New study identifies crucial factors for more accurate hydrological predictions in the Community Land Model version 5.

Groundwater modeling of irrigation return flow dynamics is important for improving estimates of water availability.

PNNL scientists investigate the promising properties of a common, Earth-abundant salt.

The Great Plains and Great Lakes regions are the major sources of moisture for the precipitation that falls in the Great Lakes region.

A team of researchers at PNNL designed and constructed an atmospheric chamber to simulate the fate of volatile and semivolatile radioisotopes.

A computationally efficient calibration procedure helps constrain simulated runoff in an Earth system model.

The complex interactions between the land and atmosphere can affect cloud growth.

Existing clouds affect the growth of neighboring clouds.

Oluwayemi Garuba brings Arctic expertise to panel on climate modeling.

By mimicking nature, scientists make a breakthrough in breaking down wood.

Yun Qian has been selected as an editor of the Journal of Geophysical Research: Atmospheres, a prominent atmospheric science journal.

PNNL researchers explore the feasibility of using low-flying drones to survey sites for low levels of radiation.

Assessing observed weather conditions that support or suppress the growth of clouds into deep precipitating storms during the Cloud, Aerosol, and Complex Terrain Interactions experiment.

Multiple concurrent single-particle measurements help develop a quantitative and predictive understanding of secondary organic aerosols.

Applying a novel framework to a climate model reveals new characteristics about how elevated concentrations of aerosols affect clouds.

A review describes the latest techniques for simulating hydropower at large scales to inform power grid planning and operations.

Hydropower generation estimated using water proxy data leads to a revision of the nation’s historical seasonal hydropower output.

PNNL researchers teamed up with marine energy developers to test a new wave energy conversion device through the TEAMER program.

As the world races to discover solutions for reaching net zero carbon emissions, a PNNL analysis quantifies the economic value of the existing nuclear power fleet and its carbon-free energy contributions.

A deep learning model overcomes persistent challenges in emulating long-term simulations of secondary organic aerosols.