A new value-added product (VAP) provides three-dimensional (3D) positions of clouds captured by stereo cameras. The Point Cloud of Cloud Points (PCCP) VAP enables 3D representations related to macrophysical cloud features such as the cloud-base and cloud-top heights, structure of cloud boundaries, and cloud-level horizontal velocities.
PCCP data are retrieved from images captured by stereo-calibrated camera pairs at SGP extended facilities E43 (Lamont), E44 (Billings), and E45 (Tonkawa).
The E43, E44, and E45 stereo cameras encircle the SGP Central Facility at an approximate 6-kilometer radius facing southeast, southwest, and north, respectively. Each camera has an approximate 75-degree horizontal field of view that extends out as far as tens of kilometers. Therefore, PCCP may include thousands of cloud-point positions from a wide horizontal region and altitude range at a time.
PCCP data for the three extended facilities are available with a 20-second time resolution in a netCDF format. A Python script that plots instantaneous PCCP data is also available.
The dates for available data are as follows:
E43: September 1, 2017, to March 3, 2020
E44: September 1, 2017, to December 1, 2019
E45: September 1, 2017, to October 30, 2019.
Scientists can use the PCCP SGP data now, with more to come.
PCCP data are also coming from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina. In addition, there are plans for PCCP data from the upcoming TRacking Aerosol Convection interactions ExpeRiment (TRACER) in the Houston, Texas, area.
The Clouds Optically Gridded by Stereo (COGS) VAP, a four-dimensional (4D) mask of cloudiness in the common field of view of the three SGP stereo camera pairs, will be available soon.
After tallying your votes, the Atmospheric Radiation Measurement (ARM) user facility’s User Executive Committee (UEC) has welcomed seven new members picked to represent you.
During the election period from October 20 to November 6, 2020, 260 votes were cast virtually. Twenty-one candidates appeared on the ballot.
New members, whose terms began January 1, 2021, are listed below with their home institutions and the science themes they were selected to represent on the UEC. The themes were previously covered by members who rolled off after serving up to four years.
The UEC is charged with providing objective, timely feedback to ARM leadership with respect to the user experience. Reporting to ARM Technical Director Jim Mather, the UEC serves as the official voice of the user community in its interactions with ARM management.
Most of the new members will serve four-year terms. The early career representative will rotate off the UEC in two years along with the current returning members.
New members are:
Pacific Northwest National Laboratory
Argonne National Laboratory
Colorado State University
University of Alaska, Fairbanks
National Center for Atmospheric Research
Early Career Representative
Lawrence Livermore National Laboratory
University of Maryland, College Park
Cloud Measurements, Cloud Modeling
Returning members are:
Allison C. Aiken, Chair
Los Alamos National Laboratory
Aerosol Measurements, Cloud-Aerosol-Precipitation Interactions
Lawrence Berkeley National Laboratory
Brookhaven National Laboratory
Cloud Measurements, Precipitation Processes, Cloud-Aerosol-Precipitation Interactions
Texas A&M University
Aerosol Modeling, Cloud Modeling, Global-Scale Modeling
Brookhaven National Laboratory
Pacific Northwest National Laboratory
Cloud Measurements, Cloud Modeling, High-Resolution Modeling
University of Miami
Aerosol Measurements, Land-Atmosphere Interactions
Aiken moved into the chair position after serving as the vice-chair for the past two years. The UEC will choose Aiken’s vice-chair.
With these VAPs, scientists can more easily use corrected observations from ARM scanning and vertically pointing cloud radars, and they can access hydrometeor masks and cloud boundaries from the entire CACTI campaign.
Scanning ARM Cloud Radar Grid (SACRGRID) VAPs provide radar moments from range-height indicator (RHI) scans on a Cartesian grid. Two SACRGRID products are available from CACTI: KASACRGRIDRHI, for the Ka-band SACR; and XSACRGRIDRHI, for the X-band SACR.
The gridded radar moments include reflectivity, mean Doppler velocity, spectral width, signal-to-noise ratio, differential phase, and—for the X-band radar—differential reflectivity and specific attenuation. The moments are accompanied by a significant detection mask, linear depolarization ratio, and copolar-to-crosspolar correlation coefficient.
