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.

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

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

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.

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

Researchers designed a new, highly efficient, stable redox-active material from neutral red dye that can efficiently capture CO2 from air.

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 autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

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

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

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

The size of the alkali metal within the material influences the crystallization pathway.

Surface characterization allows researchers to explore the compatibility between different solvent-membrane combinations.