"Typically, forecasts of the atmosphere and oceans require data assimilation - combining different sources of information about the weather to obtain a more accurate result," said Romit Maulik, assistant professor in the College of IST. "However, that data assimilation can slow down the forecast time significantly. We plan to use computer vision to dramatically accelerate this process."

Computer vision, a subset of AI, leverages machine learning and neural networks to enable computers to analyze and interpret visual data. This approach allows systems to learn from such data to improve performance over time.

The research team includes Steven Greybush, associate professor of meteorology in Penn State's College of Earth and Mineral Sciences, as well as scientists from Argonne National Laboratory, NASA Goddard Space Flight Center, the National Oceanic and Atmospheric Administration, and the University of Chicago. They aim to incorporate various data sources, including satellite imagery, to build on previous forecasting efforts that utilized transformer-based AI algorithms and machine learning techniques.

"The work will involve retraining some portions of our model to take these new datasets as inputs and improve predictions," Maulik explained. "Then, we will integrate these improved algorithms into the NASA Goddard Earth Observing System so it can rapidly incorporate satellite system observations into its operational data assimilation workflows."

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