Also available from CACTI is the Ka-Band ARM Zenith Radar Active Remote Sensing of CLouds (KAZRARSCL) VAP. KAZRARSCL provides best-estimate radar reflectivities, mean Doppler velocities and spectral widths in time and height, along with a significant detection mask and best-estimate cloud base. The VAP also provides cloud boundaries for up to 10 cloud layers.
KAZRARSCL, KASACRGRIDRHI, and XSACRGRIDRHI cover the entire CACTI deployment from October 2018 through April 2019 in Argentina’s Sierras de Córdoba mountain range.
KASACRGRIDRHI and XSACRGRIDRHI remap crosswind-RHI scans to a Cartesian grid. The crosswind-RHI scans consist of multiple consecutive horizon-to-horizon scans (elevations 0–180 degrees). Six azimuth angle sweeps are scanned at the CACTI site for the SACRs.
Input data are b1-level calibrated and corrected RHI data. A nearest neighbor algorithm is used for gridding, with a censor mask applied. Additional details on these pre-processing details may be found in CACTI b1-level radar processing documentation. Gaseous attenuation correction is also applied for the Ka-band radars—KASACR and KAZR—before gridding.
The output is a single netCDF file containing all aforementioned calibrated radar moments remapped onto a two-dimensional (2D) Cartesian grid.
Currently, KASACRGRIDRHI and XSACRGRIDRHI are evaluation products.
The established KAZRARSCL VAP combines KAZR, micropulse lidar, and ceilometer observations, and sounding and microwave radiometer input observations. It is run using b1-level input data.
Reflectivity is corrected for near-field antenna effects and gaseous attenuation. KAZR radar modes are optimally merged and non-hydrometeor clutter returns are removed to provide best-estimate reflectivity. Mean Doppler velocities are dealiased to correct periods when observed velocities exceeded the radar’s maximum (Nyquist) velocity and were incorrectly recorded.
Up to 10 layers of cloud boundaries (cloud base and cloud top) are determined by combining KAZR and micropulse lidar observations.
This product is available in daily files. It provides vertical profiles every four seconds from approximately 160 meters above ground level up to 18 kilometers, with 30-meter height resolution.
KASACRGRIDRHI and XSACRGRIDRHI will move to production after their evaluation period.
The Atmospheric Radiation Measurement (ARM) user facility’s latest annual report is now available for you to view online. In addition to story briefs summarizing fiscal year 2020 (FY2020), the report contains striking images from contributors within and outside ARM.
The report includes an overview of ARM, featured field campaigns conducted during FY2020, user research results, ARM infrastructure achievements, and data product announcements. Also, there are updates on past ARM field campaigns that continue to generate papers and other publications.
Read the technical director update to learn how ARM navigated challenges associated with the COVID-19 pandemic while continuing to augment its scientific impact.
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.
In September 2019, the expedition commenced on a German research icebreaker to document the atmosphere, sea ice, ocean, biogeochemistry, and ecosystem in the central Arctic over a full year. ARM provided more than 50 instruments for MOSAiC.
In addition, Shupe was a panelist during a December 8 press conference about the 16th edition of NOAA’s Arctic Report Card and during a December 15 MOSAiC roundtable.
“MOSAiC represents the most comprehensive set of observations ever made in the central Arctic,” Shupe said during the press conference. “There’s a tremendous data legacy that will feed the arctic research community for years.”
CACTI Principal Investigator Adam Varble said that researchers are building a database chronicling thousands of convective cells tracked during the 2018–2019 campaign in Argentina. Data from the tracked cells showed cell formation to peak at three different times of day, said Varble, a scientist at Pacific Northwest National Laboratory in Washington state.
Varble also said that cell formation often occurred along the eastern slope of the Sierras de Córdoba mountain range during the day, but researchers saw a surprising shift to the western slope overnight.
Other CACTI research presented during AGU included findings on cell reflectivity and environmental conditions that support storm formation, results from ice-nucleating particle analysis, and insights into periods of warm shallow clouds.
During an ACE-ENA talk December 9, Peiwen Wang of Stony Brook University in New York shared results of the micro-spectroscopic analysis of ground and airborne samples of ice-nucleating particles and other aerosols.
