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.

Seaweed could be a new source of critical minerals for electronics, vehicles, and buildings.

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.

This research helps predict how coastal forests will respond to future sea level changes which is crucial for protecting these ecosystems.

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.

The Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field, Measurements, and Experiments project established a network of observational field sites across Chesapeake Bay and western Lake Erie.

Due to their inherent variability and complexity over space and time, scientists are challenged to understand the complex interactions among soil, vegetation, and water along coastal terrestrial-aquatic interfaces.

This study characterized above- and below-ground properties to explore the spatial heterogeneity of the terrestrial aquatic interface ecosystem within the Chesapeake Bay area and evaluate the major drivers of soil respiration.

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.

Over the next four years, PNNL and University of Arizona will develop open-source computational tools to better identify and characterize the viruses associated with the human microbiome.

Armed with some of the world’s most advanced instrumentation, researchers at PNNL are working to analyze huge amounts of data and uncover hidden biological connections.

A potential quantum advantage for fluid dynamics simulations from molecular to atmospheric scales with a new formula for success.

CACTUS provides AI-powered cheminformatics for autonomous science.

Scientists are using artificial intelligence and powerful computing to sculpt new molecules in an effort to treat disease.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

This study evaluated the sensitivity of multiple geophysical methods to measure and evaluate the spatiotemporal variability of select soil properties across terrestrial–aquatic interfaces.