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.

Researchers are developing an understanding of how either wet or dry soil patches change the sizes of atmospheric eddies and cloud clusters.

The overlooked impact of regional hydroclimate changes on lake evaporation implies more robust regional lake volume changes around the globe.

Changes in the downward flow of infrared energy in clear skies are a key factor influencing recent rapid wintertime Arctic warming.

Ensembles of 20–25 members, notably smaller than traditional large ensembles, can accurately represent changes in extremes of temperature and precipitation.

Researchers achieved the complete assignment of the infrared spectrum of a hydronium-water “magic number” cluster reported in Science in 2004.

Researchers developed a new model to understand the initiation of summer-time mesoscale convective systems over the central United States.

Implementing and evaluating a new radiative transfer scheme that accounts for various topographic effects on solar radiation in an Earth system model.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

Better representing cloud microphysics can Reduce model bias in simulating mesoscale convective system precipitation.

New analysis demonstrates that the Madden-Julian Oscillation can influence tropical cyclones in the Pacific through both the atmosphere and the ocean.

PNNL researchers developed a way to produce accurate simulations of complex physical systems using renormalized approximations across multiple scales

Scientists created a benchmark dataset and explored new algorithms to better predict changes in the intensity of North Atlantic tropical cyclones.

Hypergraphs can more faithfully identify important genes in complex immune responses.

The web-based tool, called PLATIPUS, is publicly available and interacts with publications available in the CovidScholar database.

Seeking a deeper understanding of when, where, and why large wildfires occur using explainable artificial intelligence.

Researchers used deep learning methods to estimate the subsurface permeability of a watershed from stream discharge measurements.

Rigorous mathematical methods can help avoid unnecessary simplifications in numerical models of atmospheric clouds.

More accurate methods provide better results in numerical simulations of atmospheric clouds by avoiding accidental contradictions.