Advancing AI for Earth Science: A Data Systems Perspective - Eos

Helping address these problems, however, is a wealth of data sets--containing atmospheric, environmental, oceanographic, and other information--that are mostly open and publicly available. This fortuitous combination of pressing challenges and plentiful data is leading to the increased use of data-driven approaches, including machine learning (ML) models, to solve Earth science problems. Machine learning, a type of artificial intelligence (AI) in which computers learn from data, has been applied in many domains of Earth science (Figure 1). In traditional Earth science modeling, researchers use a top-down approach based on our understanding of the physical world and the laws that govern it. This approach allows us to interpret model outputs, yet it can be limited by the sheer amount of computing power required to solve large problems and by the difficulty of finding patterns where we don't expect them.