New research shows how cloud shapes affect the process of cloud evolution, resulting in better understanding of how clouds behave, improving weather forecasts, and enhancing comprehension of climate systems.
A team of scientists at PNNL developed new computational models to predict the behavior of these impurities and reduce the expense and risk related to actinide metal production.
PNNL has joined Gender Champions in Nuclear Policy, a leadership network that brings together leaders of organizations working in nuclear policy who are committed to breaking down gender barriers.
Resolving how nanoparticles come together is important for industry and environmental remediation. New work predicts nanoparticle aggregation behavior across a wide range of scales for the first time.
The roles of the various environmental variables in the transition from suppressed to active tropical precipitation regimes are characterized using statistical analysis and machine learning.
This study revealed that fresh organic vapors are soluble in particulate organics that are actively growing in size. However, if the particulate matter ages, fresh organic vapors can no longer mix with the organic matter.