Research Highlights

Radiosondes are considered the gold standard for measuring profiles of temperature, humidity, and wind, but their temporal resolution is often too coarse for many applications. This study proposes a new method using a normalized height grid in the temporal interpolation process that yields more accurate profiles in the convective boundary layer, i.e., the turbulent atmospheric layer between the surface and the free troposphere.

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility has launched a new multi-year observational site in the Bankhead National Forest (BNF) in northern Alabama, a region characterized by abundant biogenic aerosol, frequent convection, and strong land-atmosphere coupling. This site has an extensive suite of advanced instruments—including ground-based aerosol sensors, atmospheric profilers, scanning cloud and precipitation radars, elevated tower measurements, and aerial platforms—designed to capture observations from the forest canopy up through the clouds. These detailed observations will reveal how the forest influences the aerosol population, the land surface regulates the transition from shallow-to-deep convection, and vegetation controls energy and turbulence within the boundary layer.

New particle formation (NPF) is a major source of atmospheric nanoparticles that affect aerosol populations, air quality, human health, and the atmosphere. The complex and nonlinear interactions among radiation, gases, and meteorology make it difficult to pinpoint what conditions trigger events that form new particles. In this study, researchers applied a machine learning technique (random forest) to long-term atmospheric measurements in a rural continental environment to classify NPF and non‑NPF days and to identify which environmental factors matter most. The approach captures the intricate relationships that traditional methods often miss and provides a quantitative ranking of the controlling variables.