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.

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.

A new study led by PNNL researcher TC Chakraborty provides some answers about how irrigated agricultural land near cities affects urban heat stress.

Researchers developed a robust, cost-effective, and easy-to-use cap-based technique for spatial proteome mapping, addressing the lack of accessible proteomics technologies for studying tissue heterogeneity and microenvironments.

New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.

Knowing future snow levels is key for managing the recovery of endangered bighorn sheep. PNNL scientists help to forecast how the sheep could fare.

A new study reveals just how costly Earth's tipping points can be.

The demand for energy is growing—and so is the technology supporting it. However, future development of power generation technologies could be affected by a key factor: material supply.

Mengqi Zhao and Chris Vernon were selected to serve in editorial roles at JOSS for Earth science and data science, respectively.

The Delaware River Basin is projected to experience a regional increase in flood severity under future climate scenarios.

New findings document fewer ship tracks, reduced cloud cover, and boosted warming after ship emissions regulations took effect in 2020.

Ljiljana Paša-Tolić, a PNNL fellow, is the recipient of the 2024 International Mass Spectrometry Foundation’s Jochen Franzen Award. 

Aerosol particles imbue climate models with uncertainty. New work by PNNL researchers reveals where in the world and under what conditions new particles are born.

Accessing groundwater may become more difficult—and more expensive—as groundwater supplies become increasingly scarce and underground aquifer levels fall.