rmap enables users with limited to no geographic information system knowledge use R to visualize simple tabular data.

The methods researchers use to estimate crop yield gaps determines the location of projected future agricultural “breadbasket” regions.

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

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.

Understanding why the westerly jets shift poleward in certain seasons and regions but equatorward in others under global warming.

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

Secondary organic aerosol formation from monoterpenes is more strongly influenced by oxidant and monoterpene structure than by nitric oxides and hydroperoxy radical concentrations.

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

Two case studies of clouds evolving over the Arctic provide data that will help researchers evaluate cloud simulations.

Evaluating the accuracy of predicted plant aboveground biomass with different numerical solution options in a model of water transport in plants.

How hailstorms respond to human-created warming in the Great Plains depends on large-scale weather-producing systems.

The presence of mMarine microorganisms may enhance cloud droplet formation, leading to brighter clouds that reflect more sunlight.

Better representing electric capacity markets, economic retirements, and power-plant age structure provides a more robust understanding of the future evolution of the electric sector.

Newly developed deep learning models can estimate 3-D permeability fields from time-lapse geophysical electrical imaging.

Repeated aircraft measurements over central Oklahoma allow researchers to better understand the spatial variability of aerosol properties that affect cloud evolution.

Observational evidence shows that clouds strongly influence the seasonal heat exchange between Southern Ocean air and sea.

Developing a new method to identify the fraction of green vegetation from satellite data at high spatial resolution.

Both the type and composition of landforms affect atmospheric properties and clouds in a state-of-the-art Earth system model.