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

Existing clouds affect the growth of neighboring clouds.

Findings in a new PNNL report show long-duration energy storage will be a necessity in decarbonizing the grid and recommends the planning and procurement process to identify those needs start immediately.

The popular approach of organizing soil bacteria into fast- or slow-growing groups is problematic because most bacteria grow at comparable rates in soil.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.

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.

PNNL researchers explore the feasibility of using low-flying drones to survey sites for low levels of radiation.

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.

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

A review describes the latest techniques for simulating hydropower at large scales to inform power grid planning and operations.

Hydropower generation estimated using water proxy data leads to a revision of the nation’s historical seasonal hydropower output.

As the world races to discover solutions for reaching net zero carbon emissions, a PNNL analysis quantifies the economic value of the existing nuclear power fleet and its carbon-free energy contributions.

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

A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.

The frequency and intensity of storms affecting U.S. coastal areas will likely increase in a warming world.

Researchers investigated the impact of using constant versus spatially varying crop parameters on carbon and energy fluxes in a realistic crop rotation scenario.

A new model uses machine learning to project electricity loads across the United States.