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EarthCARE offers a sneak peek into Earth’s energy balance
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By European Space Agency
With ESA’s EarthCARE satellite and four measuring instruments all working extremely well and fully commissioned, the mission’s ‘first level’ data stream is now freely available.
By combining data from all four instruments, scientists ultimately aim to address a critical Earth science question: how do clouds and aerosols affect the heating and cooling of our atmosphere?
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By Space Force
SECAF Kendall offers his vision for the security challenges the Air Force and Space Force could face in 2050 and what is needed to properly respond.
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By NASA
7 min read
Newly Selected Citizen Science Proposals: A Peek at What’s Next
Last year, the NASA citizen science community saw a prize from the White House and two prizes from professional societies: one from the Division of Planetary Sciences and one from the American Astronomical Society. Our teams published two papers in the prestigious journal, Nature, one on a planetary crash and one about a distant world that seems to have auroras. 2024 was a year of 5000 comets, two solar eclipses and plenty of broken records.
But we’re not stopping to rest on our laurels. In 2024, NASA selected 25 new citizen science proposals for funding that will lead to new projects and new results to look forward to in 2025 and beyond. Here’s a roundup of those selections and the principal investigators (PIs) of each team—a sneak peek at what’s coming next in NASA citizen science! Note that these investigations are research grants–some of them will result in new opportunities for the public, others will use results from earlier citizen science projects or develop new tools.
Bright green glow observed from Texas on June 1, 2024, by Stephen Hummel. A new grant to the Spritacular project team will support citizen science research on this newly-discovered phenomenon. Stephen Hummel Citizen Science Seed Funding Program (CSSFP)
The CSSFP aims to support scientists and other experts to develop citizen science projects and to expand the pool of scientists who use citizen science techniques in their science investigations. Four divisions of NASA’s Science Mission Directorate are participating in the CSSFP: the Astrophysics Division, the Biological and Physical Sciences Division, the Heliophysics Division, and the Planetary Science Division. Nine new investigations were recently selected through this program:
Astrophysics Division
SuPerPiG Observing Grid, PI Rachel Huchmala, Boise State University. Use a small telescope to monitor exoplanets to improve our knowledge of their orbits. Understanding the Nature of Clumpy Galaxies with Clump-Scout 2: a New Citizen-Science Project to Characterize Star-Forming Clumps in Nearby Galaxies. PI Claudia Scarlata, University of Minnesota. Label clumps of distant galaxies to help us understand Hubble Space Telescope data. ‘Backyard Worlds: Binaries’ — Discovering Benchmark Brown Dwarfs Through Citizen Science. PI Aaron Meisner, NSF’s NOIRLab. Search for planet-like objects called brown dwarfs that orbit nearby stars. Mobile Toolkits to Enable Transient Follow-up Observations by Amateur Astronomers. PI Michael Coughlin, University of Minnesota. Use your own telescope to observe supernovae, kilonovae and other massive explosions. Planetary Science Division
A Citizen Scientist Approach to High Resolution Geologic Mapping of Intracrater Impact Melt Deposits as an input to Numerical Models, PI Kirby Runyon, Planetary Science Institute. Help map lunar craters so we can better understand how meteor impacts sculpt the moon’s surface. Identifying Active Asteroids in Public Datasets, PI Chad Trujillo, Northern Arizona University, Search for icy, comet-like bodies hiding in the asteroid belt using new data from the Canada-France-Hawaii telescope. Heliophysics Division
Enabling Magnetopause Observations With Informal Researchers (EMPOWR). PI Mo Wenil, Johns Hopkins University. Investigate plasma layers high above the Earth using data from NASA’s Magnetospheric Multiscale (MMS) mission and the Zooniverse platform. High-resolution Ionospheric Imaging using Dual-Frequency Smartphones. PI Josh Semeter, Boston University. Study the upper atmosphere using cell phone signals. Large Scale Structures Originating from the Sun (LASSOS) multi-point catalog: A citizen project connecting operations to research. PI Cecelia Mac Cormack, Catholic University of America. Help build a catalog of structures on the Sun. Comet Identification and Image Annotation Modernization for the Sungrazer Citizen Science Project. PI Oliver Gerland. Search for comets in data from ESA and NASA’s Solar and Heliospheric Observatory (SOHO) mission using new web tools. Heliophysics Citizen Science Investigations (HCSI)
