Tracking water age using a new precipitation tagging method within a hydrologic model generates key information to support hydropower energy and other water-dependent systems.
A new report highlights a public workshop hosted by the National Academies Forum on Microbial Threats and features PNNL's Lauren Charles and the AI-Driven One Health Security program.
Now, anyone can easily explore and access data from a nationwide map of data centers, the infrastructure that powers them, and projections of future data center locations.
Distributed science is thriving at PNNL, where scientists share data and collaborate with researchers around the world to increase the impact of the work.
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.
A study by researchers at PNNL assessed the feasibility of using strontium isotope ratios and an existing machine learning–based model to predict and verify a product’s source—in this case, honey.
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.
Large clusters of organized thunderstorms, called mesoscale convective systems, account for half of summer rainfall in the central and eastern U.S. Their formation can be influenced by weather patterns in the mid-levels of the troposphere.
Atmospheric aerosol particles modulate climate and the Earth’s energy balance by scattering and absorbing sunlight. They also seed clouds, acting as cloud condensation nuclei.
High-resolution hydrodynamic-sediment modeling shows that inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics drive sediment deposition in Amazonian floodplains.