Outlining Best Practices for Portable Optical Particle Spectrometer Use in Atmospheric Studies
Demonstrating methods for using a portable optical particle spectrometer, a miniaturized and highly sensitive instrument, to measure atmospheric aerosol size distributions.
Careful Coupling Improves High-Resolution Climate Simulation
Study demonstrates that choosing more accurate numerical process coupling helps improve simulation of dust aerosol life cycle in a global climate model.
In-Plant Biochemistry Governs High Altitude Fine Particles over the Amazon
Combining aircraft measurements and regional modeling allowed researchers to identify the role of in-plant biochemistry in secondary organic aerosol formation.
Born to Modulate: Researchers Reveal Origins of Climate-Controlling Particles
Aerosol particles imbue climate models with uncertainty. New work by PNNL researchers reveals where in the world and under what conditions new particles are born.
Computational and Applied Geophysical and Geomechanics Laboratory
The Computational and Applied Geophysical and Geomechanics Laboratory enables subsurface fluid dynamics modeling, stability prediction of perforation tunnels, simulation test design, fluid removal or injection monitoring, and more.
Shrivastava Gives Keynote at Atmospheric Optics Meeting
Shrivastava invited to present a keynote talk on secondary organic aerosols at Air & Waste Management Association conference.
Wildfires Trigger Violent Storms with Large Hail and Lightning
Research reveals how heat and aerosols from wildfires initiate and invigorate severe storms.
More Urbanization Could Mean More Rain for Cities
A new study shows that urban heat island effects and increased urban aerosols can spur intense rainfall.
Sulfur Dioxide Emission Height Reveals Uncertainty in Radiative Forcing Across Earth System Models
The Emissions Model Intercomparison Project examined how selected emissions-related properties affected results in 11 global chemistry and Earth-system models.
Emulating Interactions Between Atmospheric Particles and Light with Machine Learning
Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.