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

A team of researchers from PNNL provided technical knowledge and support to test a suite of techniques that detect genetically modified bacteria, viruses, and cells.

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.

PNNL researchers are key contributors to GAO report

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.

The ChemSpace Tool, when fully developed, is intended to divide chemical space into three subsets: the detectable space, the identifiable space, and the region that includes compounds that are not detectable or identifiable.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

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.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.

Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.

How a model treats gas transfer behavior onto dust and sea salt particles significantly influences nitrate aerosols and their effects on climate.

Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.

Secondary organic aerosol formation from monoterpenes is more strongly influenced by oxidant and monoterpene structure than by nitric oxides and hydroperoxy radical concentrations.