Classifying weather to tease out how aerosols influence storms
Machine learning study tracks large-scale weather patterns, providing
baseline categories for disentangling how aerosol particles affect storm severity

Date:
March 21, 2022
Source:
DOE/Brookhaven National Laboratory
Summary:
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 will help scientists seeking to understand
how aerosols -- tiny particles suspended in Earth's atmosphere --
affect the severity of thunderstorms.



FULL STORY ==========================================================================
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.

"During TRACER, we are aiming to determine the influence of aerosols
on storms.

However, those influences are intertwined with those of the large-scale
weather systems (think of high- or low-pressure systems) and local
conditions," Jensen said.

To tease out the effects of aerosols, the scientists have to disentangle
those influences.



==========================================================================
Die' Wang, an assistant meteorologist at Brookhaven Lab and lead author
of the paper looking back at 10 years of data prior to TRACER, explained
the approach for doing just that.

"In this study, we used a machine learning approach to determine the
dominant summertime weather condition states in the Houston region,"
she explained. "We will use this information in our TRACER analysis and modeling studies by comparing storm characteristics that occur during
similar weather states but varying aerosol conditions." "That will help
us to minimize the differences that are due to the large-scale weather conditions, to help isolate the effects of the aerosols," she said.

The project is the first step toward fulfilling the goals supported by
DOE Early Career funding awarded to Wang in 2021.

Bringing students on board The study also provided an opportunity for
several students involved in virtual internships at Brookhaven Lab to contribute to the research. Four co-authors were participants in DOE's
Science Undergraduate Laboratory Internship (SULI) program, and one was interning as part of Brookhaven's High School Research Program (HSRP).



==========================================================================
Each intern investigated the variability of different cloud and
precipitation properties among the weather categories using datasets
from radar, satellite, and surface meteorology measurement networks.

"This work was well suited to the virtual internship as it was largely
driven by computational data analysis and visualization," Jensen
said. "The 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 an ARM blog: "At first,
I faced a lot of challenges...with my computer being able to handle the
size and number of data files I was using....Die', Mike, and my fellow
interns were always there when I needed help," he said.

"Given my passion for meteorology, I was psyched to have this position
in the first place, but writing code and spending probably way too long formatting plots didn't feel like work because I found the topic so fascinating," he added.

In the same blog post, Amanda Rakotoarivony, an HSRP intern from
Longwood High School, said, "this internship allowed me to truly
connect the topics I've learned in school to the real-world research
that's being done....[and] showed me how research and collaboration is interdisciplinary at the core." Details of the data The southeastern
Texas summer weather is largely driven by sea- and bay-breeze circulations
from the nearby Gulf of Mexico and Galveston Bay. These circulations,
in conjunction with those from larger-scale weather systems, affect the
flow of moisture and aerosol particles into the Houston region and impact
the development of thunderstorms and their associated rainfall.

Understanding how these flows affect clouds and storms is important to improving models used for weather forecasts and climate predictions.

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 meteorological observations.

"The combined data produces a complete, long-term description of three- dimensional atmospheric properties including pressure, temperature,
humidity, and winds," said Wang.

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

"The weather regimes we identified pull together complex information
about the dominant large-scale weather patterns, including factors
important for the formation and development of storms. By looking at
how the storm cloud and precipitation properties vary under different
aerosol conditions but similar weather regimes, we are able to better
isolate the effects of the aerosols," Wang said.

The team will use high-resolution weather modeling to incorporate
additional local-scale meteorology measurements -- for example, the
sea-breeze circulation -- and detailed information about the number,
sizes, and composition of aerosol particles.

"This approach should allow us to determine exactly how aerosols are
affecting the clouds and storms -- and even tease out the differing
effects of industrial and natural sources of aerosols," Wang said.

Brookhaven Lab's role in this work and 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
manages Brookhaven Lab of behalf of DOE.


========================================================================== Story Source: Materials provided by
DOE/Brookhaven_National_Laboratory. Original written by Karen McNulty
Walsh. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Die' Wang, Michael P. Jensen, Domenic Taylor, Grace Kowalski, Marcie
Hogan, Brian M. Wittemann, Amanda Rakotoarivony, Scott
E. Giangrande, J.

Minnie Park. Linking Synoptic Patterns to Cloud Properties and
Local Circulations Over Southeastern Texas. Journal of Geophysical
Research: Atmospheres, 2022; 127 (5) DOI: 10.1029/2021JD035920 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220321115853.htm

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