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.

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.

Microbiome and soil chemistry characterization at long-term bioenergy research sites challenges idea that switchgrass increases carbon accrual in surface soils of marginal lands.

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

PNNL researchers investigate the environmental conditions, from soil moisture to surface temperature, that lead to wildfire.

New biogeochemical modeling uses high-resolution mass spectrometry data to reveal how organic matter thermodynamics affect ecosystems across scales.

The map fills in a portion of the study site missing from sampling studies and enables a better understanding of hydrological dynamics in a complex river corridor.

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.

This committee represents the country’s soil science community in the International Union of Soil Sciences, advises The National Academies, and communicates with professional societies and organizations.

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.

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.

Ecosystem feedback fire model opens new investigative pathways into impacts of fire on future climate.

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.