Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.
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.
Performing closure studies using aerosol size, aerosol composition, and cloud condensation nuclei measurements of mixed aerosol from the Southern Great Plains region.
An evaluation of models and prediction tools for distributed wind turbines has unearthed data that can help potential users make the most informed decisions on upfront investments.
Secondary organic aerosol formation from monoterpenes is more strongly influenced by oxidant and monoterpene structure than by nitric oxides and hydroperoxy radical concentrations.
Repeated aircraft measurements over central Oklahoma allow researchers to better understand the spatial variability of aerosol properties that affect cloud evolution.
The Earth system model aerosol-cloud diagnostics package version 1 uses aircraft, ship, and surface measurements to evaluate simulated aerosols in an Earth system model.
PNNL wind energy experts have published the Distributed Wind Market Report: 2022 Edition, supplying key findings that can help businesses, communities, and homeowners make informed decisions.
Investigating cloud condensation nuclei activities in various airmasses enabled linking activity variations with organic oxidation levels and volatility