PNNL’s Chris Chini has been named a guest editor of Environmental Research: Infrastructure and Sustainability’s special issue examining energy infrastructure vulnerabilities from physical and natural threats.

PNNL scientists have put forth a new approach to protect the electric grid, creating an approach that sorts and prioritizes cyber threats on the fly.

PNNL chief scientist and joint appointee Auroop Ganguly was recently appointed a Distinguished Member of the Association for Computing Machinery, a high honor from the world's largest computer science society.

Decreased snow cover observed over the past few decades and projected for the future suggest increasing snow droughts that threaten water security and management.

Using regional meteorological data from an atmosphere reanalysis product, scientists identified 12 unique winter weather systems in the Puget Sound area, featuring differing precipitation and temperature responses to climate variabilities.

ChatGrid™ is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.

The Emissions Model Intercomparison Project examined how selected emissions-related properties affected results in 11 global chemistry and Earth-system models.

The Earth System Model Aerosol–Cloud Diagnostics package version 2 uses aircraft, ship, ground, and satellite measurements to evaluate detailed physical processes in aerosols, clouds, and aerosol–cloud interactions.

The physics-informed machine learning optimization framework is now PNNL’s most popular open-source repository on GitHub.

Samantha Erwin, a PNNL data scientist, was recently appointed to the Society for Industrial and Applied Mathematics Membership Committee.

PNNL researchers use chemometric analysis to model chemistry in molten salt.

With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

In soil, microbes produce and consume methane. Using a technique called pool dilution, researchers can separate the rate of methane production and consumption from the net rate.

Clustering wildfires into four types based on their land-atmosphere drivers can potentially improve regional wildfire predictions.

Ang participated in a White House-hosted CHIPS R&D event and roundtable discussion with senior leaders from industry, academia and key government agencies.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.