Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

With future warming, storms in the Western U.S. will be larger and produce more intense precipitation, particularly near the storm center, and increase flood risks.

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

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.

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.

Report for the Oregon Public Utility Commission highlights innovations and best practices for resilience and utility planning could be helpful to other states as well.

Improving a snow albedo model to account for non-spherical snow grain shapes and the mixing of dust inside snowflakes in an Earth system model.

Building new climate outcome scenarios by stitching together pieces of existing ones.

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.

The flux of carbon dioxide from forest soil may acclimate to some aspects of climate change.

Identifying links between large-scale and local-scale model biases in tropical precipitation.

Interactive freshwater flux works to prolong density anomalies over deep water formation areas in the North Atlantic.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

Interactions between fronts and the land-sea breeze lead to strong vertical exchange in the northern Gulf of Mexico.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

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

When wildfires burn in the west, the heat and airborne particles they release inflict stronger rain and larger hail upon central states.

Evaluating the relative roles of soil water stress and vapor pressure deficit at limiting canopy conductance in coupled land-atmosphere simulations.