A December 14 oral session included ACE-ENA presentations from Xiquan Dong of the University of Arizona and Qiuxuan Zheng of Rutgers University. Both presentations addressed retrievals during the summer 2017 and winter 2018 campaign in the Azores.
Other ARM-related presentations examined topics such as the seasonal influence on wind energy in the Southern Great Plains, relationships between environmental variables and cloud properties in the Northern and Southern hemispheres, and the upcoming TRacking Aerosol Convection interactions ExpeRiment (TRACER) in Texas.
Zuidema, a professor at the University of Miami and a member of ARM’s User Executive Committee, shared her tips for preparing a successful proposal. Her tips included reviewing facility documents, such as white papers, to understand how ARM observations could help you with your science.
Zuidema also recommended contacting experts within ARM and—for additional perspectives—researchers outside of the ARM/ASR community.
A shortlist of candidate sites for the AMF3 move is expected to be compiled by March 2021. Chongai Kuang, the AMF3 site science team lead from Brookhaven National Laboratory in New York, said the team would continue to seek feedback from the scientific community even with the shortlist in hand.
Members of the campaign’s core science team gave talks on potential SAIL observations that could help improve modeling in mountainous areas, process studies related to specific topical areas, and a DOE laboratory scientific focus area that will provide key watershed observations for SAIL.
The SAIL campaign is scheduled to go from September 2021 to June 2023.
A December 9 Honors Showcase, which recognized AGU’s newest award recipients, included five members of the ARM/ASR community:
William D. Collins (AGU Fellow), Lawrence Berkeley National Laboratory and the University of California, Berkeley
Pavlos Kollias (Atmospheric Sciences Ascent Award), Brookhaven National Laboratory and Stony Brook University
Tristan L’Ecuyer (Atmospheric Sciences Ascent Award), University of Wisconsin, Madison
Ruby Leung (Jacob Bjerknes Lecture), Pacific Northwest National Laboratory
Greg McFarquhar (AGU Fellow), University of Oklahoma.
Atmospheric research turns to the power of computer programs that learn from data
Click to watch the opening keynote talk from Rick Stevens of Argonne National Laboratory during the October 2020 ASR/ARM Topical Workshop on Machine Learning and Statistical Methods for Observations, Modeling, and Observational Constraints on Modeling. Stevens outlined AI for Science, a U.S. Department of Energy report he co-authored.
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 October 19–20 workshop was also a natural follow-up to previous ARM/ASR joint meeting breakout sessions on ML.
A Good Match
ARM, a DOE scientific user facility, seems like a good match for ML applications. In play for nearly three decades, ARM is among the largest sources of atmospheric data in the world and has petabytes of them.
In addition to fixed and mobile observatories, the user facility has a pair of high-powered computing clusters, Stratus and Cumulus. Both systems can be accessed by scientists funded by ASR or other research programs to work with ARM data through high-performance computing facility requests, which are much like requests for field campaigns.
The two clusters are also “data adjacent” and can easily stage large archives of ARM measurements for ML tasks.
In 2017, to acknowledge and encourage using ML in weather and earth system science, ARM issued a call asking for “applications of machine learning for improving ARM data quality and uncertainty.” The call yielded projects that illustrate the kind of ML work being done within atmospheric science.
For example, Shaocheng Xie, a research scientist at Lawrence Livermore National Laboratory in California, led a team of researchers to develop an ML framework for automating quality assessments of ARM data. The team focused on a model that detects signal noise from rain contamination on the wide, screen-like exterior radomes of microwave radiometers.
Edward Luke, a senior applications engineer at Brookhaven National Laboratory and a proponent of ML back in the 1990s, used the call to develop ML algorithms to detect errors introduced into scanning radar data by sea clutter. The term describes a non-meteorological phenomenon linked primarily to wave action on open water.
“We felt it was important to get practitioners of machine learning in ARM and ASR together. We need to discuss approaches and limitations, and to build a community.”
Luke and Xie, who is now ARM’s value-added products (VAPs) and translators lead, were involved in breakout sessions on ML applications during the 2018 and 2019 ARM/ASR joint meetings.
