The Tackling Climate Change with Machine Learning Workshop at the International Conference on Learning Representations (ICLR), held in Vienna, Austria, and remotely in May, awarded a best paper award to a group exploring and utilizing machine learning to predict weather.
This illustration of three globe graphs shows a five-day forecast of near-surface wind speed and mean sea level pressure. On Dec. 31, 2020, an extratropical cyclone impacted Alaska, setting a new North Pacific low-pressure record. A team of researchers from the University of California-Los Angeles, Carnegie Mellon University, Argonne National Laboratory, and Penn State evaluated Stormer's ability to predict this record-breaking event five days in advance. Image Credit: Provided by the researchers.
Romit Maulik, a Penn State Institute for Computational and Data Sciences (ICDS) co-hire and assistant professor in the College of Information Sciences and Technology co-authored the winning article “Scaling transformer neural networks for skillful and reliable medium-range weather forecasting.”
This has been a year-long collaboration between the University of California-Los Angeles, Carnegie Mellon University, Argonne National Laboratory, and Penn State.
Romit Maulik, Study Co-Author and Assistant Professor, College of Information Sciences and Technology, Penn State University
The study looked at the utilization of modern artificial intelligence (AI) techniques for forecasting compared to traditional approaches currently used for operational forecasts.
Maulik added, “It is a paradigm shift from looking at the classical forecasts provided by several agencies. Those forecasts are typically obtained with very large computing resources, and it can be computationally costly. We thought, what if we took an alternative route?”
According to Maulik, the AI model learns weather patterns using data from historical sources like satellite images and archival forecasts. It is built on computer vision techniques.
“Then, a trained model can make forecasts in real time, without requiring access to very large computational resources. Once the neural networks are trained and released, the model deployment can be done effectively on a laptop and, eventually, on increasingly smaller resources such as cell phones,” Maulik further added.
According to Maulik, the ICLR hosts seminars on AI-related subtopics where researchers can present their work and get feedback.
Maulik stated, “Getting accepted into a workshop, which is quite competitive, maximizes the paper’s visibility. It helps us get good feedback from both the AI and the domain sciences community and significantly improve our methods. The award itself is great; it validates our hard work. However, our long-term goal remains the same. We want to find ways to improve our current models and provide a viable competitor to classical weather forecasting approaches.”
One of the objectives, according to Maulik, is for the researchers to be able to predict weather extremes more accurately than existing models may be able to.
Maulik concluded, “Our eyes are set on grander challenges. That being said, as computational scientists, we want to solve the problem and we think of the tool after. We try to balance classical and machine learning methods and are not partial to either.”
Maulik, Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Veerabhadra Rao Kotamarthi, Ian Foster, Sandeep Madireddy, and Aditya Grover are among the article's authors and collaborators.