Value-Added Product Delivers 3D Cloud Positions Based on Stereo Photographs

Point Cloud of Cloud Points value-added product data from Southern Great Plains atmospheric observatory
The figure presents PCCP VAP data from the Southern Great Plains E45 site (Tonkawa, Oklahoma) on March 24, 2020. The upper panel displays the distribution of the reconstructed cloud points by time and height. The colors represent the count of the detected points in the corresponding bin. The bottom left panel is a picture from the E45a camera captured at 20:43:20 UTC. Vertically projected positions of the reconstructed cloud points associated with that picture appear in the bottom right panel on the x-y plane. The blue and green squares represent the two E45 stereo cameras.

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.

Currently, PCCP is available for the Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) atmospheric observatory in Oklahoma.

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.

More information about PCCP can be found on the VAP web page. For questions or to report data problems, please contact Rusen Öktem or David Romps.

Data can be referenced as doi:10.5439/1531325.

Access the data set in the ARM Data Center. (Go here to create an account to download the data.)

New Members Selected to Join ARM User Executive Committee

Allison C. Aiken, an aerosol scientist at Los Alamos National Laboratory, is the ARM User Executive Committee’s new chair. Aiken leads a committee of 14 members—seven of them recently chosen by the ARM user community.

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:

Susannah Burrows
Pacific Northwest National Laboratory
Aerosol Modeling

Scott Collis
Argonne National Laboratory
Precipitation Processes

Jessie Creamean
Colorado State University
Aerosol Measurements

Jennifer Delamere
University of Alaska, Fairbanks
Radiative Transfer

Christina McCluskey
National Center for Atmospheric Research
Early Career Representative

Yunyan Zhang
Lawrence Livermore National Laboratory
Land-Atmosphere Interactions

Youtong Zheng
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

Daniel Feldman
Lawrence Berkeley National Laboratory
Radiative Transfer

Scott Giangrande
Brookhaven National Laboratory
Cloud Measurements, Precipitation Processes, Cloud-Aerosol-Precipitation Interactions

Xiaohong Liu
Texas A&M University
Aerosol Modeling, Cloud Modeling, Global-Scale Modeling

Art Sedlacek
Brookhaven National Laboratory
Aerial Measurements

Adam Varble
Pacific Northwest National Laboratory
Cloud Measurements, Cloud Modeling, High-Resolution Modeling

Paquita Zuidema
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.

Learn more about the UEC members on the committee’s web page.

For more information about UEC operations, read the UEC Charter.

New Cloud Radar Products Available for CACTI Campaign

XSACRGRIDRHI CACTI product with 2D horizontal-distance-versus-vertical-height gridded radar reflectivity
This shows the XSACRGRIDRHI CACTI product with two-dimensional horizontal-distance-versus-vertical-height gridded radar reflectivity when the azimuth angle is 90 degrees (east-to-west scan).

The Atmospheric Radiation Measurement (ARM) user facility released new cloud radar value-added products (VAPs) from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign.

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.

Plots illustrating the KAZRARSCL CACTI product
These plots illustrate the KAZRARSCL CACTI product. From top to bottom are time-versus-height plots of cloud boundaries, best-estimate hydrometeor reflectivity, and dealiased mean Doppler velocity from December 30, 2018.

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.

For more information about these VAPs, go to the web pages for KASACRGRIDRHI, XSACRGRIDRHI, and KAZRARSCL.

For questions, feedback, or to report data problems, please contact Meng Wang or Scott Giangrande for KASACRGRIDRHI and XSACRGRIDRHI, and Karen Johnson or Scott Giangrande for KAZRARSCL.

KASACRGRIDRHI data can be referenced as doi:10.5439/1645175 and XSACRGRIDRHI data as doi:10.5439/1645174.

KAZRARSCL data can be referenced as doi:10.5439/1228768.

Access the data sets for KASACRGRIDRHI, XSACRGRIDRHI, and KAZRARSCL in the ARM Data Center. (Go here to create an account to download the data.)

Save the Date for the 2021 ARM/ASR Joint Meeting!

