Atmospheric System Research

Kara Sulia is turning early meteorological dreams into the reality of a new database for ice-crystal shapes

In the two-decade period 2000 to 2020, eight atmospheric science field campaigns―many of them funded by the U.S. Department of Energy (DOE)―used specialty cameras mounted on the underside of research aircraft to sweep up over 8.6 million images of ice crystals and cloud water droplets.
There were many more such campaigns and images, but this specific collection of data is at the heart of a long-term project underway at the University at Albany – State University of New York. The project, which just started its cycle of funding in October 2020 through DOE’s Atmospheric System Research (ASR) program , employs machine learning to characterize and categorize ice particles. Data are collected from DOE field campaigns.
The ASR work is led by research associate Kara Sulia, an ice-crystal growth theorist at Albany’s Atmospheric Sciences Research Center. Her ice-particle shape project has a suitably aerial acronym, COCPIT, which stands for Classification of Cloud Particle Imagery and Thermodynamics.

Good and Bad Habits

The automated instrument used to collect such images, some of them startlingly detailed and vivid, is the cloud particle imager (CPI), a workhorse device in many atmospheric research aircraft. The bullet-shaped CPI, mounted beneath the aircraft, has a tube-shaped sampler where particles passing through are snapped at 75 frames a second, with each frame capturing about 25 particle images. (Upgraded CPIs are now capable of 500 frames per second.)
Weighing 36 pounds and 26 inches long, both the old and new versions of the CPI “are giant compared to other pylon probes,” says Pacific Northwest National Laboratory atmospheric scientist Fan Mei, who is the CPI instrument mentor for the DOE’s  Atmospheric Radiation Measurement (ARM) user facility. “It captures (images of) lovely large droplets and crystals.”
Linked to a 50-pound data system inside the research aircraft, CPI instruments gather pictures of ice crystals, cloud water droplets, fragments, blank images, and blurry images. For purposes of Sulia’s ice-targeted work, specialized software filters out everything but the images of ice crystals. Because the data on fragments might be useful someday, they are saved.
“Our focus right now is on clear, pristine images,” says Sulia, who estimates a winnowed working database to be about 800,000 pictures. Her team is sorting them by shape with machine learning algorithms.
Key to the effort is Albany PhD candidate Vanessa Przybylo. Most of her graduate work has been funded through ASR projects.

Fetch, Filter, Analyze

The intent of COCPIT is to develop a cohesive framework for what Sulia calls “a more seamless interaction” with CPI images. They each measure 2.3 microns across, but in a raw-data state, are compressed about 1,000 times.
At the moment, there is no user-friendly, open-source tool for using CPI data. There is also no current way researchers can get a sense of predominant crystal types in a given cloud region or any detailed properties COCPIT would provide.
Sulia’s vision is to categorize ice-crystal images by shape, then create linked metadata that records contextual information. This would include not only information about the particles themselves but also the environment in which they were captured (temperature and altitude, for instance) and the field campaign from which the images originated.
All this will enable researchers to derive the microphysical details of specific case studies during DOE campaigns. Through COCPIT, says Sulia, they can “easily fetch, filter, and analyze these data, and perhaps get a better handle on the system they are investigating.”
The eight campaigns Sulia is tapping for data are or were fully or partly funded by the DOE―in some cases by ARM, the user facility.
One example is the 2008 Indirect and Semi-Direct Aerosol Campaign (ISDAC), which involved ground-based and airborne instrument platforms at ARM’s North Slope of Alaska atmospheric observatory.
To make sense of such data, Sulia’s team had to write the code to filter out liquid drops, establish a software interface to process the raw data, and chop it into sheets of images.
“That was months and months and months of tedious script-writing,” says Sulia.

