Data scientist at PNNL receives the Environmental and Engineering Geophysical Society and Geonics Limited Early Career Award for work with geophysical modeling and subsurface inversion codes.
This study demonstrates a new model that integrates complex organic matter (OM) chemistry and multiple electron acceptors to predict kinetic rates of OM oxidation.
Study demonstrates that choosing more accurate numerical process coupling helps improve simulation of dust aerosol life cycle in a global climate model.
Researchers seek to bring down costs, address potential environmental risks and maximize the benefits of harnessing wind energy above the deep waters of the Pacific.
Researchers show that small-scale turbulent fluctuations lead to larger concentrations of cloud droplets than would be possible in conventional models of atmospheric clouds
PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.
There are many ways that researchers at PNNL bring unique perspectives to the field of distributed wind. One is the fact that PNNL's distributed wind projects are all led by women.
Researchers seeking to enhance a climate model’s predictive capability identify parameters that cause the largest sensitivities for several important cloud-related fidelity metrics.