The HCSI program supports medium-scale citizen science projects in the Heliophysics Division of NASA’s Science Mission Directorate. Six investigations were recently selected through this program:
Investigation of green afterglow observed above sprite and gigantic jet tops based on Spritacular project database, PI Burcu Kosar. Photograph electric phenomena above storm clouds to help us understand a newly discovered green glow and learn about atmospheric chemistry. Machine Learning competition for Solar Wind prediction in preparation of solar maximum. PI Enrico Camporeale, University of Colorado, Boulder. Take part in a competition to predict the speed of the solar wind using machine learning. A HamSCI investigation of the bottomside ionosphere during the 2023 annular and 2024 total solar eclipses. PI Gareth Perry, New Jersey Institute of Technology. Use Ham Radio data to investigate the effects of solar eclipses on the ionosphere. Dynamic footprint in mid-latitude mesospheric clouds. PI Chihiko Cullens, University of Colorado, Boulder. Collect and analyze data on noctilucent clouds, rare high-altitude clouds that shine at night. Monitoring Solar Activity During Solar Cycle 25 with the GAVRT Solar Patrol Science and Education Program. PI Marin Anderson, Jet Propulsion Laboratory. Track solar activity during the period leading up to and including solar maximum. What is the total energy input to the heliosphere from solar jets? PI Nour Rawafi, The Johns Hopkins University Applied Physics Laboratory. Identify solar jets in images from the Solar Dynamics Observatory Citizen Science for Earth Systems Program (CSESP)
CSESP opportunities focus on developing and implementing projects that harness contributions from members of the general public to advance our understanding of Earth as a system. Proposals for the 2024 request were required to demonstrate a clear link between citizen science and NASA observation systems to advance the agency’s Earth science mission. Nine projects received funding.
Engaging Citizen Scientists for Inclusive Earth Systems Monitoring, PI Duan Biggs, Northern Arizona University. Measure trees in tropical regions south of the equator with the GLOBE Observer App to improve models of vegetation structure and biomass models from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission. Integrating Remote Sensing and Citizen Science to Support Conservation of Woodland Vernal Pools, PI Laura Bourgeau-Chavez, Michigan Technological University. Map and monitor shallow, seasonal wetlands in Michigan, Wisconsin and New York to better understand these key habitats of amphibians and other invertebrates. Citizen-Enabled Measurement of PM2.5 and Black Carbon: Addressing Local Inequities and Validating PM Composition from MAIA, Albert Presto/Carnegie Mellon University. Deploy sensors to measure sources of fine airborne particle pollution filling gaps in data from NASA’s Multi-Angle Imager for Aerosols (MAIA) mission. Expanding Citizen Science Hail Observations for Validation of NASA Satellite Algorithms and Understanding of Hail Melt, PI Russ Schumacher, Colorado State University. Measure the sizes and shapes of hailstones, starting in the southeastern United States, using photographs and special pads to help us understand microwave satellite data. X-Snow: A Citizen-Science Proposal for Snow in the New York Area, PI, Marco Tedesco, Columbia University. Measure snow in the Catskill and Adirondacks regions of New York to help improve NASA’s models of snow depth and water content. Coupling Citizen Science and Remote Sensing Observations to Assess the Impacts of Icebergs on Coastal Arctic Ecosystems, PI, Maria Vernet, University of California, San Diego. Measure phytoplankton samples in polar regions to understand how icebergs and their meltwater affect phytoplankton concentration and biodiversity. Forecasting Mosquito-Borne Disease Risk in a Changing Climate: Integrating GLOBE Citizen Science and NASA Earth System Modeling, PI Di Yang, University of Florida, Gainesville. Using data on mosquitoes from the GLOBE Observer App to predict future changes in mosquito-borne disease risk. Ozone Measurements from General Aviation: Supporting TEMPO Satellite Validation and Addressing Air Quality Issues in California’s San Joaquin Valley with Citizen Science, PI Emma Yates, NASA Ames Research Center. Deploy air-quality sensors around Bakersfield, California and compare the data to measurements from NASA’s Tropospheric Emissions Monitoring of Pollution instrument (TEMPO). Under the Canopy: Capturing the Role of Understory Phenology on Animal Communities Using Citizen Science, PI Benjamin Zuckerberg, University of Wisconsin, Madison. Measure snow depth, temperature, and sound in forest understories to improve satellite-based models of vegetation and snow cover for better modeling of wildlife communities. For more information on citizen science awards from previous years, see articles from:
September 2023 August 2022 July 2021 For more information on NASA’s citizen science programs, visit https://science.nasa.gov/citizenscience.
