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.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

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.

Researchers at PNNL are pursuing new approaches to understand, predict and control the phenome—the collection of biological traits within an organism shaped by its genes and interactions with the environment.

By combining computational modeling with experimental research, scientists identified a promising composition that reduces the need for a critical material in an alloy that can withstand extreme environments.

A new report details the potential for floating offshore wind to lower energy costs and enhance the resilience of the nation’s western grid.

PNNL’s year in review includes highlights ranging from advancing soil science to understanding Earth systems, expanding electricity transmission, detecting fentanyl, and applying artificial intelligence to aid scientific discovery.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

The National Transmission Planning Study presents several transmission expansion scenarios that would reliably support the growing demand for energy across the nation.

New transmission lines and renewable energy projects would bring economic and climate change benefits to the Western United States’s electricity grid.

Department of Energy presented six teams from PNNL with the Secretary's Honor Award.

Senior Advisor Isabella van Rooyen shares her journey from child to internationally recognized scientist in nuclear materials and manufacturing.

PNNL scientists took thousands of measurements of firefighters in training to learn more about how the body responds to vigorous exercise.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

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