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.
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.
The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.
The first tidal turbine deployed in the Pacific Northwest at PNNL-Sequim showcases the Lab’s growing role as a regional center for marine energy research.
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
PNNL researchers developed the dummy payload to evaluate the performance of marine energy device prototypes in the Powering the Blue Economy: Ocean Observing Prize Competition.
The Triton Initative discusses special issue publications from the Triton Field Trials on environmental monitoring recommendations for marine energy applications.
