Microsoft's artificial intelligence model, Aurora, is set to revolutionize weather forecasting and earth system predictions. Developed by Microsoft and presented in a recent publication in the journal Nature, Aurora has been trained on over a million hours of geophysical data obtained from satellites, radars, weather stations, simulations, and forecasts. The extensive training enables Aurora to generate high-resolution weather predictions with unprecedented accuracy and efficiency.
Aurora adopts a fundamentally different approach from traditional weather forecasting models. Instead of relying on systems built over decades, it learns patterns directly from data by identifying complex relationships in historical data of the Earth system. The method allows Aurora to make predictions that could lead to more reliable forecasts of extreme phenomena exacerbated by global warming.
In 2023, Aurora outperformed all operational forecasting centers, including the U.S. National Hurricane Center, by correctly predicting the formation and direction of all hurricanes in the United States more accurately than any meteorological center. It achieved better results than seven prediction centers in 100% of the cases measured for five-day cyclone trajectories. Additionally, Aurora demonstrated the ability to generate high-resolution weather forecasts in seconds, including ten-day forecasts, and track hurricane trajectories with unprecedented accuracy and speed.
"Aurora represents a significant innovation in environmental system forecasting," said Paris Perdikaris, research director at Microsoft Research AI for Science. "It is the first artificial intelligence model that functions as a single fundamental model capable of adapting to different applications, from high-resolution weather forecasting and air quality predictions to monitoring tropical cyclones and ocean waves."
One of the key advantages of Aurora is its efficiency. It operates at operational costs several hundred times lower than traditional models, making environmental forecasts accessible to broader communities worldwide. Current forecasting systems rely on supercomputers and specialized teams for maintenance, which makes them inaccessible to many communities worldwide. Aurora's approach achieves high accuracy with thousands of times lower computational cost.
Aurora surpassed existing models in air quality, ocean wave predictions, tropical cyclone tracking, and high-resolution weather forecasting. For high-resolution weather conditions, Aurora surpassed the performance of the leading numerical weather model IFS HRES in 92% of targets at 0.1° resolution, showing better performance in extreme events. For air quality prediction, Aurora reached or exceeded the Copernicus Atmosphere Monitoring Service in 74% of targets while being approximately 50,000 times faster.
"Aurora outperforms all operational centers dedicated to hurricane forecasting," Perdikaris added. "For the first time, an AI system can surpass all operational centers in hurricane prediction." He also noted, "My team is extending this vision beyond Earth sciences to various applications in science and engineering, creating artificial intelligence systems that can not only predict but also help us understand complex physical phenomena across multiple disciplines."
The development of Aurora was achieved in a remarkably short period. The experiments necessary to train Aurora lasted between four and eight weeks, while years are needed to develop benchmark models. The authors noted that the rapid progress was only possible thanks to the enormous amount of data generated by decades of research with classical numerical methods.
Aurora serves as a base model for the Earth system and could be adapted for other uses beyond weather prediction, including non-meteorological applications such as agricultural productivity or pollination patterns. The model can be adjusted for any desired prediction task and offers superior results to current ones at a much lower cost.
Looking ahead, researchers contemplate that Aurora could operate directly with observational data. "In the next five to ten years, the 'holy grail' will be to develop systems that can work directly with observations from remote sensing sources, such as satellites and meteorological stations, to generate high-resolution forecasts wherever needed," Perdikaris stated. "We are at the beginning of a new era in atmospheric science."
Aurora's success highlights a paradigm shift in weather prediction, demonstrating the potential of AI models in forecasting Earth's systems and weather phenomena. This includes providing timely warnings about extreme events, such as hurricanes, floods, and wildfires. Aurora enables better early warnings and mitigation of natural disasters, serving as an integral tool for such warnings.
The researchers emphasize that Aurora could evolve in directions. "The model can be easily scaled to generate ensembles of predictions, something key in high-uncertainty situations, such as long-term forecasts or very localized phenomena," they state. They also note that the performance ceiling of the system has not yet been reached. "Our results suggest that it is possible to further improve accuracy if trained with more diverse data or if the model size is increased."
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