A new cross-journal special collection invites contributions on how machine learning can be used for solid Earth observation, modeling and understanding.
Machine learning and data on aquifer type, sediment thickness, and proxies for irrigation water use has been used to produce the most comprehensive map of land subsidence in the western U.S. to date.
As weather and climate models grow larger and more data intensive, the amount of energy needed to run them continues to increase. Are researchers doing enough to minimize the carbon footprint of their computing?
Developing trustworthy artificial intelligence for weather and ocean forecasting, as well as for long-term environmental sustainability, requires integrating collaborative efforts from many sources.
Our August issue explores the way we process, analyze, and clearly present the massive amounts of information collected by scientists today.
Humans found hundreds of thousands of craters on Mars greater than 1 kilometer in diameter, but now computers automate the process delivering crater counts as well as geologically meaningful ages.
Ensemble learning models for estimating past changes of terrestrial water storage from climate are presented and tested in the Pearl River basin, China.
Experts agree that as urbanization continues through the 21st century, cities need to focus on sustainable development to meet climate goals.
An efficient, low-resolution machine learning model can usefully predict the global atmospheric state as much as 3 days out.
Color plays a major role in the analysis and communication of scientific information. New tools are helping to improve how color can be applied more accurately and effectively to data.