The online format of the 2020 workshop allowed for a significantly expanded audience of researchers―more than otherwise possible at an in-person meeting. Breakout sessions normally do not draw almost 200 participants, as the October workshop did. They also take two hours or less, not two half-days.
“We felt it was important to get practitioners of machine learning in ARM and ASR together,” says workshop co-organizer Joseph Hardin, a computational scientist at Pacific Northwest National Laboratory (PNNL) in Washington state. “We need to discuss approaches and limitations, and to build a community.”
The online workshop revealed emerging ML-centered partnerships among national laboratories, industry, and universities.
Combined with DOE’s interest in ML and AI, says Hardin, such collaborations “accelerate the speed at which we can understand how, and where, these techniques can best be used in our field.”
Bulk Ice, Cloud Particles, and More
The October workshop unleashed a fire hose of information. There were four hours of presentations each day, adding up to 22 talks, with deep-diving side chats and lively question-and-answer sessions.
To provide a small sample, there were workshop talks that touched on ML-assisted investigations of:
Vanessa Przybylo, a PhD student at the University at Albany, State University of New York, delivered an update on Classification of Cloud Particle Imagery and Thermodynamics (COCPIT). The ASR-funded project is developing an ML-aided scheme for classifying ice particle images captured by research aircraft during DOE field campaigns. COCPIT then co-locates these images with associated environmental properties.
Currently, says Przybylo, these abundant ice particle images “lack consolidation and are vastly underutilized.”
To date, most ML applications involve neural networks and decision trees―nested sets of decisions through which data flow.
Neural networks were popular in the 1980s and periodically thereafter. They failed to live up to their data-analysis promise, however, until graphics processing units (GPUs) started to proliferate in high-performance computers. More GPUs made training the layered, brain-like neural networks practical.
In August 2020, Hardin, a former ARM radar engineer, helped teach an online course on understanding and applying ML.
Finding the right algorithm and tuning it is only about one-third of the ML challenge, he says. “The rest is cleaning up and preparing the data.”
In addition to Hardin, other workshop co-organizers were:
Most of the organizers are involved in active ML projects.
Theisen, for example, is on a team working on a simple ML algorithm to calculate estimated rain rates from a variety of sensors. Their testbed is ARM’s Southern Great Plains (SGP) atmospheric observatory, where optical, mechanical, acoustic, and laser sensors measure precipitation.
‘We Want to Find Ways to Make This Work’
“As scientists, it’s our job to sift through piles and piles of data and try to extract useful relationships. Machine learning methods are designed to help. These tools are no longer black boxes. They are a game-changer.”
“As scientists, it’s our job to sift through piles and piles of data and try to extract useful relationships,” she says. “Machine learning methods are designed to help. These tools are no longer black boxes. They are a game-changer.”
Making ML Impacts
Within ARM and ASR, “machine learning is already making an impact,” says Hardin.
He cites investigations of proxy models, parameterizations, earth system forecasting, data fusion, super-resolution outputs, and even model replacement.
In other ML-radar work, PNNL’s Samson Hagos, Zhe Feng, and others leveraged 15 years of ARM radar observations in the tropical western Pacific to develop a model that constrains poorly understood interactions between tropical convective and stratiform clouds. Hagos presented a workshop talk on the ML-assisted cloud population model, which enables improved representations in high-resolution regional and global earth system models.
ANL’s Scott Collis introduced the Sage project, funded by NSF. It will “push AI to the edge,” he says, by deploying a network of sensors to record images, audio, weather, air quality, and other data. (The “edge” refers to the part of a network as close to a sensor as possible.) The plan is to link existing sensors to computers embedded within ML frameworks.
A sister project, with Collis as the lead scientist, is called ARMing the Edge, designed to improve Doppler lidar observations streaming from ARM’s SGP atmospheric observatory. (Work started in August 2020.)
During the workshop, ANL’s Robert Jackson outlined applications of edge computing to ARM. He ran case studies from historical Doppler lidar data collected at the SGP.
Jackson’s early results indicate that strategic lidar processing has the potential to predict clear, cloudy, and rainy conditions well over 90% of the time. That will improve the cloud processing studies now possible with SGP data, which must be compressed because of their large bandwidth.
