IBM and NASA Unveil Versatile AI Model for Weather and Climate Forecasting

The future of AI-powered weather forecasting is looking promising. Some deep-learning models trained on historical weather data can already match the performance of conventional weather models that simulate physical processes on massive supercomputers that traditionally few people have access to.

In collaboration with NASA and with contributions from Oak Ridge National Laboratory, IBM has developed Prithvi WxC, a customizable, open-source foundation model for weather and climate-related applications. According to IBM Research, the model is designed to run on a desktop computer and is now available on Hugging Face.

It took several weeks and dozens of GPUs to train the model on 40 years of historical weather data from NASA’s MERRA-2 harmonized dataset of satellite and other Earth observation data. The model can now be quickly tuned for different use cases and served from a desktop computer in seconds. Potential applications include creating targeted forecasts from local weather data, predicting extreme weather events, improving the spatial resolution of global climate simulations, and enhancing the representation of physical processes in conventional weather and climate models.

“We designed our foundation model so that all the hard work and GPU hours invested upfront would pay off by allowing people to quickly spin off and run new applications,” said Campbell Watson, an IBM climate researcher who helped develop the model.

In one experiment, the model took a tiny, localized sample of weather data and accurately reconstructed global surface temperatures by filling in 95% of the missing values. “The ability to generalize from a tiny sample of high-quality historical data to the entire planet is useful for a wide range of weather and climate projection tasks,” said Juan Bernabé-Moreno, the director of IBM Research Europe and IBM’s lead for climate and sustainability.

Downscaling, Hurricane Forecasting, and Capturing Earth’s Elusive Gravity Waves

The new weather and climate foundation model is described in a new paper posted on arXiv. Researchers detailed how they built the model and fine-tuned it on specialized data to create three applications with immediate relevance for forecasters.

The first application is designed to zoom in on low-resolution data for more detail, a method known as downscaling. By localizing weather and climate projections, downscaling can provide early warnings of extreme flooding events or hurricane-force winds. IBM’s downscaling application takes data of varying resolutions and types, like temperature and rainfall, and magnifies them by up to 12 times. The downscaling application is available through IBM’s Granite geospatial models on Hugging Face.

The second application focuses on hurricane forecasting. Researchers used the model to accurately reconstruct the track of Hurricane Ida, which struck Louisiana in 2021 and caused $75 billion in damages, making it the fourth costliest Atlantic hurricane on record. In the future, this model could be used to more accurately track where to shore up defenses against oncoming hurricanes.

The third application is designed to improve estimates of gravity waves. In Earth’s atmosphere, gravity waves influence cloud formation and global weather patterns, such as where aircraft turbulence appears. Traditional climate models fail to properly capture gravity waves at high resolution, adding uncertainty to weather and climate projections. This could be game-changing for the orchestration of global supply chains.

Separately, IBM is working with Canada’s weather agency, Environment and Climate Change Canada, to customize the base model for precipitation nowcasting, which involves using real-time radar data to make highly local rainfall predictions several hours out. The hope is that the data-driven foundation model approach could potentially use fewer computing resources and deliver more accurate results.

Learning to ‘Think’ Like a Forecaster

This new weather and climate foundation model joins a growing family of open-source models designed to make NASA’s collection of satellite and other Earth observational datasets faster and easier to analyze. The model owes its flexibility to its hybrid architecture and unusual training regimen.

It’s built on a vision transformer and a masked autoencoder, allowing the model to encode spatial data unfolding through time. By extending the model’s attention mechanism to include time, it’s able to analyze MERRA-2 reanalysis data, which integrates multiple streams of observational data.

The model is also capable of running on both a sphere, as traditional gridded climate models do, and on a flat, rectangular surface. These dual representations allow the model to flip from global to regional views without sacrificing resolution.

During training, researchers fed the model gridded, heavily blacked-out climate reanalysis data and had it reconstruct each image pixel by pixel. They also had the model project the blacked-out image into the future. “The model effectively learns how the atmosphere evolves over time,” said Johannes Schmude, an IBM researcher who helped develop the model.

Asking the model to piece together incomplete weather data and envision its future state had two benefits. It cut in half the amount of data researchers needed to train the model, reducing GPU and energy consumption. It also taught the model how to fill in missing information, both in the present moment and beyond. This is essentially what weather forecasters do.

“Weather data is inherently sparse,” said Schmude. “To learn how to forecast, you have to learn how to fill in gaps.”

What’s Next

IBM and NASA plan to see if their existing open-source geospatial AI model for analyzing earth observation data can be combined with their new model for weather and climate. Released last year, the Prithvi Earth Observation model has been developed into a wide array of applications that have together been downloaded more than 10,000 times. Among other things, the applications have been used to estimate the extent of past floods and infer the intensity of past wildfires from burn scars.

Together, the Earth Observation and weather and climate models could be applied to equally challenging tasks, from forecasting expected crop yields to predicting extreme flooding events and their impact on communities.

For more information, visit the original source at IBM Research.

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