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.
Battery energy storage systems are being proposed in municipalities across the U.S. PNNL researchers can help community planners guide safe siting and operations.
Findings in a new PNNL report show long-duration energy storage will be a necessity in decarbonizing the grid and recommends the planning and procurement process to identify those needs start immediately.
With an eye on renewable, accessible, and resilient power, PNNL researchers show hyper-local microgrids are a viable option, if designed with the right mix of sources.
A comprehensive literature review linking algae and antivirals determines compounds in algae may demonstrate an exceptional—and as yet untapped—potential to combat viral diseases at every point along the viral infection pathway.
Researchers at PNNL examined heat pump water heater (HPWH) operation in Pacific Northwest residences, gaining insights into HPWH electricity use patterns. Part of the study captured trends during a COVID-19 stay-at-home order.
