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.

PNNL’s Nick Ward has been honored with the American Geophysical Union’s Thomas Hilker Early Career Award.

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

Neutrino mass, a crucial piece of many unresolved physics puzzles, may one day be revealed through a novel measurement system that has just proven its mettle: Cyclotron Radiation Emission Spectroscopy.

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

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

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

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

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

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.

This study demonstrated that a large-scale flooding experiment in coastal Maryland, USA, aiming to understand how freshwater and saltwater floods may alter soil biogeochemical cycles and vegetation in a deciduous coastal forest.

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.

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 Great Plains and Great Lakes regions are the major sources of moisture for the precipitation that falls in the Great Lakes region.

A computationally efficient calibration procedure helps constrain simulated runoff in an Earth system model.