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.

O’Neil met with members of parliament, the shadow parliament, and the UK’s national security organizations. The entire board, along with 100 invited cybersecurity professionals, attended a special event at the House of Commons.

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

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.

Existing clouds affect the growth of neighboring clouds.

Oluwayemi Garuba brings Arctic expertise to panel on climate modeling.

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

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

Researchers have knitted together three large databases of information to help cybersecurity experts detect and prevent attacks.

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.

A new model uses machine learning to project electricity loads across the United States.

Watershed hydrological responses are sensitive to the spatial and temporal resolution of the gridded meteorological forcing used in simulations.

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