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.

A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.

A holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.

Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.

Four PNNL researchers received highly competitive DOE Early Career Research Program awards, providing five continuous years of funding for their projects.

A new open-source feature tracking package is now available to facilitate advanced model evaluation, model development efforts, and scientific discovery.

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

ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.

Two researchers at PNNL have been named fellows of the American Chemical Society: Karthikeyan Ramasamy and Yuyan Shao.

This study demonstrated that a large-scale flooding experiment in coastal Maryland, USA, aiming to understand how freshwater and saltwater floods may alter soil biogeochemical cycles and vegetation in a deciduous coastal forest.

Recovery Simulator and Analysis tool further strengthens PNNL’s short-term resilience planning efforts for Puerto Rico.

Waste Management Symposia ‘Paper of Note’ and ‘Superior Paper’ awards recognize PNNL contributions to advancing radioactive waste and materials management.

Algae can collect and concentrate rare earth metals from seawater—an alternative to imported sources mined from the ground.

Models show that in a warmer climate, the 2012 North American derecho would shrink and decay earlier in Mid-Atlantic coastal areas.

By adding rain, snow, and rain-on-snow precipitation data to a background model, a new scheme pinpoints local flood risks in order to improve the design of small-scale hydrological infrastructure.

A new flow battery design achieves long life and capacity for grid energy storage from renewable fuels.

PNNL researchers led a collaborative effort to showcase the most up-to-date, innovative, commercial cement and concrete technologies.

Developed a data-assimilation method based on physics-informed deep learning to resolve downscaled flow fields within a large-scale river model.

Chemical Engineer Yong Wang explains the influence and opportunity for joint appointments. Wang maintains one of the longest joint appointment tenures at PNNL.

Unveiling diverse scenarios and implications for water resource management in a rapidly changing world.