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Last Updated Jan 13, 2025 Related Terms
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By NASA
NASA’s Dawn spacecraft captured this image of Vesta as it left the giant asteroid’s orbit in 2012. The framing camera was looking down at the north pole, which is in the middle of the image.NASA/JPL-Caltech/UCLA/MPS/DLR/IDA Known as flow formations, these channels could be etched on bodies that would seem inhospitable to liquid because they are exposed to the extreme vacuum conditions of space.
Pocked with craters, the surfaces of many celestial bodies in our solar system provide clear evidence of a 4.6-billion-year battering by meteoroids and other space debris. But on some worlds, including the giant asteroid Vesta that NASA’s Dawn mission explored, the surfaces also contain deep channels, or gullies, whose origins are not fully understood.
A prime hypothesis holds that they formed from dry debris flows driven by geophysical processes, such as meteoroid impacts, and changes in temperature due to Sun exposure. A recent NASA-funded study, however, provides some evidence that impacts on Vesta may have triggered a less-obvious geologic process: sudden and brief flows of water that carved gullies and deposited fans of sediment. By using lab equipment to mimic conditions on Vesta, the study, which appeared in Planetary Science Journal, detailed for the first time what the liquid could be made of and how long it would flow before freezing.
Although the existence of frozen brine deposits on Vesta is unconfirmed, scientists have previously hypothesized that meteoroid impacts could have exposed and melted ice that lay under the surface of worlds like Vesta. In that scenario, flows resulting from this process could have etched gullies and other surface features that resemble those on Earth.
To explore potential explanations for deep channels, or gullies, seen on Vesta, scientists used JPL’s Dirty Under-vacuum Simulation Testbed for Icy Environments, or DUSTIE, to simulate conditions on the giant asteroid that would occur after meteoroids strike the surface.NASA/JPL-Caltech But how could airless worlds — celestial bodies without atmospheres and exposed to the intense vacuum of space — host liquids on the surface long enough for them to flow? Such a process would run contrary to the understanding that liquids quickly destabilize in a vacuum, changing to a gas when the pressure drops.
“Not only do impacts trigger a flow of liquid on the surface, the liquids are active long enough to create specific surface features,” said project leader and planetary scientist Jennifer Scully of NASA’s Jet Propulsion Laboratory in Southern California, where the experiments were conducted. “But for how long? Most liquids become unstable quickly on these airless bodies, where the vacuum of space is unyielding.”
The critical component turns out to be sodium chloride — table salt. The experiments found that in conditions like those on Vesta, pure water froze almost instantly, while briny liquids stayed fluid for at least an hour. “That’s long enough to form the flow-associated features identified on Vesta, which were estimated to require up to a half-hour,” said lead author Michael J. Poston of the Southwest Research Institute in San Antonio.
Launched in 2007, the Dawn spacecraft traveled to the main asteroid belt between Mars and Jupiter to orbit Vesta for 14 months and Ceres for almost four years. Before ending in 2018, the mission uncovered evidence that Ceres had been home to a subsurface reservoir of brine and may still be transferring brines from its interior to the surface. The recent research offers insights into processes on Ceres but focuses on Vesta, where ice and salts may produce briny liquid when heated by an impact, scientists said.
Re-creating Vesta
To re-create Vesta-like conditions that would occur after a meteoroid impact, the scientists relied on a test chamber at JPL called the Dirty Under-vacuum Simulation Testbed for Icy Environments, or DUSTIE. By rapidly reducing the air pressure surrounding samples of liquid, they mimicked the environment around fluid that comes to the surface. Exposed to vacuum conditions, pure water froze instantly. But salty fluids hung around longer, continuing to flow before freezing.
