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.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

New research sheds light on the biomolecular dangers of night shift work.

PNNL contributes to a nationwide research consortium investigating the molecular mechanisms triggered by endurance training.

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

A streamlined informatics workflow that automatically processes complex data and provides the locations of double bonds in lipids.

Hi-C metagenomics-informed phage-host infection networks detected in soil pre- and post-drying.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

New research findings published in Science Advances (November 2022), help explain the progression of Alzheimer-related dementia in each patient. The findings outline a biological classification system that predicts disease severity.

PNNL chemist Ying Zhu was honored as one of the top 40 researchers under the age of 40 in analytical science by The Analytical Scientist.

Data from increasingly complex mass spectrometry instruments met with adaptable data analysis solution.

The nested nanoPOTS chip is the next generation of technology developed at PNNL to prepare single cells for proteomics.

The Washington State Academy of Sciences consists of more than 300 elected members who are nationally recognized for their scientific and technical expertise.

Kristin Burnum-Johnson presented at two virtual events in June on proteome mapping.