The research presented in this article significantly enhances the ability to predict and prepare for compound flood events along the U.S. coastline.

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

Pacific Northwest National Laboratory scientists discussed their materials research at Accelerate’24.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

Pyrocumulonimbus clouds are increasing in frequency as large wildfires become more prevalent in a warming climate. These clouds can inject smoke particles into the atmosphere, where they can remain suspended for several months.

Samrat (Sam) Chatterjee, a PNNL chief data scientist and team leader with the Data Sciences and Machine Intelligence group, was co-author of a CSET workshop report on agentic artificial intellilligence

This study provides a comprehensive analysis of isolated deep convection & mesoscale convective systems using self-organizing maps to categorize large-scale meteorological patterns and a tracking algorithm to monitor their life cycle.

The speed and agility of cloud computing opens doors to completing advanced computational chemistry workflows in days instead of months.

Joint Appointee Mitra Taheri spoke on the AI in Government episode of the EY Leading into Tomorrow podcast.

This study explored the future effects of climate change and low-carbon energy transition (i.e., emission reduction) on Arctic offshore oil and gas production.

Mengqi Zhao and Chris Vernon were selected to serve in editorial roles at JOSS for Earth science and data science, respectively.

PNNL chemist John Shilling builds custom chamber helping uncover how aerosol particles and clouds contribute to Earth’s changing climate.

Using numerical simulations to reproduce the laboratory experiments, this study reveals that liquid droplets are present near the bottom surface, which warms and moistens the air in the chamber.

This work shows that linear pattern scaling is an effective means of obtaining global-to-local relationships for CMIP6 models, as it has been in past model eras.

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy

Ján Drgoňa invited as a speaker at the Grainger Foundation Frontiers of Engineering 2024 Symposium of the National Academy of Engineering.

This study examined the role of river sinuosity using computer models to understand what drives hyporheic exchange, a process that significantly affects water quality and ecosystem health.

PNNL biodefense experts seek to identify, understand and mitigate the risks of biological pathogens—whether naturally occurring or intentionally created—so steps can be taken to prepare and respond.

PNNL postdoc Pengfei Shi won first place in the Early Career Researcher Poster Competition at the recently concluded NOAA Subseasonal and Seasonal Applications Workshop.