The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.

Researchers have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

PNNL is highlighting scientific and technical experts in the national security domain who were recently promoted to scientist and engineer level 5, one of PNNL’s most senior research roles.

Chemist April Carman was recognized for her career accomplishments with the Professional Achievement Award from the University of Nevada, Reno, College of Science.

PNNL commemorates 9/11 and reflects on the 20 years of science and technology produced since to protect against threats and make America safer.

New study elucidates the complex relaxation kinetics of supercooled water using a pulsed laser heating technique at previously inaccessible temperatures.

PNNL's Rich Ozanich, project manager of opioids standards and equipment testing, served on an expert panel about opioid detection as part of a Department of Homeland Security S&T research and development showcase.

PNNL’s infrared pulsed heating technique reveals supercooled water’s weird behavior; opens door to other fluid studies at new Energy Sciences Center.

In the face of the COVID-19 pandemic, PNNL pivoted to online learning approaches to conduct critical national security training across the globe.

PNNL researchers are using explainable artificial intelligence to develop new ways to understand and interpret data for national security.

One year ago, Verizon announced a partnership that made PNNL the U.S. Department of Energy’s first national laboratory with Verizon 5G ultra-wideband wireless technology.

Explainable artificial intelligence—understanding and explaining the reasoning behind AI decisions—is a necessity for national security.

Explainable AI helps scientists understand and untangle the threads of information that the system uses to make choices or recommendations.

In 2020, virtual Washington State University teams successfully worked together in a program sponsored by the National Nuclear Security Administration’s (NNSA) Office of International Nuclear Safeguards.

A recent edition of the Infrastructure Resilience Research Group Journal featured an article written by PNNL researchers Rob Siefken and Jake Burns about “Design Basis Threat and the Low Threat Environment.”