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AI and ML to Better Understand Earth Systems

The application of artificial intelligence (AI) can transform the ability to observe, comprehend, and anticipate processes in Earth’s systems. AI and ML computational capabilities can assist researchers and scientists in collecting, understanding, and analysing enormous amounts of data with a faster, more accurate, and more knowledgeable process for decision-making agility.

The researchers and scientists then collaborate to promote Earth and environmental science by using AI and modelling approaches such as machine learning (ML). They convened a workshop to determine particular priorities for addressing computational difficulties and attempted to nurture advancements in AI and ML, algorithms, data management, and other areas.

The workshop was designed by roughly 100 specialists based on 156 white papers given by 640 writers from 112 institutions worldwide. These principles’ consequences can help develop a technology infrastructure that is efficient, accurate, strategic, and convenient while also reaching across resources.

“Effective improvements in Earth system prediction necessitate significant advances across the ​Model-Experiment (ModEx) environment,” said Nicki Hickmon, Associate Director for operations for DOE’s Atmospheric Radiation Measurement Office of Science at DOE’s Argonne National Laboratory.

The workshop narrowed down 17 issues relevant to the integrated water cycle and extreme weather occurrences within that cycle during the session. Experts debated nine topics connected to Earth system forecasts, including hydrology, watershed research, coastal dynamics; the atmosphere, land, oceans, and ice; and climatic variability and extremes.

Researchers analysed issues in each session that indicate the need for revolutionising AI technology and infrastructure to manage complicated tasks in environmental science. Participants investigated the potential of artificial intelligence (AI) to uncover scientific discoveries using technologies such as neural networks, knowledge-informed machine learning, AI architectures, and co-design.

“We need new AI methodologies that integrate process understanding and respect physical laws. (It is) to make estimations of Earth system behaviour scalable, trustable, and relevant under future climate regimes,” Charu Varadharajan, a research scientist at DOE’s Lawrence Berkeley National Laboratory, directs the Earth AI & Data Programme Domain, added.

Through the workshop and report, the researchers and scientists created 2-, 5-, and 10-year targets for the integrated framework development for each focal topic. They also identified priorities for Earth science, computational science, and programmatic and cultural improvements that would support the mission of AI4ESP.

Experts prepared a comprehensive list of scenarios in which AI research and development could help address some of Earth science’s most critical concerns. These challenges include handling and analysing massive volumes of data to increase the ability to detect and predict extreme events and promote the incorporation of human behaviours into theory and models.

Forrest Hoffman, group leader for the Computational Earth Sciences group at the Department of Energy’s Oak Ridge National Laboratory, suggested developing new hybrid models that integrate process-based and ML-based modules is one of the most intriguing prospects.

The modelling frameworks allow for the addition of data regarding poorly understood processes, which can increase accuracy and often result in enhanced computational performance for Earth system models, allowing for more simulations and analyses to be performed within given resource constraints. The workshop provided a cross-disciplinary and cross-mission opportunity for the scientific and application communities to collaborate toward understanding the required advancements.

Programmatic and cultural adjustments are also required to promote a more cohesive mission across diverse scientific and government agencies and a skilled workforce capable of successfully integrating technology into humanistic research and activities. The experts offered options such as AI research centres focused on environmental science, frameworks that enable shared services across multiple communities, and continuing training and support missions.


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