Earthquakes as deep as 50 kilometers below the seafloor were detected by 12 ocean bottom seismometers placed around the Challenger Deep.
machine learning & AI
Machine Learning Emulation of Atmospheric Radiative Transfer
Using machine learning to represent sub-grid processes in weather and climate models holds promise, but also faces challenges. Incorporating physical knowledge can help.
AI Algorithm Provides More Accurate Forecasts of Water Use
The new graph convolutional recurrent neural network (GCRNN) will enable water utilities to forecast water use, even if some sensors fail.
Modeling Entrainment with Machine Learning
Researchers present a new approach to modeling the stochastic mixing process of convection using a machine learning technique.
Fiona Lo: A “Really Long, Convoluted Path” to Health
Lo uses her background in atmospheric sciences to forecast pollen concentrations.
For Western Wildfires, the Immediate Past Is Prologue
A new machine learning approach trained on winter and spring climate conditions offers improved forecasts of summer fire activity across the western United States.
Dynamics of Volcanic Processes
A new cross-journal special collection invites contributions on modern approaches used to investigate dynamics of volcanic processes.
Monitoreando terremotos a la velocidad de la luz
Nueva investigación utiliza la gravedad y un modelo de aprendizaje automático para estimar instantáneamente la magnitud y ubicación de grandes terremotos.
Despite Improvements, China’s Air Remains Unsafe
Toxic particulate matter has decreased by about a third over the past decade, but levels are still above what’s considered healthy.
Learning from Climate Simulations for Global Seasonal Forecast
A probabilistic deep learning methodology that learns from climate simulation big data offers advantageous seasonal forecasting skill and crucial climate model diagnosis information at a global scale.
