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.

PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.

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.

With the launch of a large research barge, PNNL and collaborators took another significant step to improve offshore wind forecasting that will lower risk and cost associated with offshore wind energy development.

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.

The nation is closer to its offshore wind energy goals than ever before, but better wind forecasting is still needed. To address this challenge, PNNL and collaborators are charting a new course with help from novel technology.

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.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

Floating offshore wind farms could potentially triple the Pacific Northwest's wind power capacity while offsetting billions of dollars in costs for utilities, ratepayers, insurance companies, and others.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

Regional wind data helps utilities save money, plan for windy days.

Recovery Simulator and Analysis tool further strengthens PNNL’s short-term resilience planning efforts for Puerto Rico.

PNNL is making waves in pursuit of a blue economy. Researchers are advancing science and technology to expand—and protect—the gifts of the sea.

PNNL researchers developed a new model to help power system operators and planners better evaluate how grid-forming, inverter-based resources could affect the system stability.

Recognizing how innovation and clean technologies at the very edge of the grid can work together to transition the electricity system, PNNL takes a multidisciplinary approach to advancing and integrating renewable energy solutions.