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.
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.
Led by interns from multiple DOE programs, a newly expanded dataset allows researchers to use easy-to-obtain measurements to determine the elemental composition of a promising carbon storage mineral.
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.
The Grid Storage Launchpad dedication event was attended by leaders in grid and transportation energy storage, battery innovation, and industry stakeholders working to transform America’s energy system.
Anderson is one of only 25 women from throughout the world selected to participate in the Epistimi-Women in STEMM Leadership Workshop on “Women in the Energy Sector,” held July 15–19 in Athens, Greece.
Erich Hsieh, Deputy Assistant Secretary for OE’s Energy Storage Division, shared insights about the Grid Storage Launchpad and energy storage innovations .
