Using a combination of satellite data and modeling to study the temperatures and humidity people might feel in urban areas, researchers have pinpointed who in the U.S. is most vulnerable to heat stress.

Quantifying the long-term destination of anthropogenic emissions and robustness of global carbon sinks.

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.

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.

Team brought experience in nuclear waste forms and regulatory policies to the Federally Funded Research and Development Center’s report, which was reviewed by a National Academies’ committee.

Existing clouds affect the growth of neighboring clouds.

Oluwayemi Garuba brings Arctic expertise to panel on climate modeling.

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.

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

Ann Lesperance has been invited to continue her role on the Domestic Preparedness advisory board, which convenes multidisciplinary subject matter experts to support the Domestic Preparedness Journal's editorial plan.