PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Pyrocumulonimbus clouds are increasing in frequency as large wildfires become more prevalent in a warming climate. These clouds can inject smoke particles into the atmosphere, where they can remain suspended for several months.

Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy

Researchers show how satellite observations from the MODerate Resolution Imaging Spectroradiometer and CloudSat radar can be used to constrain the ACI radiative forcing that is linked to droplet collection in marine liquid clouds.

A set of papers combines surface science measurements on tiny particles with uncrewed aerial systems to better understand and model aerosols.

Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.

Models have identified a new source of anthropogenic emissions driving the observed weakening of the Atlantic Meridional Overturning Circulation.

Atmospheric rivers, filaments of intense moisture transport in the atmosphere, can now be automatically detected in satellite observations.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

This study revealed that fresh organic vapors are soluble in particulate organics that are actively growing in size. However, if the particulate matter ages, fresh organic vapors can no longer mix with the organic matter.

Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.

Partitioning measured ice nucleating particle concentrations into individual particle types leads to a better understanding of the sources and model representations of these particles.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

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.

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

The complex interactions between the land and atmosphere can affect cloud growth.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.