Researchers from PNNL and Parallel Works, Inc., applied machine learning methods to predict how much oxygen and nutrients are used by microorganisms in river sediments.
The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.
To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.
This study presents an automated method to detect and classify open- and closed-cell mesoscale cellular convection (MCC) using long-term ground-based radar observations.
In a new paper, researchers point to three major efforts where the biggest climate mitigation gains stand to be realized: ramping up carbon dioxide removal, reigning in non-carbon dioxide emissions and halting deforestation.
A new discovery by PNNL researchers has illuminated a previously unknown key mechanism that could inform the development of new, more effective catalysts for abating NOx emissions from combustion-engines burning diesel or low carbon fuel.
PNNL has developed seaweed-based inks and materials for 2-D and 3-D printing that can be used for a multitude of applications in the art, medical, STEM, and other fields.
A discovery from PNNL and Washington State University could help reduce the amount of expensive material needed to treat vehicle exhaust by making the most of every precious atom.