A surrogate model was trained to predict E3SM atmosphere model spatial fields as a function of uncertain physical parameters and used to optimize the parameters for present-day climate.
machine learning & AI
Posted inEditors' Vox
Harmonizing Theory and Data with Land Data Assimilation
Land data assimilation advances scientific understanding and serves as an engineering tool for land surface process studies, reflecting the trend of harmonizing theory and data in the big data era.
Posted inEditors' Highlights
Learning Data Assimilation Without the Help of the Gaussian Assumption
Major Earth system processes are non-linear and non-Gaussian, and so should be our data assimilation approaches.
Posted inEditors' Highlights
Machine Learning Accelerates the Simulation of Dynamical Fields
Fourier neural operator solvers accurately emulate particle-resolved direct numerical simulations and significantly reduce the computational time by two orders of magnitude.
Posted inEditors' Vox
Introducing the new Editor-in-Chief of JGR: Solid Earth
Learn about the person taking the helm of JGR: Solid Earth and his vision for the coming years.
Posted inScience Updates
Decoding the Dialogue Between Clouds and Land
New research is challenging established assumptions about how clouds form and interact with Earth’s surface. One result may be better weather forecasts.
Posted inEditors' Highlights
Deep Learning Facilitates Earthquake Early Warning
A deep learning model trained with real-time satellite data significantly reduces the time to predict the ground motion of big earthquakes.
Posted inEditors' Highlights
Machine Learning for Geochemists Who Don’t Want to Code
Geochemistry π is an easy-to-use step-by-step interface to carry out common machine learning tasks on geochemical data, including regression, clustering, classification, and dimension-reduction.
Posted inNews
How Did We Miss 20% of Greenland’s Ice Loss?
The ice loss was hidden in places existing monitoring methods can’t reach, such as hard-to-map fjords. Machine learning helped scientist revise mass loss estimates and uncover patterns in glacial retreat.
Posted inEditors' Highlights
Using Machine Learning to Reconstruct Cloud-Obscured Dust Plumes
Satellite-observed dust plumes from North Africa are frequently obscured by clouds, but a new study uses machine learning to reconstruct dust patterns, demonstrating a new way to validate dust forecasts.