The brines they experimented with were a little over an inch (a few centimeters) deep; scientists concluded the flows on Vesta that are yards to tens of yards deep would take even longer to refreeze.
The researchers were also able to re-create the “lids” of frozen material thought to form on brines. Essentially a frozen top layer, the lids stabilize the liquid beneath them, protecting it from being exposed to the vacuum of space — or, in this case the vacuum of the DUSTIE chamber — and helping the liquid flow longer before freezing again.
This phenomenon is similar to how on Earth lava flows farther in lava tubes than when exposed to cool surface temperatures. It also matches up with modeling research conducted around potential mud volcanoes on Mars and volcanoes that may have spewed icy material from volcanoes on Jupiter’s moon Europa.
“Our results contribute to a growing body of work that uses lab experiments to understand how long liquids last on a variety of worlds,” Scully said.
Find more information about NASA’s Dawn mission here:
https://science.nasa.gov/mission/dawn/
News Media Contacts
Gretchen McCartney
Jet Propulsion Laboratory, Pasadena, Calif.
818-287-4115
gretchen.p.mccartney@jpl.nasa.gov
Karen Fox / Molly Wasser
NASA Headquarters, Washington
202-358-1600
karen.c.fox@nasa.gov / molly.l.wasser@nasa.gov
2024-178
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Last Updated Dec 20, 2024 Related Terms
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By NASA
Download PDF: Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
Energy modulators (EM), also known as energy absorbers, are safety-critical components that are used to control shocks and impulses in a load path. EMs are textile devices typically manufactured out of nylon, Kevlar® and other materials, and control loads by breaking rows of stitches that bind a strong base webbing together as shown in Figure 1. A familiar EM application is a fall-protection harness used by workers to prevent injury from shock loads when the harness arrests a fall. EMs are also widely used in parachute systems to control shock loads experienced during the various stages of parachute system deployment.
Random forest is an innovative algorithm for data classification used in statistics and machine learning. It is an easy to use and highly flexible ensemble learning method. The random forest algorithm is capable of modeling both categorical and continuous data and can handle large datasets, making it applicable in many situations. It also makes it easy to evaluate the relative importance of variables and maintains accuracy even when a dataset has missing values.
Random forests model the relationship between a response variable and a set of predictor or independent variables by creating a collection of decision trees. Each decision tree is built from a random sample of the data. The individual trees are then combined through methods such as averaging or voting to determine the final prediction (Figure 2). A decision tree is a non-parametric supervised learning algorithm that partitions the data using a series of branching binary decisions. Decision trees inherently identify key features of the data and provide a ranking of the contribution of each feature based on when it becomes relevant. This capability can be used to determine the relative importance of the input variables (Figure 3). Decision trees are useful for exploring relationships but can have poor accuracy unless they are combined into random forests or other tree-based models.
The performance of a random forest can be evaluated using out-of-bag error and cross-validation techniques. Random forests often use random sampling with replacement from the original dataset to create each decision tree. This is also known as bootstrap sampling and forms a bootstrap forest. The data included in the bootstrap sample are referred to as in-the-bag, while the data not selected are out-of-bag. Since the out-of-bag data were not used to generate the decision tree, they can be used as an internal measure of the accuracy of the model. Cross-validation can be used to assess how well the results of a random forest model will generalize to an independent dataset. In this approach, the data are split into a training dataset used to generate the decision trees and build the model and a validation dataset used to evaluate the model’s performance. Evaluating the model on the independent validation dataset provides an estimate of how accurately the model will perform in practice and helps avoid problems such as overfitting or sampling bias. A good model performs well on
both the training data and the validation data.
The complex nature of the EM system made it difficult for the team to identify how various parameters influenced EM behavior. A bootstrap forest analysis was applied to the test dataset and was able to identify five key variables associated with higher probability of damage and/or anomalous behavior. The identified key variables provided a basis for further testing and redesign of the EM system. These results also provided essential insight to the investigation and aided in development of flight rationale for future use cases.
For information, contact Dr. Sara R. Wilson. sara.r.wilson@nasa.gov
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