2021 ARM/ASR virtual joint meetingThe next Joint Atmospheric Radiation Measurement (ARM) User Facility/Atmospheric System Research (ASR) Principal Investigators Meeting will be a virtual meeting the week of June 21 to 25, 2021.

This meeting will bring together ARM users, ARM infrastructure members, and ASR researchers to review progress and plan future directions for the ARM user facility and ASR research.

Check back soon for updates.

2020 ARM Annual Report Now Available

2020 ARM Annual Report cover
Now available online, the Fiscal Year 2020 ARM Annual Report includes stunning images from the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.

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.

Access the report online.

2020 AGU Fall Meeting: All Virtual, Still Heavy on ARM Data

Premier earth and space science meeting includes an abundance of ARM presentations

2020 American Geophysical Union Fall MeetingFor 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 almost 150 oral presentations and posters related to data from the Atmospheric Radiation Measurement (ARM) user facility.

All About MOSAiC

Matthew Shupe during the MOSAiC expedition in the Arctic
Matthew Shupe, co-organizer of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, was the primary convener of several MOSAiC-related oral and poster sessions during the 2020 AGU Fall Meeting. He was also a panelist during a NOAA press conference and a MOSAiC roundtable. Photo is courtesy of Shupe, Cooperative Institute for Research in Environmental Sciences.

About two months after the conclusion of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, groups of researchers presented preliminary findings at AGU.

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.

On December 11, back-to-back MOSAiC-focused oral sessions included talks on ARM Aerosol Observing System data, warm-air intrusions, sea ice reflectivity, and snowfall estimation.

A related poster session on the 14th explored topics such as ice-nucleating particles, aerosol number concentration, and the structure and evolution of arctic cyclones.

Matthew Shupe, a co-coordinator of MOSAiC and a senior research scientist with the Cooperative Institute for Research in Environmental Sciences, was the primary convener of all three sessions.

A researcher supported by the U.S. Department of Energy’s (DOE) Atmospheric System Research, Shupe also presented a poster about cloud influences on the surface energy budget.

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.”

For other MOSAiC news from AGU, read ARM’s AGU Scoop blog.

Around the World, ARM Style

Second-generation C-Band Scanning ARM Precipitation Radar during CACTI
Data from the second-generation C-Band Scanning ARM Precipitation Radar helped researchers build a database of thousands of convective cells tracked during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina. Photo is by Joseph Hardin, Pacific Northwest National Laboratory.

Besides MOSAiC, there were clusters of AGU presentations on ARM field campaigns such as Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) and Cloud, Aerosol, and Complex Terrain Interactions (CACTI).

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.

Read the AGU Scoop blog for more on these and other presentations.

Town Halls

Third ARM Mobile Facility at Oliktok Point, Alaska
The third ARM Mobile Facility (AMF3) will stop operating in Alaska in fiscal year 2021. Its eventual destination will be the Southeastern United States.

Day One of AGU featured a town hall on collaborating with ARM and other DOE scientific user facilities. The town hall included a presentation from Paquita Zuidema, principal investigator for ARM’s 2016–2017 Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign on Ascension Island.

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.

The next day, a town hall on the upcoming deployment of the third ARM Mobile Facility (AMF3) to the Southeastern United States covered science drivers, siting considerations, instrumentation, and outreach to possible collaborators.

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.

ARM’s upcoming Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the Colorado Rockies also got a town hall on December 8. SAIL Principal Investigator Daniel Feldman, a research scientist at Lawrence Berkeley National Laboratory in California, moderated the program.

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.

Applause, Applause

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.

Read more about the researchers and their awards in this article.

The Emerging Magic of Machine Learning

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 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.

A Good Match

Edward Luke at ARM's Eastern North Atlantic atmospheric observatory
Brookhaven National Laboratory senior applications engineer Edward Luke, an early proponent of machine learning, stands at ARM’s Eastern North Atlantic atmospheric observatory. There, he conducted a machine learning-aided study of sea clutter that interferes with radar signals. Photo is by Nitin Bharadwaj, then at Pacific Northwest National Laboratory.

