The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.
To identify communities ready for marine energy, help them realize their energy resilience goals, and facilitate community leadership in future projects, two national laboratories are developing the Deployment Readiness Framework.
The roles of the various environmental variables in the transition from suppressed to active tropical precipitation regimes are characterized using statistical analysis and machine learning.
Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.
A modeling study finds that multiple factors almost perfectly balance under anthropogenic greenhouse gas forcing, leaving no footprint on the dynamically induced ocean heat storage in the Southern Ocean.
Claudia Tebaldi, a PNNL Earth scientist, has been named a Fellow of the American Geophysical Union. Tebaldi and others will be recognized at AGU23 in December.
Floating offshore wind farms could potentially triple the Pacific Northwest's wind power capacity while offsetting billions of dollars in costs for utilities, ratepayers, insurance companies, and others.
A team of researchers at PNNL has created a publicly available Hydropower eLibrary to improve access to information that could help streamline the FERC environmental review and licensing process.