A new model accounts for co-occurring environmental parameters to improve predictions of a hurricane’s intensification.

Research indicates shifts in two climate oscillations drive spatial differences in Arctic atmospheric river trends over the past four decades.

In this study, researchers used fire intensity data from satellites to map the distribution of aerosols into the atmosphere during a fire.

A new study uses direct numerical simulations to develop a near-surface turbulence model for thermal convection using interpretable and physics-aware neural networks, broadening the applications of numerical simulations.

A PNNL study developed a water management module for Xanthos that distinguishes between the operational characteristics of hydropower, irrigation, and flood control reservoirs.

The Earth System Model Aerosol–Cloud Diagnostics package version 2 uses aircraft, ship, ground, and satellite measurements to evaluate detailed physical processes in aerosols, clouds, and aerosol–cloud interactions.

With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

New research shows how cloud shapes affect the process of cloud evolution, resulting in better understanding of how clouds behave, improving weather forecasts, and enhancing comprehension of climate systems.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

As electricity grids incorporate renewables, several factors will constrain future output, according to a new paper.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

Scientists recently developed a flow direction model which produces high-quality flow routing parameters on any mesh system.

PNNL-Sequim scientists will spend the next year testing a new technology that could allow the ocean to soak up more carbon dioxide without contributing to ocean acidification.

A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.

PNNL researchers developed the dummy payload to evaluate the performance of marine energy device prototypes in the Powering the Blue Economy: Ocean Observing Prize Competition.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

Across the United States, water moving between the river and riverbed sediments does not overcome localized processes that govern organic matter chemistry.