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.

Luke says the proposed ML framework can help maximize the value of data from ARM’s Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The two-phase ground- and air-based campaign in the Azores spanned 2017 and 2018.

Next Steps

“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.”

Joseph Hardin, workshop co-organizer from Pacific Northwest National Laboratory

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

Cloud particle image
Cloud particle images, such as the one here, are being identified and categorized in a machine learning-aided process developed at the University at Albany, State University of New York. Image is courtesy of Kara Sulia, University at Albany.

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:

Daniel Feldman, a research scientist at Lawrence Berkeley National Laboratory in California, delivered a summary of ML science opportunities during ARM’s upcoming Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign. Feldman is the principal investigator for SAIL, slated to launch in September 2021 in the Colorado Rockies.

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.”

See the full list of speakers and topics.

Saved by GPUs

Susannah Burrows, Pacific Northwest National Laboratory
Workshop co-organizer Susannah Burrows, a Pacific Northwest National Laboratory researcher interested in the power of machine learning, introduces a speaker for an October 19 talk.

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.”

Elizabeth Barnes, Colorado State University

In the first of the workshop’s three keynote addresses, ANL computational scientist Rick Stevens outlined AI for Science, a DOE report that appeared in February 2020. Co-authored by Stevens and five other DOE researchers, the report is a baseline document for developing AI technologies over the next five to 10 years.

Day Two keynote speaker Amy McGovern of the University of Oklahoma outlined aspirations for the new $20 million, five-year National Science Foundation (NSF) institute she directs, the AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography.

“We want to find ways to make this work across (atmospheric science) agencies,” she says.

Elizabeth Barnes of Colorado State University delivered the workshop’s last keynote, on the need for interpretable ML in climate science.

“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

Machine learning algorithm to derive convective and stratiform transition functions
A 2020 paper led by Samson Hagos of Pacific Northwest National Laboratory demonstrated this machine learning algorithm, constructed to derive convective and stratiform cloud transition functions. Figure is courtesy of the Journal of Advances in Modeling Earth Systems.

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.

Hardin is working on ML applications for radars with PNNL postdoctoral researcher Andrew Geiss, who delivered a workshop talk. A key paper of theirs, on using a neural network to achieve super-resolution radar images of precipitation features, was published online in November.

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.

For numerical weather forecast models, ANL’s Jiali Wang is developing a domain-aware neural network to emulate the planetary boundary layer. During the October workshop, she delivered a talk on the challenges of using deep learning—an extension of ML—to generate high-resolution earth system data.

ARM is also developing VAPs from its data by using ML. At PNNL, earth scientist Donna Flynn created a soon-to-be-released version of the Micropulse Lidar Cloud Mask (MPLCMASK) VAP. It uses ML to improve the cloud mask from the ARM micropulse lidar, and hence also improves the detection of hard-to-discern low-level clouds.

Sage and a Sister Project

Doppler lidar at ARM's Southern Great Plains atmospheric observatory
Doppler lidar observations from ARM’s Southern Great Plains atmospheric observatory are at the heart of a new machine learning-related project. Photo is by Nicki Hickmon, Argonne National Laboratory.

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.”

View the ML workshop presentations in this YouTube playlist.

AGU Honors ARM/ASR Community Members in 2020

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.

Greg McFarquhar
Greg McFarquhar

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.

William D. Collins
William D. Collins

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.

Pavlos Kollias
Pavlos Kollias

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.

Tristan L'Ecuyer
Tristan L’Ecuyer

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 Lecturer Honor

L. Ruby Leung
L. Ruby Leung

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 open January 15, as do nominations for union honors.

The 2021 AGU Fall Meeting is scheduled for December 13–17 in New Orleans, Louisiana.

Changes on the ARM Personnel Radar

Iosif “Andrei” Lindenmaier now leads the ARM radar systems engineering group, and Timothy Wendler and Vagner Castro join the team

Iosif “Andrei” Lindenmaier
Iosif “Andrei” Lindenmaier poses by radars being calibrated at Pacific Northwest National Laboratory (PNNL). Lindenmaier became ARM’s radar systems engineering lead in 2020. Photo is by Andrea Starr, PNNL.

