From developing new energy storage materials to revealing patterns of Earth’s complex systems, studies led by PNNL researchers are recognized for their innovation and influence.
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 from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.
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.
Physicist Emily Mace will share her science journey and an interactive presentation about her current research with middle school and high school students from across the country at the National Science Bowl.
PNNL provided ultra-low measurements of argon-39 to date groundwater as part of a collaborative study of the aquifer in California’s San Joaquin Valley. PNNL is one of only a few laboratories worldwide with this capability.
Scientists at PNNL have contributed much of the nuclear science that underlies an international monitoring system designed to detect nuclear explosions worldwide. The system detects radioxenon anywhere on the planet.
Ten staff members from PNNL were invited to attend and lead the various breakout sessions at the Department of Energy Office of Science 5G Enabled Energy Innovation Workshop (5GEEIW), which was held in early March.