A yearlong effort in and around Baltimore, Maryland, reveals insights into atmospheric interactions across urban, rural, and coastal areas.

Researchers are using data from the 18-month Cloud And Precipitation Experiment at kennaook (CAPE-k) to paint a clearer picture of aerosol-cloud-precipitation interactions in a unique environment.

Learn how ARM’s newest atmospheric observatory grew in capabilities, scope, and data during its first year of operations in northern Alabama.

Value-added products providing horizontal wind profiles and vertical velocity statistics from Doppler lidar data are now available for the 2024–2025 Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) in the Baltimore, Maryland, area.

The Precipitation Best Estimate (PrecipBE) product provides time-series data and statistics on precipitation events measured at ground level by ARM.

This value-added product allows for examination of cloud-base and boundary-layer heights while making it easier to access and use measurements from ARM tethered balloon system missions.

Land-atmosphere interactions are crucial for the formation and evolution of low-level clouds and planetary boundary layer (PBL). However, characterizing the different coupling states between clouds and the surface is uncertain, particularly in continental regions due to complex thermodynamics. This study examines cloud-surface coupling at five ARM sites across three continents, highlighting both the complexities and commonality of cloud-land-surface interactions.

Cloud microphysics refers to the microscale processes that impact liquid droplets and ice crystals in clouds and precipitation. Because of uncertainty in both cloud microphysical processes and in how these processes should be represented in models, they are a major source of uncertainty in current weather and climate models. The recent application of machine learning (ML) methods to atmospheric model development holds significant promise to address current limitations in modeling cloud microphysics and thus improve atmospheric models. In this perspectives paper, we discuss both challenges and future opportunities in applying ML methods to cloud microphysics.

Downdrafts, or downward moving currents of air, form around the edges of thunderstorms. They can be an important feature of severe thunderstorms, particularly downbursts, and can be hazardous to aviation as well as drive strong surface winds. More generally, downdrafts are also important for the vertical transport of energy, dry air, and aerosols and chemical constituents in the atmosphere. However, uncertainty about the mechanisms driving downdrafts remains, and this study uses numerical modeling to address this knowledge gap.