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.

Iosif “Andrei” Lindenmaier, who has been with ARM as a radar engineer since 2012, stepped into the role of radar systems engineering lead. Timothy “Tim” Wendler, who was a postdoctoral researcher at Pennsylvania State University, joined ARM on July 6 as the radar group’s newest engineer and instrument mentor.

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

ARM radars operate all over the world
ARM radars, such as Ka-Band ARM Zenith Radars (KAZRs), scanning ARM cloud and precipitation radars (SACRs and SAPRs), and the Marine W-Band ARM Cloud Radar (MWACR), operate all over the world.

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.

“We are also interested in the region around the column―in three-dimensional space,” says ARM Technical Director Jim Mather.

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.”

Andrei Lindenmaier, ARM radar systems engineering lead, acknowledging pandemic-related interruptions

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

Second ARM Mobile Facility during AWARE field campaign in Antarctica
Radars with the second ARM Mobile Facility collected data at McMurdo Station during the 2015–2017 ARM West Antarctic Radiation Experiment (AWARE). Lindenmaier prepared and installed radars for AWARE.

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 Antarctica, he prepared and installed radars for the 2015–2017 ARM West Antarctic Radiation Experiment (AWARE).

“This was a successful campaign,” says Lindenmaier, who points out that summertime Antarctica is “colder than Finland in winter.”

During AWARE, the second ARM Mobile Facility was in the field, one of three ARM Mobile Facilities deployed around the world for campaigns up to a year at a time, on average.

“Based on that accumulated experience” during AWARE, says Lindenmaier, “I prepared the same radars (in 2019) for the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign.”

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’

Timothy Wendler
Timothy Wendler, newly at PNNL, pauses from work in his home office. On the right-hand monitor, he was taking a virtual tour of radars at ARM’s Southern Great Plains atmospheric observatory in Oklahoma. Under the center monitor is a schematic for the pictured radar. Photo is courtesy of Wendler.

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

Vagner Castro
Vagner Castro, in his signature sunglasses, poses in front of ARM instruments in Yacanto, Argentina, in 2018 during ARM’s Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. Photo is courtesy of Castro, now at PNNL.

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.

In all of Castro’s travels for ARM, one item of gear has stayed the same: the pair of sunglasses he bought in Australia during his first ARM training at the now-closed Tropical Western Pacific atmospheric observatory in Darwin.

“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.

‘Everything’s Perfect’

In 2013, hard work paid off when a contractor advertised for an information technology expert who spoke English and Portuguese. Destination: ARM’s Green Ocean Amazon (GoAmazon2014/15) field campaign in Brazil.

“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.)

MOSAiC PI Matthew Shupe and instrument technicians on the ice
During the 2019–2020 Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, Castro, left, poses for a photo on the ice with, from second to left, MOSAiC co-coordinator Matthew Shupe and ARM technicians Steele Griffiths and Juarez Viegas. Photo is courtesy of Shupe, Cooperative Institute for Research in Environmental Sciences.

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.)

“I will always be grateful,” he says.

Soon, at least for a week or two at a time, Castro will be on the road again. He’ll help pack up a huge C-Band Scanning ARM Precipitation Radar at the Southern Great Plains atmospheric observatory, then set it up in Houston, Texas. The radar will contribute to ARM’s TRacking Aerosols Convection interactions ExpeRiment (TRACER), slated to last a year.

Getting to Richland in October cost Castro and his wife a pandemic-related delay of seven months in Brazil. Now they are a 10-minute walk from the PNNL campus, with a view of trees and farm fields.

“Everything’s perfect,” he says. “I want to live here forever now.”

Development Achievements Keep ARM Moving Forward

Users reap benefits of activities associated with instruments, facilities, and data

Jennifer Comstock, ARM engineering and process manager
Jennifer Comstock, ARM’s engineering and process manager, gives updates on ARM development progress in fiscal year 2020.