An Iterative Process

Sulia calls COCPIT’s machine learning-aided work “an iterative process. Over time we can recognize when the model has difficulty appropriately categorizing a crystal type. As more data is fed into the model, we adjust categories and crystal types.”
After initial training by machine learning, she adds, “millions of images can be processed.”
COCPIT is informed by some urgent needs.
For one, millions of images are captured by CPI devices during field campaigns. But that’s too much data for one person to analyze for type, properties, and growth environment―the kind of information needed to visualize the evolution of a particular cloud system.
Categorizing cloud-particle images can help profile the systems that created them. That’s a boon to numerical weather models, which use data about cloud-particle type and ice-crystal shape to accurize their simulations.
Radiative transfer in cloud systems also depends on predicted habits (shapes). So does calculating the mass of precipitation. Missing the difference between a 420-micrometer sphere and a 5- millimeter stellar dendrite, can lead to particle-mass estimates being off by a factor of 15. Meanwhile, correct estimates of particle type improve climate models because thermodynamic feedbacks are linked to particle shape.
Current ice-habit studies often rely on crystal-classification programs that fall short, says Sulia. COCPIT can help by assembling such images in a coherent database, with classifications “done in a systematic and repeatable way.”
Validation accuracies using COCPIT are up around 99%―10 or 15 points above other classification schemes available.

Soon, a CPI Database

In the course of work on COCPIT, Przybylo, in particular, has grappled with a few challenges. These include separating individual images from sheets, or frames of images; dealing with outdated computer interfaces; introducing novel file formats; and the struggle for direct access to CPI data, which sometimes requires reaching out to experts who took part in a given campaign.
Przybylo was behind one important recent step: setting up a pre-processing function for images. Sulia has also hand-labeled an initial dataset and hyperoptimized the machine learning scheme to enable accurate model learning.
Sulia estimates that her COCPIT group will establish a proof-of-concept scheme in three years, including a working CPI database with which DOE users can interact.
She also predicts that more DOE campaigns and millions of additional images (some, perhaps, from sources other than CPI) will expand the machine-learning model’s capabilities and database.
“We are really excited about this project,” says Sulia, “and intend to expand the capabilities we envision beyond three years.”
“We” includes not only the intrepid Przybylo, but Carl Schmitt, a project scientist at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. He’s an expert in CPI instrumentation and the raw data it generates.
Zachary Lebo, an assistant professor at the University of Wyoming, is in charge of linking the COCPIT project with traditional modeling approaches that may inform CPI particle growth history beyond simply capturing a given particle.

Early Dreams and Ambitions

As it happens, Lebo was a couple of years ahead of Sulia at Pennsylvania State University, where she earned her degrees in meteorology (B.S. 2009, PhD 2013).
For the longest time, Penn State loomed large in her imagination.
Sulia was in fourth grade in her native Cookstown, New Jersey, when―inspired by her older sister Justine―she decided to become a broadcast meteorologist. (Her father was a logistics officer in the U.S. Air Force at nearby McGuire Air Force Base, where now, as a civilian, he is Inspector General; her mother is a grade-school teacher.)
By sixth grade, Sulia had settled on the exact university she would attend and all through high school “everything I did was to get into Penn State.” That included AP calculus and a half-day weekly internship one year with the National Weather Service.
In college, Sulia participated in Campus Weather Service, doing both TV and radio work―but without much joy.
Instead, she dug into all the “nitty-gritty math and physics” classes she could find. During a NASA internship one summer, Sulia did her first coding and software script writing.
As Sulia took a hard turn towards research, professors Jerry Harrington and Jon Nese were special influences.

Shape Matters

In the PhD program at Penn State, Sulia worked a lot with Eugene Clothiaux and (extensively, she says) Johannes Verlinde.
Verlinde was lead scientist on a 2004 ARM field campaign called the Mixed-Phase Arctic Cloud Experiment (M-PACE), which also generated CPI data Sulia is using for COCPIT.
Her dissertation work―on improving a model that predicts ice particles―foreshadowed what she does now. It was on a model that captured changing particle shapes and how they evolved over time.
Shape matters in ice particles, including their fall speed through the atmosphere and the difference in radiative effect determined by shape, whether plate, column, or sphere.
To this day, some model parameters assume that ice particles are shaped like spheres. Sulia’s dissertation “allowed for a better prediction of mass and energy budget.”
However, the PhD work was about the growth of individual ice particles. Today, her work is about such particles in the aggregate. In fact, it is “a new way to represent the aggregation of different ice particles with different shapes,” says Sulia.
That new way started with her 2016-2019 ASR project, which wrapped up in the fall of 2020. It was not intended to fund a version of COCPIT but to investigate the evolution of ice-particle size distribution in mixed-phase clouds.

