By Manil Maskey, Hamed Alemohammad, Kevin J. Murphy, and Rahul Ramachandran
The Earth sciences present uniquely challenging problems, from detecting and predicting changes in Earth’s ecosystems in response to climate change to understanding interactions among the ocean, atmosphere, and land in the climate system. 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 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). Such applications include land cover and land use classification [Jin et al., 2019], precipitation and soil moisture estimation [Kolassa et al., 2018], cloud process representations in climate models [Rasp et al., 2018], crop type detection and crop yield prediction [Wang et al., 2019], estimations of water, carbon, and energy fluxes between the land and atmosphere [Alemohammad et al., 2017], spatial downscaling of satellite observations, ocean turbulence modeling [Sinha, 2019], and tropical cyclone intensity estimation [Pradhan et al., 2018], among others [Zhu et al., 2017].
Maskey, M., H. Alemohammad, K. J. Murphy, and R. Ramachandran (2020), Advancing AI for Earth science: A data systems perspective, Eos, 101, doi: 10.1029/2020EO151245. Published on 06 November 2020.