Rigorous mathematical methods can help avoid unnecessary simplifications in numerical models of atmospheric clouds.

More accurate methods provide better results in numerical simulations of atmospheric clouds by avoiding accidental contradictions.

Despite the challenges, the talented and dedicated staff at Pacific Northwest National Laboratory kept delivering for the nation.

PNNL researchers are examining the volatile radioisotope iodine as it evolves in the atmosphere and as it lands on a filtering surface.

Urban landscapes and anthropogenic aerosols have the potential to make gusts stronger, hail larger and even steer storms toward cities.

PNNL computational biologists, structural biologists, and analytical chemists are using their expertise to safely accelerate the design step of the COVID-19 drug discovery process.

California and other areas of the U.S. Southwest may see less future winter precipitation than previously projected by climate models, according to new research that corrects for a long-standing model error: the double-ITCZ bias.

Long-term PNNL support has helped enable the continued success of the Chesapeake Large-Scale Analytics Conference.

As COVID-19 was limiting in-person contact, halting travel, and creating additional barriers, researchers at PNNL were working to find solutions on how they could still get work done while establishing new safety protocols.

Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.

Rey Suarez was the keynote speaker at the Preparatory Commission of the Comprehensive Nuclear-Test-Ban Treaty Organization’s Specialized Technical Meeting on Preventive and Predicative Maintenance of the International Monitoring System.

PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.

Ken Geelhood is a nuclear engineer who is studying how the challenging conditions of a nuclear reactor affect the reliability of fuel.

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.

PNNL data scientists Ján Drgoňa and Draguna Vrabie are working to reduce deployment costs of advanced energy control systems for buildings.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

PNNL and Microsoft Quantum partner to link quantum circuits to powerful government supercomputers.

Pacific Northwest National Laboratory researchers developed a graphical processing unit (GPU)-centered quantum computer simulator that can be 10 times faster than any other quantum computer simulator.

Researchers at PNNL have developed a bacteria testing system called OmniScreen that combines biological and synthetic chemistry with machine learning to hunt down pathogens before they strike.

PNNL’s new Smart Power Grid Simulator, or Smart-PGSim, combines high-performance computing and artificial intelligence to optimize power grid simulations without sacrificing accuracy.