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SARP East 2024 Terrestrial Fluxes Group


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Preparations for Next Moonwalk Simulations Underway (and Underwater)

Faculty Advisors:

Dr. Lisa Haber, Virginia Commonwealth University

Dr. Brandon Alveshere, Virginia Commonwealth University

Dr. Chris Gough, Virginia Commonwealth University

Graduate Mentor:

Mindy Priddy, Virginia Commonwealth University

Mindy Priddy, Graduate Mentor

Mindy Priddy, graduate mentor for the 2024 SARP Terrestrial Fluxes group, provides an introduction for each of the group members and shares behind-the scenes moments from the internship.

Angelina De La Torre

Using NDVI as a Proxy for GPP to Predict Carbon Dioxide Fluxes

Angelina De La Torre

Climate change, driven primarily by greenhouse gases, poses a threat to the future of our planet. Among these gases is carbon dioxide (CO₂), which has a much longer atmospheric residence time compared to other greenhouse gases. One potential factor in reducing atmospheric CO₂ enrichment is plant productivity. Gross Primary Productivity (GPP) estimates the amount of CO₂ fixed during photosynthesis. The Normalized Difference Vegetation Index (NDVI) provides insight into the health of an ecosystem by measuring the density and greenness of vegetation. Therefore, it can be inferred that there is a relationship between NDVI and GPP, as greener plants are likely more productive. In this study, we used NDVI as a proxy for GPP and analyzed the effect NDVI had on CO₂ fluxes during California’s wet season between January and March 2023 in a restored tidal freshwater wetland. GPP and CO₂ flux data were obtained from the Dutch Slough AmeriFlux tower in Oakley, California. Landsat data were used to calculate the average NDVI. The influence of NDVI on GPP was assessed using linear regression. A second linear regression was then performed using NDVI and CO₂ flux, of which GPP is one component. We anticipate that wetlands with greater vegetation density will have lower CO₂ emissions.

Because Landsat data scans in 16-day intervals, daily variation in NDVI could not be observed. This translates to a frequency discrepancy between the Landsat and AmeriFlux data, as AmeriFlux towers measure in half-hour intervals. Additionally, the wet season represented was limited by data availability, as the data before 2023 were unavailable. Despite data limitations in this study, the outlined process could be repeated in various wetland and climate classifications for further analysis of a larger sample size. This study could assist in developing strategies to increase CO₂ sequestration in an attempt to slow the effects of climate change.

Samarth Jayadev

Using Machine Learning to Assess Relationships between NDVI and Net Carbon Exchange During the COVID-19 Pandemic

Samarth Jayadev

Understanding the movement of carbon between Earth’s land surface and atmosphere is essential for ecosystem monitoring, creating climate change mitigation strategies, and assessing the carbon budget on national to global scales. Measures of greenness serve as indicators of processes such as photosynthesis that control carbon exchange and are vital in modeling of carbon fluxes. NASA’s Orbiting Carbon Observatory (OCO-2) provides high quality measurements of column-averaged CO₂ concentrations that can be used to derive net carbon exchange (NCE), a measure of CO₂ flux between terrestrial ecosystems and the atmosphere.

From OCO-2, NCE data collected at the land nadir, land glint satellite position combined with in situ sampling can provide accurate measurements on a 1°x1° scale suitable for carbon flux characterization across the contiguous United States (CONUS). Normalized difference vegetation index (NDVI), which ranges from -1 to +1, measures the greenness of vegetation, serving as an indicator of plant density and health. This can help to understand ecosystem to carbon-cycle interactions and be leveraged for determining patterns with NCE. We examined the relationship between NDVI and NCE across CONUS during 2020 using Gradient Boosting Decision Trees (GBDT) which specialize in classifying and predicting non-linear relationships. This algorithm takes multiple weak learners (decision trees) and combines their predictions in an iterative ensemble method to improve prediction accuracy. Feature and permutation importance tests found that January and August (trough and peak NDVI, respectively) were the highest weighted predictor variables related to NCE. The dataset was split in a 90% training 10% test ratio across latitude/longitude grid cells to assess and verify model performance. Using the mean squared error loss function and hyperparameters with optimal estimators, tree depth, sample split, and learning rate the algorithm was able to converge the test predictions to match the deviance of the training data. The gradient boosting model can be applied to different months and years of NDVI/NCE to further explore these relationships or a multitude of research questions. Further studies should consider integrating land use and land cover change variables such as bare land and urbanization to improve predictions of NCE.