Being in Shape Matters

Aside from COCPIT, Sulia is also busy as director of a data and visual analytics center at Albany called ExTREME Collaboration, Innovation, and Technology―xCITE, for short. Machine learning is among the pursuits there.
The xCITE center represents one of the two directions Sulia says her career is taking. One, as for years, is the science of meteorology. Another is computer science and software development. (At Albany, to illustrate, Sulia is a candidate for a bachelor’s degree in computer science.)
“I am marrying the two as much as I can,” she says.
The center, for instance, not only helps support the ASR work. But it is also at the heart of funding partnerships with the New York State Energy Research and Development Authority. Sulia and others, for instance, are leveraging the tools of meteorology to predict electricity loads and outages. They are also investigating ways to do photovoltaic forecasting of direct solar radiation.
While some of her work involves investigating a variety of shapes, Sulia is also busy, outside of science, staying in shape. She works out every day with a high-intensity exercise regime and lifestyle called CrossFit.
Says Sulia, “That’s the second thing that takes most of my time.”

Researchers receive awards during the 2020 AGU Fall Meeting

The Atmospheric Radiation Measurement (ARM) user facility and Atmospheric System Research (ASR) community is known globally for its impactful contributions to earth system research.
During its virtual 2020 fall meeting, the American Geophysical Union (AGU) held an Honors Showcase on December 9 to recognize its newest award recipients. Five members of the ARM/ASR community shared the spotlight.

New AGU Fellows

Greg McFarquhar and William D. Collins were among 62 researchers selected as 2020 AGU Fellows.
McFarquhar is the director of the Cooperative Institute for Mesoscale Meteorological Studies and a professor in the School of Meteorology at the University of Oklahoma.
Collins is the director of the Climate and Ecosystem Sciences Division at Lawrence Berkeley National Laboratory (LBNL) and a professor in residence at the University of California, Berkeley.
Both scientists entered rarefied air: AGU limits fellows to 0.1% of its membership each year.

McFarquhar has a long history working with ARM and ASR. The former chief scientist of the ARM Aerial Facility has been an ASR-funded lead scientist or co-investigator on numerous ARM campaigns.
More recently, McFarquhar was the principal investigator for the 2017–2018 Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. He is a co-investigator for the TRacking Aerosol Convection interactions ExpeRiment (TRACER), scheduled to start in June 2021 in the Houston, Texas, area.
AGU recognized McFarquhar “for fundamental advances in the understanding of cloud properties and processes, leading to their improved representation in weather and climate models.”
In a video on the AGU website, McFarquhar said that many of the ideas for his campaigns and papers “have originated from discussions that have taken place at AGU.”
McFarquhar also co-chairs the ASR High-Latitude Processes Working Group and is on the ARM-ASR Coordination Team, which fosters communication between ARM and ASR.
Learn more about McFarquhar’s career.

Collins is a co-investigator for ARM’s 2021–2023 Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in Colorado. He is also the founding director of the Environmental Resilience Accelerator, a UC Berkeley-LBNL initiative that focuses on solving challenges posed by environmental change.
The AGU Fellow citation for Collins noted his “pioneering contributions to the fundamental understanding of atmospheric radiation, radiative forcing, and the role of radiation in climate.”
In 2015, Collins received a U.S. Department of Energy (DOE) Secretarial Honor Award as chief scientist of the Accelerated Climate Modeling for Energy (ACME) project. From 2003 to 2005, he chaired the DOE/National Science Foundation Community Climate System Model Scientific Steering Committee.
“I very much look forward to continuing to tackle the critical issues around climate change with all the AGU membership in years to come,” said Collins in his acceptance video.
Read more about Collins and his AGU honor in this LBNL release.

Researchers Receive Midcareer Honors

ARM/ASR veteran Pavlos Kollias and recent ASR co-investigator Tristan L’Ecuyer were among five recipients of the 2020 AGU Atmospheric Sciences Ascent Awards, honoring their influential research and scientific leadership.
The Ascent awards, given by AGU’s Atmospheric Sciences section, are presented annually to scientists eight to 20 years removed from receiving their PhDs.

