Classification of weather to unravel how aerosols

image: Dié Wang, an assistant meteorologist at Brookhaven National Laboratory, is lead author on a paper looking back at 10 years of weather data from southeast Texas to categorize conditions in a way that will help scientists tease out the effects of aerosols on storms.
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Credit: Brookhaven National Laboratory

A new study used artificial intelligence to analyze 10 years of weather data collected in southeast Texas to identify three broad categories of weather patterns and the continuum of conditions between them. The study, which has just been published in the Journal of Geophysics Research: Atmosphereswill help scientists seeking to understand how aerosols— tiny particles suspended in the Earth’s atmosphere — affect the severity of thunderstorms.

Do these tiny particles – emitted in car 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 US Department of Energy’s (DOE) Brookhaven National Laboratory and contributing author of the paper.

“Aerosols are intimately related to clouds; these are the particles around which water molecules condense to form and grow clouds,” Jensen explained.

As principal investigator for the Aerosol convection interaction tracking experiment (TRACER) – a field campaign taking place in and around Houston, Texas from October 2021 to September 2022 – Jensen is guiding the collection and analysis of data that could answer this question. TRACER uses instruments supplied by the DOE Measurement of atmospheric radiation (ARM) user facility to collect measurements of aerosols, weather, and a wide range of other variables.

“During TRACER, we seek to determine the influence of aerosols on thunderstorms. However, these influences are closely related to those of large-scale weather systems (think high or low pressure systems) and local conditions,” Jensen said.

To unravel the effects of aerosols, scientists must unravel these influences.

Dié Wang, assistant meteorologist at Brookhaven Lab and lead author of the paper looking back at 10 years of data before TRACER, explained the approach to achieving this.

“In this study, we used a machine learning approach to determine the prevailing summer weather conditions in the Houston area,” she explained. “We will use this information in our TRACER analysis and modeling studies by comparing the characteristics of storms that occur under similar weather conditions but varying aerosol conditions.”

“This will help us minimize differences due to large-scale weather patterns, to help isolate the effects of aerosols,” she said.

The project is the first step towards achieving the objectives supported by Early DOE career funding awarded to Wang in 2021.

Bringing students on board

The study also allowed several students involved in virtual internships at Brookhaven Lab to contribute to the research. Four co-authors participated in the DOE’s Undergraduate Science Lab Internship (SULI), and one was an intern under the Brookhaven program Secondary research program (HSRP).

Each intern studied the variability of different cloud and precipitation properties among weather categories using datasets from radar, satellites and surface weather measurement networks.

“This work was well suited to the virtual internship because it relied heavily on computer data analysis and visualization,” Jensen said. “Interns gained valuable experience in computer programming, real-world scientific data analysis, and the complexities of Earth’s atmospheric system.”

Dominic Taylor, a SULI intern from Pennsylvania State University, wrote about his experience for a ARM-Blog:

“In the beginning, I faced many challenges…with my computer able to handle the size and number of data files I was using…Dié, Mike and my fellow interns were always there when I needed help,” he said.

“Given my passion for meteorology, I was thrilled to get this job in the first place, but writing code and probably spending way too much time formatting plots didn’t feel like work because I found the subject so fascinating,” he added.

In the same blog post, Amanda Rakotoarivony, an HSRP intern from Longwood High School, said, “This internship allowed me to really connect the topics I learned in school with real-world research that is in progress….[and] showed me how research and collaboration are at the heart of interdisciplinarity.

Data details

Southeast Texas summer weather is largely influenced by sea breeze and bay circulations from the Gulf of Mexico and nearby Galveston Bay. These circulations, in conjunction with those of larger-scale weather systems, affect the flux of moisture and aerosol particles in the Houston area and impact the development of thunderstorms and their associated precipitation. Understanding how these flows affect clouds and storms is important for improving models used for weather forecasting and climate prediction. Categorizing patterns can help scientists assess the effects of other influences, including aerosols.

To characterize the weather patterns, the scientists used a form of artificial intelligence to analyze 10 years of data that combines climate model results with weather observations.

“The combined data yields a comprehensive, long-term description of three-dimensional atmospheric properties, including pressure, temperature, humidity and winds,” Wang said.

The scientists used a machine-learning program known as the “Self-Organizing Map” to sort this data into three dominant categories, or regimes, of weather patterns with a continuum of transition states between them. Overlaying additional satellite, radar, and surface observations on these maps allowed scientists to study the characteristics of cloud and precipitation properties in these different regimes.

“The weather patterns we have identified bring together complex information about the prevailing large-scale weather patterns, including the factors important for the formation and development of storms. By examining how the properties of thunderclouds and precipitation vary under different aerosol conditions but similar weather patterns, we are able to better isolate the effects of aerosols,” Wang said.

The team will use high-resolution weather modeling to integrate additional local-scale weather measurements – for example, sea breeze circulation – and detailed information on the number, size and composition of aerosol particles. .

“This approach should allow us to pinpoint exactly how aerosols affect clouds and storms, and even disentangle the different effects of industrial and natural aerosol sources,” Wang said.

Brookhaven Lab’s role in this work and the TRACER and SULI internships are funded by the DOE Office of Science (BER, WDTS). The HSRP program is supported by Brookhaven Science Associates, the organization that operates Brookhaven Lab on behalf of the DOE.

Brookhaven National Laboratory is supported by the U.S. Department of Energy’s Office of Science. The Office of Science is the largest supporter of basic physical science research in the United States and works to address some of the most pressing challenges of our time. For more information, visit science.energy.gov.

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