Makai Ogoshi

Deep-learning Derived Spaceborne Canopy Structural Metrics Predict Forest Carbon Fluxes

Makai Ogoshi

Terrestrial and airborne lidar data products describing canopy structure are potent predictors of forest carbon fluxes, but whether satellite data products produce similarly robust indicators of canopy structure is not known. The assessment of contemporary spaceborne lidar and other remote sensing data products as predictors of carbon fluxes is crucial to next generation instrument and data product design and large-spatial scale modeling. We investigated relationships between deciduous broadleaf forest canopy structure, derived from deep-learning models created with lidar data from GEDI and optical imagery from Sentinel-2, and forest carbon exchange. These included comparisons to in-situ continuous net ecosystem exchange (NEE), gross primary production (GPP), and net primary production (NPP). We find that the mean  canopy height from the gridded spaceborne product has a strong correlation with forest NPP, similar to prior analysis with ground-based lidar (portable canopy lidar; PCL). For comparison to NPP, heights taken from the gridded spaceborne product were compared by overlapping the product with nine terrestrial forest sites from the National Ecological Observatory Network (NEON). We used standard deviation of canopy height as a measure of canopy structural complexity. Complexity derived from the gridded spaceborne product does not show the same strong correlation with NPP as found when using PCL. Mean annual GPP and NEE across five years were compared to the gridded spaceborne product at six Fluxnet2015-tower sites with continuous, gap-filled carbon flux data. When compared to in-situ flux tower data, neither mean canopy height nor structural complexity strongly correlate to annual NEE or GPP. Primarily, the finding that derived spaceborne products exhibit a strong correlation between forest canopy height and NPP will advance global-scale application of forest-carbon flux predictions. Secondarily, a variety of limitations highlight shortcomings in the current terrestrial flux data network. A small number of available study sites, both spatially and temporally, and lack of resolution in vertical complexity of canopy structure both contribute to uncertainty in assessing the relationships to NEE and GPP.

Sebastian Reed

Porewater Methane Concentrations Vary Significantly Across A Freshwater Tidal Wetland

Sebastian Reed

Methane is a potent greenhouse gas that is over 80 times more powerful than CO₂ at trapping heat and accounts for an estimated 30% of global temperature rise associated with climate change. The largest natural source of methane worldwide is wetlands. Despite the role of methane in driving climate change, the magnitude of global annual wetland methane flux remains highly uncertain. This study analyzes the effects of greenness (assessed using Normalized Difference Vegetation Index; NDVI), plant species composition, rooting depth, atmospheric methane concentration, and plant longevity on porewater methane concentration at the Kimages Rice Rivers Center tidal freshwater wetland. Samples for atmospheric and porewater concentrations were conducted in situ in June 2024. For each sampling location (n = 23) we collected whole air samples (WAS) 2m above the marsh surface and porewater samples 5cm below the marsh surface. We visually assessed species composition at each sample location, with 12 species of wetland plants present overall. We used the TRY plant database to find the rooting depth, leaf nitrogen content, and lifespan of each species. Drone multispectral data from 2023 was used to estimate NDVI values. These variables were compared to the pore water methane concentration via stepwise linear regression. Leaf N content, NDVI, plant species, and WAS sampling did not show statistically significant correlation to porewater methane concentration. Rooting depth showed a slight positive correlation with porewater methane (alpha = 0.1, p = 0.08, R^2 = 0.1). Samples with only perennial plants (as opposed to annual plants) had a higher mean value of porewater methane (p = 0.1). Analyzing porewater methane provides insight as to what wetland components affect methanogenesis and methane release, which aids in assessing which plant functional traits are most responsible for driving or mitigating climate change. Results from this study and future research in this area has the potential to more accurately assess how methane cycles through wetlands to the atmosphere.

Nohemi Rodarte

Understanding the vertical profile of CO₂ concentration: How carbon dioxide levels change with altitude

