The ability of a storm-resolving weather model to predict the growth of storms over central Argentina was evaluated with data from the Clouds, Aerosols, and Complex Terrain Interactions (CACTI) field campaign in central Argentina.

Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.

Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.

The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.

To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.

This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.

Department of Energy presented six teams from PNNL with the Secretary's Honor Award.

Over three days more than 200 federal, state, and tribal partners gathered to evaluate and walk through the Federal Emergency Management Agency (FEMA) Region 10 Cascadia Subduction Zone Earthquake and Tsunami Response Plan.

PNNL’s Russ Haffner plays a key role in helping areas get back to normal after an emergency event such as Hurricane Ida.