for Climate and Sustainability, said that up to now many large AI models in this space focus on a fixed dataset and single use case, primarily forecasting.

"We have designed our weather and climate foundation model to go beyond such limitations so that it can be tuned to a variety of inputs and uses," Dr Bernabe-Moreno said.

"For example, the model can run both on the entire earth as well as in a local context. With such flexibility on the technology side, this model is well-suited to help us understand meteorological phenomena such as hurricanes or atmospheric rivers, reason about future potential climate risks by increasing the resolution of climate models, and finally inform our understanding of imminent severe weather events," he added.