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.
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.
PNNL researchers established an Internet of Things Common Operating Environment (IoTCOE) laboratory to explore the risks associated with IoT connectivity to the internet, the energy grid and other critical infrastructures.
PNNL research has created a unique video that shows oxygen bubbles inflating and later deflating inside a tiny lithium-air battery. The knowledge gained from the video could help make lithium-air batteries that are more compact, stable and