Kollias, a remote-sensing expert and former ARM associate chief scientist, has a joint appointment at Brookhaven National Laboratory and Stony Brook University in New York.
L’Ecuyer is a professor of atmospheric and oceanic sciences and the director of the Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin, Madison.
The two presented December 9 during the Frontiers of Atmospheric Science I session, highlighting the work of 2020 Ascent (midcareer) and James R. Holton Award (early career) recipients.
Kollias, an ASR-funded researcher, described a new framework in which active remote sensors, such as radars, are driven by satellite, camera, and other non-radar observations to track atmospheric phenomena in real time with unprecedented resolution.
“Doing that,” said Kollias of applying the Multisensor Agile Adaptive Sampling framework, “we were able to track for the first time the life cycle of convective clouds using a dynamic data-driven sampling framework that is expandable and can change the way we sample clouds, convection, and precipitation.”
Get more information about Kollias and his work in this release from Brookhaven National Laboratory.

L’Ecuyer shared what active sensors in space, such as NASA’s CloudSat satellite, have taught researchers about Earth’s energy balance. He said that mixed-phase clouds make up less than 8% of total global cloud cover but account for 20% of the net global cloud radiative effect at both the top of the atmosphere and the surface.
“We’ve also been able to show that clouds enhance Greenland ice sheet melt by up to 50 gigatons per year, and about half of that melt comes from supercooled water contained in mixed-phase clouds,” said L’Ecuyer.
From 2016 to 2018, L’Ecuyer was a co-investigator on an ASR project to develop and evaluate a data product for cold cloud and precipitation process analyses using observations from ARM’s Alaska sites.
Learn more about L’Ecuyer and his award in this release from the University of Wisconsin’s Space Science and Engineering Center.

A Lectured Honor

L. Ruby Leung, the chief scientist of DOE’s Energy Exascale Earth System Model (E3SM) project, gave the Jacob Bjerknes Lecture on December 7.
Named for a prominent weather researcher, the Bjerknes Lecture is given annually to a scientist who has done impactful work to advance the understanding of the atmosphere and Earth’s climate.
Leung, an atmospheric scientist at Pacific Northwest National Laboratory in Washington state, was the principal investigator for the 2015 ARM Cloud Aerosol Precipitation Experiment (ACAPEX). She is also on the core science team for the SAIL campaign.
Leung is a fellow of AGU, the American Meteorological Society, and the American Association for the Advancement of Science.
During her lecture, Leung discussed the use of the atmospheric energetic framework to understand regional precipitation changes and the importance of understanding and modeling convection in advancing that framework.
Learn more about Leung.

Nominate Your Peers

AGU section award nominations for 2021 are open now, as are nominations for union honors.
The 2021 AGU Fall Meeting is scheduled for December 13–17 in New Orleans, Louisiana.

For two and a half weeks, the 2020 American Geophysical Union (AGU) Fall Meeting had people glued to their computer screens.
Because of the COVID-19 pandemic, the meeting—typically a weeklong event in person—moved online and stretched from December 1 to 17 to accommodate the amount of content and varied time zones of the attendees.
People around the world logged on each day at all hours to view oral presentations and posters, participate in chats, visit exhibitor booths, and soak in as much of the virtual AGU experience as possible.
Even in a virtual environment, the world’s largest earth and space science meeting lived up to its reputation. AGU reported that more than 25,000 people from over 110 countries registered and that attendees viewed 585,000 assets—posters, oral presentations, and union and named lectures—during the meeting.
If you registered for AGU and missed a session, presentations and recordings are available to view through February 15, 2021 (log in to the AGU virtual platform). You can also check out a list of more than 125 presentations and posters related to ASR research.
Read the rest of this story on ARM.gov.

ARM/ASR workshop explores the role of machine learning in atmospheric science

Machine learning (ML), an algorithm-driven application of artificial intelligence (AI), is used to augment science and discovery, and it is beginning to supplant traditional statistical approaches. ML has the potential to revolutionize science, which is increasingly overwhelmed by big data sets that require analysis.
ML helps computers learn by automating some of the most complex parts of analysis. It sifts through data in search of correlations and predictors that would otherwise remain hidden or require intensive human labor to uncover.
Once ML is in motion and its algorithms are “trained” on data, it requires no explicit programming. In time, as more data are available, these algorithms learn to produce increasingly accurate solutions.
All this could drastically boost the productivity of researchers by allowing them to process larger and more comprehensive data sets than previously feasible.
In atmospheric science, emerging ML tools are important because weather and earth system modelers grapple with intersecting and complex variables.
Over two days in the fall of 2020, a star-power list of researchers affiliated with the U.S. Department of Energy (DOE) gathered for a virtual workshop on ML, statistical constraints, and other emerging methods for streamlining investigations of earth systems and weather.
The online meeting took the place of a breakout session that would have occurred in person at the June 2020 Joint Atmospheric Radiation Measurement (ARM) User Facility/Atmospheric System Research (ASR) Principal Investigators Meeting. That event was abbreviated by the need to meet virtually.
The October 19–20 workshop was also a natural follow-up to previous ARM/ASR joint meeting breakout sessions on ML.
Read the rest of this story.

