Highlights will be written for high-level accomplishments and published journal articles of ASR research. Each ASR principal investigator (PI) is expected to submit at least one highlight per fiscal year.
Research Highlights
Recent Highlights
Coming together! A 4D observational data set of atmospheric boundary-layer properties in Houston
24 July 2024
Lamer, Katia; Mages, Zackary
Supported by:
Research area: Atmospheric Thermodynamics and Vertical Structures
Field data from eight teams were brought together, standardized, and enhanced to facilitate research into Houston’s complex atmospheric boundary layer (ABL).
Tracking precipitation features and associated large-scale environments over southeastern Texas
24 July 2024
Fast, Jerome D
Supported by:
Research area: Cloud-Aerosol-Precipitation Interactions
Deep convection is a major contributor to annual total precipitation and a source of very high-intensity rainfall over coastal Texas. Understanding the initiation and development of deep convection, including isolated deep convection (IDC) and mesoscale convective systems (MCSs), is crucial due to their significant impact on regional weather patterns and [...]
AI teaches itself to identify clouds
22 July 2024
Fast, Jerome D
Supported by:
Research area: Cloud Distributions/Characterizations
Clouds affect the Earth’s weather and climate by influencing light, heat, and moisture in the atmosphere. They come in many varieties with unique impacts on the atmosphere. Researchers strive to understand these nuances to accurately model weather and climate. Artificial intelligence (AI) can help study clouds by analyzing satellite images, [...]
Small aerosols help decoupled cloud layers resist precipitation depletion over the North Atlantic
12 July 2024
Blossey, Peter; Wood, Robert
Supported by:
Research area: Cloud-Aerosol-Precipitation Interactions
Low-lying marine clouds reflect more sunlight back to space compared to the dark, underlying ocean surface, helping to cool the earth’s climate. Transitions of these clouds from overcast to broken conditions modify cloud cover and the resulting cooling. Computer simulations of a case study during the U.S. Department of Energy [...]
Reduced-order modeling for linearized representations of microphysical process rates
9 July 2024
Lamb, Kara Diane
Supported by:
Research area: General Circulation and Single Column Models/Parameterizations
Cloud processes present a major challenge to our ability to model future climate. Here we explore how we can use new, data-driven approaches to determine optimal representations for cloud processes in climate models.