Nohemi Rodarte

Carbon dioxide (CO₂) is one of the main greenhouse gasses that contribute to global warming.While the relationship between CO₂ concentrations and land cover types, such as forests and urban areas, is well documented, there is limited knowledge of how CO₂ concentrations vary with altitude at fine spatial scales. Guided by our hypothesis that CO₂ levels vary with altitude and increase with elevation, we used airborne data collected from the B200 aircraft, which flew at different altitudes (400 to 1200 feet) above the urban area of Hopewell, Virginia, between 9:40 AM and 10:40 AM. We analyzed the CO₂ concentrations recorded by the flight to obtain the median and range for each 100 feet of altitude. Our results reveal that carbon dioxide concentrations varied significantly across the range of altitudes investigated. Within the area studied, CO₂ concentrations were found to range between 410 and 470 ppm. The distribution of these concentrations along the altitude gradient shows a bimodal pattern, with notable peaks at altitudes of 700 to 800 feet and 1100 to 1200 feet. Although CO₂ levels were present at all measured altitudes, there was a noticeable drop in the mean concentration at 800 feet,which then stabilized until reaching 1,000 feet before rising again. This pattern indicates that the concentrations of this greenhouse gas are not uniformly distributed with altitude, but rather vary significantly, showing higher concentrations at certain elevations and lower concentrations at others. The CO₂ distribution fluctuates with altitude, showing higher or lower levels at specific heights rather than a smooth gradient, indicating that altitude impacts CO₂ concentrations. While we did not identify the drivers of this change, future studies could evaluate how factors such as surface emissions, atmospheric mixing, and local conditions may contribute to vertical CO₂ profiles, since the altitudes we considered in this research are within the troposphere.

Camille Shaw

Linking NDVI with CO₂ and CH₄ Fluxes: Insights into Vegetation and Urban Source-Sink Dynamics in the Great Dismal Swamp

Camille Shaw

In recent years, carbon dioxide, methane, and other greenhouse gases have gained attention because of their contribution to the rise in Earth’s global mean temperature. Methane and carbon dioxide have various sources and sinks, but an expanding array of sources have created a need to assess ongoing change in carbon balance. This study aims to quantify the relationship between Normalized Difference Vegetation Index, or NDVI, and methane and carbon dioxide fluxes. We measured carbon dioxide and methane concentrations within the boundary layer using the PICARRO instrument, focusing on the Great Dismal Swamp, a forested wetland, and surrounding areas in the Eastern Mid-Atlantic Region. Data collection occurred at various times of day and along different flight paths in 2016, 2017, and 2024, with each year representing data from a single season, either spring or fall, for temporal analysis. We calculated methane and carbon dioxide fluxes along the flight paths using airborne eddy covariance, a method for capturing accurate flux measurements while accounting for the mixing of gases in the boundary layer caused by heat. Additionally, we calculated NDVI for this area using NASA’s Landsat 8 and 9 satellite imagery. Analysis of the afternoon flight data revealed a negative linear correlation between NDVI and carbon dioxide flux. Urban areas, characterized by low NDVI, exhibit a positive carbon dioxide flux as a consequence of emissions from vehicles, while forested areas, with high NDVI, show a negative carbon dioxide flux because of photosynthesis. In contrast, methane flux shows minimal correlation with NDVI. The lack of correlation arises because forested wetlands, with high NDVI, emit substantial amounts of methane, while urban areas, despite having low NDVI, still produce significant methane emissions from landfills and industrial activities. Future research could further investigate how seasonal and diurnal variations influence the correlations between NDVI and greenhouse gases by collecting comprehensive data across all seasons within a given year and at various times of the day.

