Researchers demonstrate an AI that can be taught to recognize cloud types by looking at millions of satellite images of clouds without requiring human input.

Researchers measured ice nucleating particles composition at the Southern Great Plains atmospheric observatory, enabling them to identify sources of particles that make them effective ice nucleators.

A recent study suggests a higher global mean precipitation increase and narrows the uncertainty range.

PNNL and collaborators developed new models—recently approved by the U.S. Western Electricity Coordinating Council (WECC)—to help utilities understand how new grid-forming inverter technology will enhance grid stability.

Researchers find ultrafine particles abundant in smoke from Amazon forests fire may intensify storm clouds and heavy rain.

Using a refined Earth system model, researchers found that wetlands over North America will be significantly affected by climate change under future scenarios

Researchers use dataset combining observational data with advanced numerical simulations to investigate the characteristics, drivers, and trends of extreme heat events in the High Arctic over past four decades

Researchers develop framework that tracks the aerosol–cloud interactions along the trajectories of air parcels and embed framework into Weather Research Forecast model.

Study develops high-resolution land surface data for 2001 to 2020, including parameters of land use, vegetation, soil, and topography and demonstrated its use in k-scale simulation using the Energy Exascale Earth System Model.

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.

Researchers provide clear evidence to show that the fourfold Arctic Amplification over recent decades is an anomaly caused by dominant modes of natural variability.

Researchers synthesize molecular-level laboratory experiments to develop comprehensive model representations of new particle formation and the chemical transformation of precursor gases.

Researchers introduce revised emission treatment that conserves mass and preserves the original heterogeneity.

Researchers show application of a causal model better identifies direct and indirect causal relations compared to correlation and random forest analyses performed over the same dataset.

PNNL publishes Port Electrification Handbook to aid the nation’s maritime and commercial ports during this energy transition.

Study explores Exploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments, a consortium of scientists interested in the exchange between water and land in coastal systems.

With Earth system models running at higher resolutions, researchers evaluated the sensitivity of aerosol properties to horizontal grid spacing.

This study demonstrates a new model that integrates complex organic matter (OM) chemistry and multiple electron acceptors to predict kinetic rates of OM oxidation.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

Study reviews the loss of carbon dioxide from soils and explores most compelling challenges and opportunities for the future.