Researchers found that using deep neural networks can significantly enhance the resolution of observations from weather radars.

National Nuclear Security Administration Graduate Fellow Marc Wonders has spent the past year working with researchers exploring artificial intelligence in the national security mission space.

Principles derived from coastal wetlands to describe wetland channel cross-sections were applicable to the Columbia River estuary, but not the tidal river.

Field campaign data shows that biologically-derived emissions largely control the formation of organic aerosol in the Southern Great Plains.

Researchers found that warmer local sea surfaces increase the winter snowpack in the Sierra Nevada mountains, but reduce snowpack in the Cascade range.

Eleven scientists with PNNL affiliations named as top climate scientists on a Reuters list.

Researchers developed a novel database of global mesoscale convective systems to reveal storm characteristics and rainfall contributions.

PNNL Earth scientist appointed to a committee assembled by the National Academies of Sciences, Engineering, and Medicine.

A webapp developed by PNNL in collaboration with the University of Washington to help drive efficiencies for urban delivery drivers is now in the prototype stage and ready for testing.

Researchers used deep learning methods to estimate the subsurface permeability of a watershed from stream discharge measurements.

Adding an advanced atmospheric aerosol treatment to a climate model enables assessments of the radiative effects of nitrate aerosols.

Researchers attained a new understanding of how and why the Great Plains low-level jet changes under global warming has implications for future weather in the Central U.S.

The majority of California wildfires from 1984-2017 occurred on hot-dry days, with meteorological conditions contributing to their sizes and changes.

A recently developed laboratory-based technique allows researchers to quantify aerosol absorption from field campaign samples.

Understanding the fundamental relationships between river flow, water exchange, and nutrient cycling can inform ecosystem effects of river regulation.

Researchers developed a strategy for quantifying the numerical errors in global simulations of atmospheric clouds and attributing them to components in the computational model.

Scientists at PNNL and the Confederated Tribes of the Colville Reservation identify acres of spawning habitat in the Upper Columbia River.

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

By improving the detail in representations of turbulence, researchers can more accurately simulate agricultural soil emissions in the atmosphere.

PNNL researchers are using explainable artificial intelligence to develop new ways to understand and interpret data for national security.