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.

Through collaboration with the Department of Homeland Security Soft Target Engineering to Neutralize the Threat Reality Center of Excellence, PNNL is advancing research and development of tools and methodologies to protect crowded places.

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 researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL recently joined the Department of Homeland Security for two technical meetings exploring national security research spanning the threat realm, from chemical and biological attacks to adversarial artificial intelligence.

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.

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.

Purvine shares how research-collaboration workshops focused for women in data science create a sense of inclusion, empowerment, and networking.

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.

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.

The papers were featured in the security and quantum sessions at the 2023 International Symposium on Computer Architecture.

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.

PNNL receives a 2023 Federal Laboratory Consortium Far West Regional Award for a technological innovation that could help make the U.S. a producer of critical minerals used in electronics and energy production.