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Last Updated
Nov 22, 2024

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      Project Operation and Data Product Briefing
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      Invited Presentations on Synergy with New Limb Missions in Formulation
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      Aerosols
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      Jianglong Zhang [University of North Dakota] discussed the research plans of a newly funded SAGE project to investigate effective methods for improving stratospheric aerosol analyses and forecasts from aerosol models that can be used for future air quality and visibility forecasts and climate applications. Zhang also presented preliminary comparisons of collocated SAGE aerosol extinction and Cloud Aerosol Transport System (CATS) lidar aerosol extinction values in the stratosphere. [NOTE: CATS operated on ISS from 2015–2017.]
      Sara Lu [The State University of New York, Albany] discussed efforts to examine smoke aerosol radiative effects in the upper troposphere and lower stratosphere using SAGE III/ISS observations. Lu explained that this project aims to produce multiyear analysis of aerosol radiative effects from all known pyrocumulonimbus cloud (pyroCb) events worldwide over a range of pyroCb intensities and various injection altitudes, geographic locations, and backgrounds. He presented findings from a pyroCb inventory compiled by the Naval Research Lab (NRL).
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      Figure 3. Combined transmittance fitting results from Atmospheric Chemistry Experiment– Fourier Transform Spectrometer (ACE-FTS), and SAGE III/ISS measurements demonstrate an improved characterization of sulfate particle size distribution using bi-lognormal (mode) distributions compared to a single lognormal distribution. The panels on the left show the transmittance fitting [top] and residuals [bottom] for the mono-mode distribution model, while the center panels show the transmittance fitting [top] and residuals [bottom] for the bi-mode distribution. The right panel illustrates the contributions of fine and coarse mode components to the total transmittance. The measurements for this figure were taken approximately four months after the January 2022 Hunga Tonga–Hunga Haʻapai eruption at a tangent height of 23.6 km (14.5 mi) in ACE occultation (ss100628), with coincident SAGE measurements from that same period (2022041609). Figure Credit: Adam Pastorek, adapted from a Figure in a paper published in Journal of Quantitative Spectroscopy and Radiative Transfer in January 2024. Sean Davis [NOAA, Chemistry Science Lab] presented on his research aimed at constraining decadal variability and assessing trends in stratospheric composition and tropospheric circulation using SAGE III/ISS and complementary satellite data sets. The team continues to include the SAGE water vapor and O3 products in the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset. Davis also highlighted preliminary work evaluating V6 data in comparison to the former V5.3. He discussed line-of-sight, transmission-based filtering for O3 profiles and O3 diurnal variability corrections.
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      DAY TWO
      The second day started with Jack Kaye [NASA Earth Science Division—Associate Director for Research for the Earth Science Division, emeritus as of April 30, 2025] providing a historic perspective on SAGE and comments on its context within NASA’s overall Earth science program. A technical session was held with three invited presentations, followed by three additional sessions where science team members presented their research on trace gas studies, including data product calibration and validation. The meeting concluded with updates from the SAGE project team on the SAGE III/ISS website and ongoing operations aboard the ISS. In his presentation, Kaye shared about his past involvement with the SAGE program and his perspective on its future in the context of flight missions for Earth observations.
      Invited Presentations on Advanced Modeling and New Satellite Mission For UTS
      Steven Pawson [GSFC] presented on the comprehensive modeling and analysis capabilities of
      Upper troposphere and lower stratosphere (UTLS) dynamics and composition in the Goddard Earth Observing System (GEOS) model Pawson discussed the Global Modeling and Assimilation Office’s (GMAO) recent support for the Asian summer monsoon Chemical and CLimate Impact Project (ACCLIP) mission and the trend analysis of stratospheric O3. He also discussed future plans for GMAO, including improving the representation of water vapor in UTS through data assimilation and increasing horizontal and vertical resolution in the GEOS model.
      Kostas Tsigaridis [Columbia University] presented recent research on the composition and climate impacts of increasing launches to Low Earth Orbit (LEO). Assuming that there are 10,000 launches per year and all launches use liquefied natural gas (LNG) as a propellant, the team compiled launch-related emission inventories and highlighted key uncertainties that could significantly affect climate predictions – particularly the impact black carbon has on the radiative balance and heterogeneous chemistry of the UTS. In addition, water vapor was found to contribute to the heating of the stratosphere and to a nontrivial amount of O3 depletion – 13 Dobson units (DU) on the global mean.
