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 have developed the first coastal transport model for microplastics to understand how particles move in waterways and coastal currents.

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.

PNNL's accomplishments in FY 2024 help address the remaining challenges the wind industry faces.

PNNL had a significant presence at October’s North American Wind Energy Academy/WindTech 2023 Conference in Denver, Colorado. Thirteen PNNL wind experts participated in various capacities.

A research buoy managed by PNNL has been deployed in Hawai’ian waters, collecting oceanographic and meteorological measurements off the coast of O’ahu.

Multidisciplinary teams from research and industry are brought together using management systems developed by a team of software engineers at PNNL.

PNNL is lending analytics expertise to a new first-of-its-kind electric-vehicle carsharing program being launched in Hood River, Oregon.

PNNL’s Data Archive and Portal that stores and disseminates wind energy data recently reached a major milestone: 1,000 users and growing.

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.

Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.

PNNL deployed two research buoys in waters off the West Coast for the first time in deep water, supporting a DOE and Bureau of Ocean Energy Management effort to gather measurements that support offshore wind locations and technologies.

PNNL scientists are learning how to harness wind to secure a brighter, cleaner energy future.

PNNL is managing the Data Archive and Portal, which provides the wind research community with secure, timely, easy, and open access to all data brought in from research under DOE’s Atmosphere to Electrons program.

Researchers at PNNL are contributing artificial intelligence, machine learning, and app development expertise to a U of W project that will ease challenges with urban freight delivery. The project will provide delivery drivers with a tool