Brian O’Neill joins a Roundtable on climate security organized by the National Academies of Sciences, Engineering, and Medicine.

An analysis of land use in watersheds that supply drinking water to over a hundred United States cities identified a wide range of exposure to potential contamination.

PNNL experts perform fundamental climate science work at multiple scales to understand impacts on energy, water, food, and infrastructure systems.

PNNL researchers have uncovered a plant-derived process that leads to the formation of aerosol particles over the Amazon rainforest and potentially other forested parts of the world. 

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.

COVID-19 changed when and where we use electricity. Researchers showed that commonly available total demand data masked the actual changes.

Storage Targets and Release Function Inference Tool uses data to infer the operating policies of large reservoirs on a national scale.

A multisectoral analysis that combines land use data with hydrological simulations reveals wide disparities in potential source water contamination.

PNNL scientist Brian O’Neill led the compilation of an IPCC chapter about climate-related risks.

COVID-19 had multiple impacts on the electric sector and the nature of those impacts varied on the scale analyzed.

To improve the study of human-Earth interactions, a 10-year vision report by the MultiSector Dynamics community of practice encourages the use of emerging human systems datasets, embedded intelligence in modeling, and workforce diversity.

Using a novel settlement-related dataset produced more accurate population downscaling than using other, more traditional datasets.

Despite an increase in future electricity demands, virtual water trading in the U.S. electricity sector is expected to decline as renewable energy expands.

PNNL researchers contributed to the International Conference on Data Science and Advanced Analytics 2021 Best Paper Award-winning publication.

DOE’s continued support will allow CENATE researchers to advance computing through the evaluation of novel technologies.

PNNL computer scientist Ang Li serves as one of the principal investigators for a project selected to receive DOE funding.

Researchers to design their own computer accelerators for faster analysis of large datasets.

PNNL computer scientists joined international leaders in machine learning to present research to detect and address potential cybersecurity threats and devise epidemic interventions.

Sriram Krishnamoorthy, a computer scientist at PNNL, collaborated with a University of Utah team on a student computing research project that won Best Student Paper at SC20.

James A. Ang, a PNNL computing expert, was recently invited to moderate a panel in a virtual workshop focused on federally funded research and development on software for heterogeneous computing.