With data preprocessing, he says, edge computing can preserve “a lot of vital information about microphysical cloud processes” not currently transmitted to ARM users.
In other ways too, adds Collis, Sage can be “tailored (to) the needs at ARM. I’m excited to see what we can do.”
He pointed to the planned deployment of pan-tilt-zoom cameras at the SGP―an effort to test ML-aided strategies for predicting cloud fraction.
Hardin, who praised “cross-community collaborations” with NSF and others, sees the workshop as “a way to get the ARM/ASR community to share cutting-edge results so that we can learn from each other.”
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.
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.
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.”
“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.
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.
Iosif “Andrei” Lindenmaier now leads the ARM radar systems engineering group, and Timothy Wendler and Vagner Castro join the team
In the spring and summer of 2020, two changes appeared on the organizational radar of the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility. ARM’s radar systems engineering group, which oversees hardware and operations, had one promotion and one hire.
Both are at Pacific Northwest National Laboratory (PNNL) in southeastern Washington state.
In October, the group recorded another change when longtime ARM radar technician Vagner Castro started as an operations specialist. He will assist radar engineers by installing, upgrading, and troubleshooting radars during field campaigns, just as he has during five campaigns since 2013.
From his home base at PNNL, Castro―university-trained as an information technology analyst―will also help track, ship, and purchase ARM radar gear.
ARM’s radar organization also includes radar translators―data interpreters―at Brookhaven National Laboratory on Long Island, New York, and Argonne National Laboratory near Chicago, Illinois.
The ARM radar group directly in charge of operations and instruments is at PNNL, under the direction of Lindenmaier. The ARM radar group overseeing data, software, and applications―newly split into its own entity―is awaiting a new lead. (See the job announcement for the new radar data mentor.)
Radars―From One to Many
Radars are a key instrument for observing details of clouds and precipitation. These devices send out pulses of electromagnetic waves that scatter back from an object in the form of interpretable signals. The energy that is scattered back to the radar receiver by the cloud and precipitation particles depends on both the frequency of the transmitted radar beam and the properties of the particles.
Radar data can provide information about the location, amount, size, and composition (water or ice) of cloud and precipitation particles.
To help capture such signals from clouds and related phenomena, ARM installed its first radar in 1996, but its radar group was as small as just one person until about 2010. It grew rapidly after that, once the user facility greatly expanded its radar capabilities. Among other things, ARM added radars in four frequency bands across a wide range of scattering regimes and also added scanning radar capabilities.
Today there are 20-plus ARM radars in critical locations around the globe―the largest array of meteorological research radars in the world.
ARM’s original radar capabilities illustrated ARM’s traditional interest in measuring phenomena within a narrow vertical column by using zenith-pointing devices. However, the expansion to scanning radars starting in 2010 broadened ARM’s view.
Scanning radars “give us information about that space,” he says, “and also help us retrieve properties that cannot be determined with an instrument that is zenith-pointing,” such as ice-crystal processes.
Coordinating, Calibrating, Delivering Data
“I will do my best. Slowly, slowly, we are getting back to normal.”
The ARM radar mentor group is responsible for managing radar operations. Others in ARM, including site operations specialists, translators, and data product developers, work with the PNNL-based group to produce the radar data needed by the science community.
“It’s a complicated puzzle,” says Mather.
Collectively, the radar mentor group has a big job. Team members calibrate and maintain radars from a variety of manufacturers, install them in often-challenging remote locations, deliver high-quality data, and flexibly cope with the pressing schedules of field campaigns.
Since early April 2020, a big part of coordinating all of that has fallen to Lindenmaier. He spends about two days a week on the PNNL campus and the rest working from home.
“I will do my best,” says Lindenmaier of his role, acknowledging interruptions because of the pandemic. “Slowly, slowly, we are getting back to normal.”
On July 22, he and Wendler worked at PNNL to calibrate dual Ka- and X-band Doppler radars, readying the devices for a future ARM field campaign. It was Wendler’s first trip to campus since his early July start date.
Have Radar, Will Travel
ARM radar engineers and technicians travel a lot for work and will likely do so again once pandemic restrictions are in the history books.