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.

In fiscal year 2020 (FY2020), ARM devoted many resources to supporting the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE).

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.

One new Cimel sunphotometer ordered in FY2020 went to the Eastern North Atlantic (ENA) atmospheric observatory, and a second will be deployed for the upcoming TRacking Aerosol Convection interactions ExpeRiment (TRACER) in Texas.

Two more Cimels capable of detecting lunar light will be purchased in FY2021. Those Cimels are among several new instruments slated to deploy with AMF2 for the 2021–2023 Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in Colorado. Additional new instruments include a scanning mobility particle sizer and a 915-megahertz (MHz) radar wind profiler (RWP).

Raman lidar at ARM Southern Great Plains atmospheric observatory
The Raman lidar, right, and high-spectral-resolution lidar have been paired at ARM’s Southern Great Plains atmospheric observatory to obtain multiwavelength aerosol retrievals. Photo is by Nicki Hickmon, Argonne National Laboratory.

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.

The high-spectral-resolution lidar’s ultimate destination has yet to be determined, says Comstock. ARM, however, intends to install two new atmospheric emitted radiance interferometers at the SGP.

In preparation for taking aging Doppler lidars out of service for maintenance, ARM acquired a new one that could be moved in and out of locations to avoid “a big data gap,” says Comstock.

The new Doppler lidar has an extended range to roughly 10 kilometers and improved sensitivity.

ARM Facilities

New hangar for ARM Aerial Facility aircraft in Pasco, Washington
The ARM Aerial Facility now occupies a new 18,000-square-foot hangar in Pasco, Washington. Photo is by Jeff Pittman, Pacific Northwest National Laboratory.

Another FY2020 priority was improving ARM facilities.

The ARM Aerial Facility (AAF) got a new hangar to house its Bombardier Challenger 850 jet, which is being modified to accommodate scientific missions.

The hangar has office space for staff and visiting scientists plus room to perform aircraft and instrument maintenance.

In March 2020, ARM hosted an aerial instrumentation workshop to help evaluate appropriate capabilities for the new aircraft that satisfy the needs of the research community.

ARM also replaced two aging containers for AMF1, which in FY2020 marked the 15th anniversary of its field campaign debut in Northern California.

ARM Data Center

With an international research community ready to pounce on new arctic data, the ARM Data Center deftly handled MOSAiC and COMBLE data, even as large batches came in on hard drives.

Amid that work, the ARM Data Center officially moved to a new version of Data Discovery, where users can search for and order ARM data.

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

ARM scanning radars in Argentina
New radar data products are available or coming soon from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina. Photo is by Vagner Castro, then with VMC Services Eireli.

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.

ARM is in the midst of releasing cloud and precipitation radar VAPs from CACTI. Available now are the Ka-Band ARM Zenith Radar Active Remote Sensing of CLouds (KAZRARSCL) VAP and two new products that provide gridded moments from scanning ARM cloud radars.

CACTI data from the Corrected Moments in Antenna Coordinates Version 2 (CMAC2) VAP are coming soon. CMAC2 corrects raw ARM precipitation radar data and retrieves precipitation quantities from the radar measurements.

Science Product Updates

ARM's Southern Great Plains atmospheric observatory
The initial scenario of focus for the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity—shallow convection at the Southern Great Plains observatory—is now on hiatus. Now the LASSO team is working on a scenario based on deep convection during the CACTI field campaign. Photo is by Nicki Hickmon, Argonne National Laboratory.

Deep convection during CACTI is the next scenario of focus for the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) activity, which combines high-resolution modeling and ARM observations.

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 released its first three-dimensional large-scale forcing data product, VARANAL3D—an extension of the one-dimensional VARANAL. Both products use a constrained variational analysis approach. So far, VARANAL3D data are available from two past SGP campaigns.

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.

To stay up to date on new data releases, bookmark the Data Announcements page on

You can also check back periodically on the ARM Priorities page to view progress on science products and engineering tasks across the facility.