      Adam Bourassa [University of Saskatchewan, Canada] introduced the satellite mission for High-altitude Aerosol, Water vapor, and Clouds (HAWC), planned as the Canadian contribution to the NASA Atmosphere Observing System (AOS) for launch in 2031 – a key component in NASA’s next generation Earth System Observatory. Bourassa highlighted the three Canadian instruments, which include limb profilers for water vapor and aerosol in the UTS and a far infrared imaging radiometer for ice cloud microphysics and radiative budget closure. He discussed instrument requirements and development progress as well as results from recent sub-orbital testing of prototypes on the NASA Earth Resources (ER)-2 and stratospheric balloons.
      Trace Gases
      Brian Soden [University of Miami] presented a new project that will use SAGE data to constrain climate sensitivity in climate models. Climate models differ substantially in their calculation of the radiative forcing from carbon dioxide (CO2), and these intermodel differences have remained largely unchanged for several decades. Soden highlighted the role of stratospheric temperature in modulating the radiative forcing from CO2. He explained that models that simulate a cooler stratosphere simulate a larger radiative forcing for the same change in CO2 compared to models that posit a warmer stratosphere. He added that determining the cause of the model biases in stratospheric temperature – particularly the role of water vapor in driving this intermodel spread – is an area of active research.
      Ray Wang [Georgia Institute of Technology] compared the uncertainty analysis of SAGE III retrieved O3 and water vapor data in V5.3 to the same parameters in V6.0. He then compared the SAGE III data to the correlative measurements from other platforms. For O3, the differences between SAGE and measurements from the Microwave Limb Sounder (MLS) on NASA’s Aura platform are less than 5% in the stratosphere. SAGE V6.0 ozone values are systematically about 1–2% higher than those from V5.3 O3 –  due to changes in how the O3 cross-section is represented in each version. For water vapor, SAGE data agree with MLS and Frost Point Hygrometer (FPH) data within 5%. Wang showed some differences between SAGE water vapor data retrievals using V5.3 and the same data obtained using version 6.0. He also said that a two-dimensional (i.e., spatial and temporal) regression model can be used to minimize sampling bias in climatology derived from non-uniform satellite measurements – ensuring more accurate representation of long-term trends.
      Emma Knowland [GSFC/Morgan State University, Goddard Earth Sciences Technology and Research II (GESTAR II), now NASA HQ—SAGE III/ISS Program Scientist] discussed the progress of assimilating SAGE III water vapor data product into NASA’s GEOS re-analysis. Her team’s work demonstrated that while the number of solar occultation observations a day from SAGE III/ISS is about 1% of the total number of profiles observed globally by MLS, the chemical timescales of water vapor in the lower stratosphere are long enough that the SAGE III/ISS data can provide a valuable constraint on GEOS re-analysis, especially in the absence of MLS data – see Figure 4.
      Figure 4. Hovmöller diagrams of the vertical distribution of 15°S–15°N average water vapor anomalies in upper troposphere–stratosphere with water vapor relaxed to a climatology [top left] and from data assimilation of SAGE III/ISS water vapor into the Goddard Earth Observing System (GEOS) model [bottom left]. Scatter plots show water vapor mixing ratios (y-axis) with [top right] and without [bottom right] data assimilation compared independent observations from the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) data (x-axis). The ACE–FTS data were not used in data assimilation. This shows that data assimilation of SAGE data improves the agreement with ACE-FTS – especially in the lower stratosphere (400 to 500 K). Figure Credit: Emma Knowland [NASA] Melody Avery [University of Colorado, Boulder] discussed using SAGE data  and data from the Cloud–Aerosol Lidar with Orthogonal Projection (CALIOP) instrument (on the former Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission) to study thin clouds and aerosol distributions in the tropical tropopause region (TTL). Avery explained that these distributions from V5.3 of SAGE-III/ISS and V5.41 of CALIOP are shown to agree well, and CALIOP observations of cloud frequency are shown to be a sensitive metric for defining the width of the Hadley Cell near the tropical tropopause. Combining SAGE and CALIOP data produced a longer timescale to constrain and evaluate climate models that currently do not agree on how the tropical width at this altitude varies. They found that results derived using SAGE V6.0 versus V5.3 differ on the order of 2% in the TTL region.
      Pamela Wales [GESTAR II] introduced a new project that leverages SAGE III/ISS measurements to explore diurnal characteristics of O3 and nitrogen dioxide (NO2) in GEOS model products. Her team is exploring potentially using a GEOS reanalysis of stratospheric trace gases collected by MLS as a transfer standard to evaluate the consistency between the SAGE III/ISS solar and the less frequently measured lunar retrieval. They are also assessing uncertainties in stratospheric NO2 in the GEOS Composition Forecast (GEOS-CF) model using SAGE III/ISS and complementary satellite instruments. This work will inform how effectively GEOS-CF can be used in air quality studies to remove the stratospheric signal from column retrievals of NO2.
      Luis Millán [JPL] presented work on the change of stratospheric water vapor mass after the Hunga Tonga–Hunga Haʻapai (Hunga) volcano eruption in 2022. Millán found an increase (~10%) of total stratospheric water vapor – a potent greenhouse gas. Given their advanced age, MLS, ACE-FTS, and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on NASA’s Thermosphere, Ionosphere, Mesosphere, Energetics and Dynamics (TIMED) mission (Heliosphere Division), are nearing the end of their missions, leaving SAGE III/ISS as the primary instrument for monitoring the plume’s evolution. Millán discussed how the SAGE III/ISS measurements might be sufficient to observe the dispersion of the excess Hunga water vapor from stratosphere in coming years. He also discussed a 39-year plus record of stratospheric water vapor mass using the overlapping periods between SAGE II, MLS, and SAGE III/ISS.