Wendler, who went to high school in Oregon, about an hour’s drive from PNNL, expects that about 30% of his time will be on the road, domestically and internationally.
“That part I am looking forward to,” he says.
Lindenmaier, who earned his PhD in physics in his native Romania, has over the years traveled to ARM sites in Oklahoma, Antarctica, Alaska, the Azores, and Finland. With a few exceptions, it has been cold-weather work.
In Tromsø, Norway, the departure point for MOSAiC researchers, he designed a special platform for the scanning ARM cloud radar on the space-constrained deck of the R/V Polarstern. The German research icebreaker was the expedition’s chief instrument platform and ice-locked laboratory.
MOSAiC wrapped in October 2020.
‘I Feel Very Important Here’
Working with radars appeals to Wendler, who says, “I like the hardware.”
His degrees are in physics (B.S. from Utah Valley University, PhD from Brigham Young University), pursued after growing up in a variety of states, including Arizona, Idaho, and finally Oregon. (His father, an emergency room physician, took on a circuit of jobs to finish his residency, then to help pay off medical school loans.)
Early on, Wendler’s physics studies took a turn toward the practical. As an undergraduate, he worked on solar energy telemetry analysis and photovoltaic cells. In his doctoral work, Wendler modeled the quantum dynamics of reactive collisions and visualized the results in three dimensions.
His professional radar work started with U.S. Department of Defense applications at a Naval Air Systems Command facility in California. It included signal processing for multi-band radar systems.
At Penn State, Wendler was part of a multi-institutional neutrino mass experiment called Project 8, which brought him in contact with PNNL researchers. He also dealt with radio frequency hardware, though in the context of cyclotron (particle accelerator) radiation experiments.
The idea of radars applied to atmospheric science is very appealing, he says.
“It is the most important, relevant field I’ve ever been in,” says Wendler. “It’s the new level of responsibility I’ve been looking for. I feel very important here.”
Snow, Snakes, and Tiny Screws
If grades were being handed out for being part of ARM, Castro would get an A. After all, since 2013, he has done work for the user facility in a lot of alphabetically A-list places: Australia, the Amazon, Antarctica, Argentina, and—most recently—in the Arctic.
Along the way, the native of Brazil has been battered by snow at both poles of the Earth, beset by snakes in the Amazon rainforest, and challenged by frequent climbs to the monkey deck high up on a research icebreaker in the central Arctic. More than once, gloveless, he replaced a set of tiny screws on the inlet door of an aerosol sampler. Snow, snakes, or screws, Castro loved every minute of it.
“I have replaced everything—boots, work clothes,” says Castro. “But my sunglasses are the same, except I changed the lenses.”
In 2011, the lenses of his whole life changed. Determined to study English, Castro moved from his hometown of Itaperuna in southeastern Brazil to Melbourne, Australia. Behind him was a career as a telecommunications data analyst. Ahead of him was two years of formal language study. He made ends meet by working at a restaurant and a car wash, and as a cleaner.
“It was perfect for me,” says Castro, who had just married the year before. (His wife, Gizela, a registered nurse who can find work easily, has been with him on every ARM campaign except the one in the Arctic.)
He traveled to Darwin to visit the ARM site there.
“I saw it and I was in love,” he says, remembering the impressive radars and the racks of computers that steered them and the prospect of working with his hands. “I said to myself, ‘This is what I want to do for the rest of my life.’”
Castro joined ARM in June 2013 and has been on the road ever since, including 19 months on Ascension Island in the South Atlantic during ARM’s 2016–2017 Layered Atlantic Smoke Interactions with Clouds (LASIC) field campaign. Along the way during the ARM work, he and Gizela decamped to the big family home in Itaperuna. Their apartment in Richland is the first home of their own in seven years.
Looking back, Castro regrets not being able to say goodbye to the ARM mentors, technicians, and staff he worked with for so long―right up until leaving the group when MOSAiC wrapped up in October. (For four months early in the yearlong expedition, Castro set up and then watched over ARM instrumentation aboard the ship and on the ice.)
Users reap benefits of activities associated with instruments, facilities, and data
The Atmospheric Radiation Measurement (ARM) user facility prioritizes development activities with an eye on present and future user needs. Decisions to acquire or decommission instruments, upgrade facilities, and proceed with new science products all happen with the user in mind.
