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.

CACTUS provides AI-powered cheminformatics for autonomous science.

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

PMedIC, the PNNL and OHSU joint research collaboration, contributes to student success and impact in biomedicine.

A compilation of soil viral genomes provides a comprehensive description of the soil virosphere, its potential to impact global biogeochemistry, and an open database for future investigations of soil viral ecology.

In the latest issue of the Domestic Preparedness Journal, Ashley Bradley and Kristin Omberg share how new research is shedding light on the scientific and technological challenges with detecting fentanyl.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

Data reveals mechanisms in our bodies that can help us fight viral pathogenesis.

New apps help researchers with statistical analyses of data.

PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.

Researchers devised a quantitative and predictive understanding of the cloud chemistry of biomass-burning organic gases helping increase the understanding of wildfires.

A new report highlights the results of an assessment PNNL conducted of field-portable detection products used by first responders to detect illicit substances like fentanyl in the field.

Spatial proteomics enables researchers to link protein measurements to features in the image of a tissue sample, which are lost using standard approaches.