      Ryan Stauffer [GSFC] presented the operation and outcomes of the Ticosonde balloon-borne O3 and water vapor sonde project in San Jose, Costa Rica. Ongoing since July 2005, Ticosonde has collected over 700 O3 profiles and 270 water vapor profiles for climate and pollution studies and satellite validation. Because Ticosonde is the only long-term water vapor sonde station in the tropics, the stratospheric water vapor data is vital for validation of SAGE-III/ISS and MLS profiles. Ticosonde has been used to verify the success of updated water vapor retrieval algorithms for both instruments – which now agree within a few percent up to 25 km (15 mi) altitude.
      Natalya Kramarova [GSFC] showed the comparison of O3 profile retrieved from SAGE III with those derived from the OMPS-LP sensor – which is part of OMPS on NOAA-21 – from February 2023–June 2024. Diurnal corrections using the Goddard Diurnal Ozone Climatology (which is described in a 2020 article in Atmospheric Measurement Techniques) is applied to account for differences in measurement times between SAGE III’s sunrise or sunset observations and NOAA-21 LP’s midday measurements. Once the time correction is made, results show good agreement between the two instruments in depicting vertical ozone distribution across different geographical regions (e.g., tropics and mid-latitudes) and under various conditions (e.g., near the edge of the Antarctic O3 hole in October 2023). The mean biases between NOAA-21 LP and SAGE III are typically within ±5% between ~18–45 km (11–28 mi).
      Project Team and Operations Highlights
      Michael Heitz [LaRC] showed that V5.3 and previous versions of the SAGE III/ISS data product had a noticeable – and unphysical – dip in the retrieved aerosol extinction between 520–676 nm. This dip has been referred to as the aerosol “seagull.” However, adoption of a new absorption cross-section database into the V6.0 algorithm reduced the aerosol seagull effect significantly. Kevin Leavor [LaRC] presented new developments for the SAGE III/ISS quick look website. Mary Cate McKee [LaRC] introduced a new feature of the quick look website that showcases comparisons of O3 and water vapor sonde data at over 40 stations. Sonde data is sourced from the Network for the Detection of Atmospheric Composition Change (NDACC), GSFC’s SHADOZ, and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC). Heitz explained that the comparison plots are updated continuously as new coincidences occur, providing the community with valuable insight to the quality of SAGE III/ISS data relative to this external network of ground stations. Future additions to the website include aerosol and lidar comparisons, additional plot statistics, and comparisons with novel homogenized datasets.
      Returning to a topic discussed in Jamie Nehrir’s presentation, Charles Hill [LaRC] showed that the SAGE III Disturbance Monitoring Package (DMP) correction to the data product – which was implemented beginning with V5.3 – has significantly reduced the product uncertainties caused by ISS vibrations. Approximately 7% of SAGE III occultation events are highly disturbed by mechanical vibrations, and the DMP correction has improved pointing registrations in these events significantly. The DMP’s x-axis gyroscope failed on August 8, 2023 – but this loss did not significantly affect the DMP correction to scan plane elevation. Future possible losses of either the y- or z-axes will end active correction of ISS disturbances.
      Conclusion
      Jun Wang, David Flittner, and Richard Eckman led the closing discussion that highlighted the growing interest in atmospheric composition change –  particularly due to emissions from large wildfires and volcanic eruptions in recent years. This increasing interest contrasts with the declining availability of observational data from the upper troposphere, following the retirement of CALIPSO in late 2023 and the planned decommissioning of Aura’s aging limb instruments in 2026. This gap underscores the critical importance of SAGE III/ISS data – not only for current UTS research but also for the next 5–7 years, during which no new limb measurements are planned.
      SAGE III/ISS remains essential for profiling key atmospheric constituents, including water vapor, aerosols, O₃, and NO₂. The long-term, consistent data record provided by the SAGE series of instruments since the late 1970s – including SAGE III/ISS since 2017 – has been invaluable for studying past and future changes in atmospheric composition within the UTS. To further support research and applications of SAGE data products, participants discussed the possibility of proposing a special collection of articles in AGU journals.
      Overall, the 2024 SAGE III/ISS meeting was a success. Participants received valuable updates on the status of SAGE III/ISS operations, data product calibration and validation, and new developments. The meeting also showcased the collective expertise and excellence in driving advancements in UTS research, from climate change studies to data assimilation for chemistry transport models and contributions to multi-sensor data fusion.
      Jun Wang
      University of Iowa
      jun-wang-1@uiowa.edu
      David Flittner
      Langley Research Center
      david.e.flittner@nasa.gov
      Richard Eckman
      NASA Langley Research Center
      richard.s.eckman@nasa.gov
      Emma Knowland
      NASA Headquarters
      k.e.knowland@nasa.gov
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