The Engineering and Process Management group leads the coordination of new development tasks in ARM, working with facility managers, instrument mentors, data translators, software and tools developers, and site operations staff.
As MOSAiC rolled on in the central Arctic and COMBLE in northern Norway, ARM worked to secure new instruments for future deployments. Users also got a slew of new science products and a revamped data portal for accessing them.
In FY2020, ARM continued to add or replace instruments across its observatories while addressing future instrument needs.
ARM ordered two more 3-channel microwave radiometers (MWR3Cs), which will go to the Southern Great Plains (SGP) and North Slope of Alaska (NSA) atmospheric observatories. In FY2019, new MWR3Cs shipped out with the first and second ARM Mobile Facilities (AMF1 and AMF2) for COMBLE and MOSAiC, respectively.
The RWP was needed because TRACER, an AMF1 campaign scheduled to run at the same time as SAIL, will also use a 915 MHz RWP.
“A certain frequency (1290 MHz) is required outside of the U.S., and another frequency (915 MHz) is required inside of the U.S.,” says ARM Engineering and Process Manager Jennifer Comstock, who is also responsible for ARM science products. “And normally we have one outside the U.S. and one inside the U.S. at any time, so we can swap them from which AMF needs them, but we have TRACER and SAIL in the U.S. this time.”
In FY2020, ARM also performed—and prepared for—some instrument shifting from one site to another.
After upgrading a high-spectral-resolution lidar previously at the NSA, ARM is testing it at the SGP by running it with the Raman lidar. In a follow-on to a 2015 study at the SGP, ARM combined the lidars so it could test multiwavelength aerosol retrievals to provide vertical profiles of aerosol properties.
Watch the video above to learn about ARM’s new Data Discovery.
Not too long before the launch, ARM surpassed 2 petabytes of data in its nearly 30-year-old archive.
“In the past, users experienced a bit of a challenge finding the data that better suit their needs, especially with our vast collection of data,” said Giri Prakash, manager of ARM’s data services, when the new Data Discovery officially launched in late May 2020.
With feedback from users and stakeholders at the heart of its development, the new Data Discovery is designed to make it easier to find and order the right data. Additional capabilities are coming.
Another sizable FY2020 project focused on preparing existing VAPs and data ingests to run on a new version of RedHat, a Linux operating system installed on the ARM Data Center’s servers. This work included updating VAP codes to work on the new RedHat7.
For instrument mentors and site operations staff, the ARM Data Center implemented a new system in which preventive and corrective instrument maintenance could be reported in one spot.
The metadata team also participated in an effort across ARM to improve the discoverability of AAF data. The effort involved AAF personnel, ARM instrument mentors, communications and website development staff, and Comstock.
A New Group of Science Products
In FY2020, ARM released b1-level products for aerosol size distribution, aerosol chemical composition, trace gas, and radar data.
ARM b1-level products undergo calibration, correction, and quality control processes beyond ARM’s standard quality checks and corrections. Oftentimes, b-level products are then further developed into c1-level value-added products (VAPs).
The new b1-level aerosol size distribution and trace gas products emerged from efforts to harmonize—or standardize—data. For instance, in the case of aerosol size distribution, the b-level processing of data from four instruments will allow the data to be merged into a single size distribution. The four instruments are the ultra-high-sensitivity aerosol spectrometer, aerodynamic particle sizer, and regular and nanoparticle versions of the scanning mobility particle sizer.
The b1-level radar products came from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina. These highly characterized CACTI data—collectively known as a data epoch—can lessen uncertainty in retrievals such as particle phase or size distribution.
In FY2020, the LASSO team released a fifth season of data bundles from the SGP shallow convection scenario (now on hiatus). In doing so, the team made available the high-frequency data used to generate skill scores in the bundles and inform the LES simulations.
ARM also continued to roll out its best-estimate (ARMBE) data sets for atmospheric measurements and cloud and radiation quantities. In FY2020, the ARMBE team announced data from the ENA observatory and mobile facility campaigns in Brazil, Niger, and Antarctica. More ARMBE data are on the way.