Impact and turbulence models for river tremor are adapted and combined into a model that predicts the amplitude and frequency content of volcanic eruption tremor.
forecasting
Posted inNews
Scientists Claim a More Accurate Method of Predicting Solar Flares
Supercomputer 3D modeling of magnetic fields could help mitigate damage from geomagnetic storms.
Posted inEditors' Highlights
Rainwater Harvesting Can Reduce Flooding as Well as Saving Water
Weather forecasting can greatly improve benefits of rainwater harvesting.
Posted inResearch Spotlights
Regional Sensitivities Strongly Affect Modeled Climate Extremes
Analysis of temperature and precipitation extremes in two generations of CMIP climate models revealed similarities in regional climate sensitivities, contrasting with divergent global sensitivities.
Posted inEditors' Highlights
Ensemble Modeling of Coronal Mass Ejection Arrival at 1 AU
Heliospheric imaging data can be used in ensemble modeling of CME arrival time at Earth to improve space weather forecasts, treating the solar wind as a 1-D incompressible hydrodynamic flow.
Posted inEditors' Highlights
Real-time Ground Motion Estimation for Large Earthquakes
Advanced computing technology can be used to forecast ground shaking from earthquakes and provide an early warning in real time.
Posted inEditors' Highlights
Advancing Ensemble Methods in Space Weather Forecasting
A short, must-read, report for anyone developing new space weather forecasts.
Posted inResearch Spotlights
How to Improve Space Weather Forecasting
The field of space weather forecasting could take cues from its Earthly counterpart to increase the reliability of models as well as warning times ahead of inbound solar storms.
Posted inEditors' Vox
Bridging the Gap Between Weather and Climate Predictions
A special collection on subseasonal-to-seasonal prediction presents the latest progress in filling the gap between short-term weather prediction and longer-term climate prediction.
Posted inResearch Spotlights
Improving Atmospheric Forecasts with Machine Learning
An efficient, low-resolution machine learning model can usefully predict the global atmospheric state as much as 3 days out.