Learn about the person taking the helm of the Earth and Space Science Open Archive and their vision for the coming years.
machine learning & AI
Physics + Machine Learning Provide a Better Map of Ocean Measurements
A new study offers a compelling example where the merger of dynamical modeling, machine learning, and ocean measurements enhances oceanographic understanding, monitoring, and mapping.
Machine Learning Masters Weather Prediction
Community datasets and evaluation standards are needed to further advance machine learning for weather prediction.
Autocalibration of the E3SM Atmosphere Model Improves Model Fidelity
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.
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.
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.
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.
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.
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.
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.