Understanding a previously undocumented source on new particle formation will improve models of aerosols, clouds, and their impact on Earth’s climate

New results from an atmospheric study over the Eastern North Atlantic reveal that tiny aerosol particles that seed the formation of clouds can form out of next to nothingness over the open ocean. This “new particle formation” occurs when sunlight reacts with molecules of trace gases in the marine boundary layer, the atmosphere within about the first kilometer above Earth’s surface.
This research used data from the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign, which the Atmospheric Radiation Measurement (ARM) user facility conducted in summer 2017 and winter 2018. The U.S. Department of Energy’s Atmospheric System Research (ASR) and NASA funded the measurement analysis.
The findings, published January 22, 2021, by the journal Nature Communications, will improve how aerosols and clouds are represented in models that describe Earth’s climate so scientists can understand how the particles—and the processes that control them—might have affected the planet’s past and present, and make better predictions about the future.
ACE-ENA Principal Investigator Jian Wang, along with lead authors Guangjie Zheng and Yang Wang, initiated the study while working at Brookhaven National Laboratory (BNL) in New York.
“For a long time, people thought this process was very rare,” said Wang, now at Washington University in St. Louis. “We found from our study, however, that it happens quite frequently.”
“When we say ‘new particle formation,’ we’re talking about individual gas molecules, sometimes just a few atoms in size, reacting with sunlight,” said study co-author Chongai Kuang from BNL. “It’s interesting to think about how something of that scale can have such an impact on our climate—on how much energy gets reflected or trapped in our atmosphere.”
But modeling the details of how aerosol particles form and grow, and how water molecules condense on them to become cloud droplets and clouds, while taking into consideration how different aerosol properties (e.g., their size, number, and spatial distribution) affect those processes is extremely complex—especially if you don’t know where all the aerosols are coming from. So Wang led a team of scientists from BNL and collaborators in atmospheric research around the world to collect data in a relatively pristine ocean environment. In that setting, they expected the concentration of trace gases to be low and the formation of clouds to be particularly sensitive to aerosol properties—an ideal “laboratory” for disentangling the complex interactions.

Land and Sea

During the ACE-ENA campaign, researchers used ARM’s long-term ground-based observatory on Graciosa Island in the Azores (an archipelago 850 miles west of continental Portugal) and the facility’s now-retired Gulfstream-159 research aircraft outfitted with 55 atmospheric instrument systems to take measurements at different altitudes over the island and out at sea.
The team flew the aircraft on “porpoise flights,” ascending and descending through the boundary layer to get vertical profiles of the particles and precursor gas molecules present at different altitudes. And they coordinated these flights with measurements taken from the ground station.
The scientists hadn’t expected new particle formation to be happening in the boundary layer in this environment because they expected the concentration of the critical precursor trace gases would be too low.
“But there were particles that we measured at the surface that were larger than newly formed particles, and we just didn’t know where they came from,” said Kuang.
The aircraft measurements gave them their answer.
Read the full Brookhaven National Laboratory news release.

Follow the presentations of your ASR colleagues throughout the 2021 American Meteorological Society (AMS) Annual Meeting.
ASR Presentations at AMS

Atmospheric System Research (ASR) and the Atmospheric Radiation Measurement (ARM) user facility want to know about your presentations at the upcoming American Meteorological Society (AMS) annual meeting.
One of the best ways to draw attendees to your AMS sessions is to be highlighted on the ASR and ARM websites. If you or one of your team members will present a talk or poster during the virtual 2021 AMS Annual Meeting from January 10 to 15—and if that presentation is based on your ASR-funded project or uses ARM data as a key data source—please submit your information now.
Use this form to share your abstract information. It’s a simple three-step process to complete the form.
We will publish your abstracts on the ARM and ASR websites, which serve as an important guide for attendees. We will also send a special email to the ARM/ASR community to encourage their engagement with your presentation.
See the ARM and ASR presentations that have been shared so far.