A new study couples an emission and transport scheme of pollen from vegetation, and explores pollen’s evolution in different atmospheric conditions and its impacts on clouds and precipitation.
Jiwen Fan
Editor, Journal of Advances in Modeling Earth Systems
Posted inEditors' Highlights
Simulating Clouds on Arbitrary Grids in Any Spatial Direction
A new non-column based spectral element implementation of cloud microphysics enables full 3D flexibility in computing clouds and improves computational efficiency.
Posted inEditors' Highlights
Unleashing the Power of AutoML for Atmospheric Research
Automated Machine Learning liberates domain scientists from selecting learners and hyperparameters and discovers the importance of atmospheric trace gases for improving surface PM2.5 estimates.
Posted inEditors' Highlights
Examining Aerosol-Cloud-Climate Interactions at a Large Scale
A new numerical setup demonstrates that aerosols could affect clouds, and hence the radiation budget, thousands of kilometers from their location.
Posted inEditors' Highlights
A Significant Advancement in Modeling the Global Methane Cycle
The capability to fully model the global methane cycle advances the international climate science community’s ability of providing essential evidence to underpin climate mitigation policy.
Posted inEditors' Highlights
Advanced Real-Time Prediction of Storms With 30-Second Refresh
A new-generation weather radar and a massive supercomputing system enables forecasts of storms refreshed every 30 seconds, a significant development in severe weather prediction.
Posted inEditors' Highlights
Accurate and Fast Emulation With Online Machine-Learning
Online training produces more accurate and stable machine-learned models than classic offline learning from big data sets.
Posted inEditors' Highlights
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.
Posted inEditors' Highlights
Modeling Entrainment with Machine Learning
Researchers present a new approach to modeling the stochastic mixing process of convection using a machine learning technique.
Posted inEditors' Highlights
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.