A new study used artificial intelligence to analyze 10 years of weather data collected over southeastern Texas to identify three major categories of weather patterns and the continuum of conditions between them.
A new study used artificial intelligence to analyze 10 years of weather data collected over southeastern Texas to identify three major categories of weather patterns and the continuum of conditions between them. The study, just published in the Journal of Geophysics Research: Atmospheres, will help scientists seeking to understand how aerosols—tiny particles suspended in Earth’s atmosphere—affect the severity of thunderstorms.
Do these tiny particles—emitted in auto exhaust, pollution from refineries and factories, and in natural sources such as sea spray—make thunderstorms worse? It’s possible, said Michael Jensen, a meteorologist at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and a contributing author on the paper.
“Aerosols are intricately connected with clouds; they’re the particles around which water molecules condense to make clouds form and grow,” Jensen explained.
As principal investigator for the TRacking Aerosol Convection interactions ExpeRiment (TRACER)—a field campaign taking place in and around Houston, Texas, from October 2021 through September 2022—Jensen is guiding the collection and analysis of data that may answer this question. TRACER uses instruments supplied by DOE’s Atmospheric Radiation Measurement (ARM) user facility to gather measurements of aerosols, weather conditions, and a wide range of other variables.
Read more at: Brookhaven National Laboratory
Dié Wang, an assistant meteorologist at Brookhaven National Laboratory, is lead author of a paper looking back at 10 years of weather data over southeastern Texas to categorize conditions in a way that will help scientists tease out the effects of aerosols on storms. (Photo Credit: Brookhaven National Laboratory)
