PNNL is honoring its postdoctoral researchers as part of the fourteenth annual National Postdoc Appreciation Week with seven profiles of postdocs from around the Laboratory.

A new discovery by PNNL researchers has illuminated a previously unknown key mechanism that could inform the development of new, more effective catalysts for abating NOx emissions from combustion-engines burning diesel or low carbon fuel.

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

Glenn Fugate was selected to serve on the American Chemical Society's Science Committee.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

DOE Basic Energy Sciences 2023 “Poetry of Science Art Contest” aims to educate and inspire; voting for the People’s Choice Award closes September 15.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

Regional wind data helps utilities save money, plan for windy days.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

A holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.

Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.

A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.

Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.

Four PNNL researchers received highly competitive DOE Early Career Research Program awards, providing five continuous years of funding for their projects.

A new open-source feature tracking package is now available to facilitate advanced model evaluation, model development efforts, and scientific discovery.

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.

Models show that in a warmer climate, the 2012 North American derecho would shrink and decay earlier in Mid-Atlantic coastal areas.

By adding rain, snow, and rain-on-snow precipitation data to a background model, a new scheme pinpoints local flood risks in order to improve the design of small-scale hydrological infrastructure.

Developed a data-assimilation method based on physics-informed deep learning to resolve downscaled flow fields within a large-scale river model.