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.
machine learning & AI
Posted inScience Updates
Creating Data Tool Kits That Everyone Can Use
Earth scientists outline challenges to making the growing wealth of available data more accessible and to using data services for interdisciplinary research and applications.
Posted inNews
Are Cosmic Rays a Key to Forecasting Volcanic Eruptions?
A combination of relativistic particles and artificial intelligence may provide a new way to forecast when a volcano could erupt.
Posted inEditors' Highlights
A New Global Map of Seafloor Fluid Expulsion Anomalies
The first open-source database of SEAfloor FLuid Expulsion Anomalies (SEAFLEASs) at a global scale reveals their distribution and physical parameters.
Posted inResearch Spotlights
Machine Learning Improves Weather and Climate Models
New research evaluates the performance of generative adversarial networks for stochastic parameterizations.
Posted inNews
Combining AI and Analog Forecasting to Predict Extreme Weather
New deep learning technique brings an obsolete forecasting method “back to life” to predict extreme weather events.
Posted inEditors' Highlights
Representing Estuaries and Braided Rivers as Channel Networks
The human eye is quite good at identifying channel networks among the rich patterns exhibited by estuaries and braided rivers, but computers have a harder time doing so. Could they do better?
Posted inNews
New Study Hints at Bespoke Future of Lightning Forecasting
Researchers used machine learning to develop a model that can predict lightning strikes to within 30 minutes of their occurrence and within 30 kilometers of a weather station by using just four simple atmospheric measurements.
Posted inNews
Using Satellites and Supercomputers to Track Arctic Volcanoes
New data sets from the ArcticDEM project help scientists track elevation changes from natural hazards like volcanoes and landslides before, during, and long after the events.
Posted inEditors' Highlights
Using a Machine to Help Us Learn About Jupiter’s Aurora
A first usage of principal component analysis on Hubble images of Jupiter’s auroral ovals reveals the most common patterns, and machine learning classification reveals their physical causes.