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 researchers received an award from the Materials, Metals & Materials Society (TMS) for a publication on friction riveting.

Solid phase manufacturing can create new custom metal alloys through an innovative process called solid phase alloying, researchers from PNNL report.

The 98th ACS Colloid and Surface Science Symposium will be co-organized by Shuai Zhang and Jim De Yoreo.

PNNL-Sequim team wins ESTCP Project of the Year for their work supporting underwater munition remediation.

PNNL has developed seaweed-based inks and materials for 2-D and 3-D printing that can be used for a multitude of applications in the art, medical, STEM, and other fields.