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.

The Department of Energy’s Vehicle Technologies Office recently issued two awards to researchers at PNNL for their contributions to areas that are crucial for the expansion of electric vehicles.

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.

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.

Two researchers at PNNL have been named fellows of the American Chemical Society: Karthikeyan Ramasamy and Yuyan Shao.

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.

Chemical Engineer Yong Wang explains the influence and opportunity for joint appointments. Wang maintains one of the longest joint appointment tenures at PNNL.

Using a combination of satellite data and modeling to study the temperatures and humidity people might feel in urban areas, researchers have pinpointed who in the U.S. is most vulnerable to heat stress.

Quantifying the long-term destination of anthropogenic emissions and robustness of global carbon sinks.

New study identifies crucial factors for more accurate hydrological predictions in the Community Land Model version 5.

The complex interactions between the land and atmosphere can affect cloud growth.