PNNL Chief Diversity Officer and Director for STEM Education and PNNL Battery Materials and System group leader will receive Clean Energy Education and Empowerment (C3E) awards at the 2023 C3E Women in Clean Energy Symposium.

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

PNNL’s Grid Storage Launchpad delivers tomorrow’s energy storage solutions today.

The Department of Energy’s Vehicle Technologies Office recently issued two awards to researchers at PNNL for their contributions to areas that are crucial for the expansion of electric vehicles.

Regional wind data helps utilities save money, plan for windy days.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

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.

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

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.

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

DOE gives Hydrogen Program Special Award to PNNL's Hydrogen Safety Panel at department's 2023 Annual Merit Review and Peer Evaluation Meeting.

PNNL researchers are helping to better define the need for grid energy storage in future clean energy scenarios, as well as working to improve technologies for storing renewable energy so it's available when and where it's needed.

Using a combination of satellite data and modeling to study the temperatures and humidity people might feel in urban areas, researchers have pinpointed who in the U.S. is most vulnerable to heat stress.