New research from PNNL results in faster and more accurate variational quantum algorithm training.

PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.

PNNL researchers created a model that helps materials scientists not only predict what is, but what could be.

Researchers at PNNL are developing a better model of the soil, better representing the atmosphere, and identifying sources of record high rainfall within a model of the Earth system.

An improved model captures the interactions between the U.S. economy, energy, water, and land systems with increased spatial and temporal detail.

Recalibrating both cloud and subgrid representations improves model projections of the base climate, aerosol-cloud interactions, and cloud feedbacks.

A climate change study co-authored by PNNL scientist Hailong Wang inspired an artist to create an exhibit based on the research.

A newly developed tool helps researchers analyze process-level relationships within atmospheric models.

A global warming-induced poleward shift of the westerly jet affects the hydroclimate of the U.S. Midwest differently in the spring and summer.

The COVID-19 pandemic massively disrupted human activities, which caused additional turmoil through regional record rainfall and floods.

Researchers use machine learning to speed up the analysis of aerosol datasets from mass spectrometry.

The climate models best at simulating current precipitation and its drivers show more consensus in projecting future precipitation.

Examining the vast scientific literature about the urban impact on weather and climate enabled researchers to synthesize current understanding and to inform future work.

Quantum simulators help navigate noise on GPU-based HPC systems.

State-of-the-art numerical models can better simulate the convective clouds that produce most of the Amazon’s wet season rainfall.

Researchers used ground-based and remote-sensed datasets to comprehensively validate a tool for simulating land surface processes.

PNNL researchers utilized the Peeters-Devreese-Soldatov formulation to improve the accuracy of calculations for quantum chemistry.

A new hexagonal framework helps more accurately model watersheds at a range of scales.

Study reveals importance of accounting for micrometer scale distribution of substrates to predict carbon emissions from soil.

Developing a model for global soil erosion and sediment flux to advance understanding of land, river, and ocean geomorphology and biogeochemistry.