A theoretical index linking precipitation and atmospheric evaporative demand explains historical land humidity changes across observations, reanalyses, and climate models.

Observations, reanalysis, and E3SMv3 experiments linked inter-annual variability of Congo Basin surface temperature to the zonal position of precipitation over the Indo-Pacific maritime continent region.

Quasi-stationary Rossby waves shape North American temperature extremes, but higher model resolution does not always improve their modeled representation.

Tests show which low-cost 3D method best matches field measurements to minimize errors in mapping headwaters.

Watershed-based representation discretized into topographic subgrid units better captured small-scale land cover, precipitation, temperature, and snow variability across multiple spatial scales.

Numerical simulations reveal how well-controlled wall temperature and moisture can generate a stratocumulus-like cloud top in a laboratory cloud chamber.

A new tracer model tracked how May 2015 storm water re-entered the atmosphere and contributed to later rainfall.

Physics-informed and data-driven neural network models enhance Earth system models in simulating compound flooding at river-ocean interfaces.

Researchers showed that spiking neural networks can be accurate for regression problems, opening opportunities for efficient neuromorphic computing.

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.

More rain and less snow shorten how long water stays in watersheds.

​​Tests show which low-cost 3D method best matches field measurements to minimize errors in mapping headwaters.​

PNNL scientists were among the specialists keeping the crowds safe at the recent FIFA World Cup competition.

New partnership agreement to focus on AI, robotics, and advanced energy 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.

A research team from Pacific Northwest National Laboratory used machine reasoning to schedule scientific workflows while guaranteeing quality of service (QoS).

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

PNNL researchers highlighted advances in AI for scientific discovery at the AI+ Expo, showcasing generative AI, autonomous research workflows, and scalable AI systems supporting energy, security, and national competitiveness.

At a geospatial intelligence conference, senior data scientist Adam Attarian participated in a panel discussing how national laboratories are delivering science, technology, and partnerships to enhance modeling, analysis, and innovation.

David Wunschel, a chemist and Laboratory Fellow, participated on a panel focused on how emerging technologies are transforming CBRN defense, detection, and response across military and government operations.