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NASA NTRSDate not supplied

From CMS to the U.S. Greenhouse Gas Center – Improved Tracking of Recent Changes in Co 2 and Ch 4 From NASA’s Quasi-Operational Modeling Systems

Reliable, low latency delivery of high quality global flux and concentration information is a growing but still unmet need to advance expanding measurement, monitoring, reporting, and verification efforts that underpin federal climate mitigation strategies. Here we present on progress toward developing space-based greenhouse gas (GHG) monitoring systems that can provide comprehensive information trailing real time by a matter of weeks to a few months. Through support from its Carbon Monitoring System (CMS) program, NASA has developed the capability to assimilate XCO 2 retrievals from the Orbiting Carbon Observatory, 2 (OCO 2 ) into the Goddard Earth Observing System (GEOS) Constituent Data Assimilation System (CoDAS) to create gap-filled, three-dimensional (3D) estimates of CO 2 mixing ratio. When observations are unavailable, concentration fields are further informed by observationally-informed bottom-up flux packages that incorporate remotely sensed fire, nighttime lights, and vegetation observations combined with estimates of atmospheric growth rate based on surface in situ data. The 3D nature of these datasets supports evaluation with independent aircraft data to improve confidence in satellite data, development of new regional modeling approaches, and quantification of the climate impacts of GHGs. The system has recently been expanded to assimilate XCH 4 from ESA’s TROPOspheric Monitoring Instrument (TROPOMI) instrument and is supported by companion efforts to improve delivery of estimates of bottom-up land and ocean fluxes. The quasi-operational GEOS-GHG system is contributing to the recently announced U.S. Greenhouse Gas Center (GHG Center) by delivering information on recent changes in CO 2 and CH 4 emissions and concentrations to support stakeholder and research communities. In this presentation, we provide an overview of the current system configured to support the GHG Center. We highlight examples of how this data contributes to broader NASA initiatives including the Earth Information Center, an innovative virtual and physical exhibit designed to show how NASA data helps the nation combat climate change. We conclude by discussing how innovations in CMS research can address remaining data gaps and modeling challenges to advance operational GHG monitoring in the future.

Lesley Ott↗

DOE OSTI2026

Evolution of storage monitoring – update in response to commercial and regulatory drivers

Carbon Capture and Storage (CCS) is in transition from first-of-a kind projects and research-orientated pilots to commercially-motivated applications. Monitoring results from many newly developed and planned large scale commercial projects are limited; however, it is worthwhile to assess their evolution and consider new strategies as part of an effort to assess and document best practices. Commercial monitoring is targeted to activities that comply with regulatory drivers and de-risk investments. Commercial monitoring also supports accounting that storage has occurred and is tied to project financing. It deals with long time frames and large volumes injected into multiple wells and multiple projects in favorable areas. We see developing trends toward reproducible workflows that systematically reduce risks and clarify expectations for oversight and long-term surveillance. Monitoring techniques showing increasing trends include injection zone pressure as a history-matching and compliance tool. To reduce cost and environmental impact of time-lapse seismic data collection, deploying new approaches and tools, such as use of fibre and installed sources are increasingly applied. Concern over the risk of induced seismicity by regulatory bodies and the general public has increased, which has also resulted in increased monitoring. Some techniques used in the early research phases have been sidelined or used only in restricted applications. For example, geochemical analyses in the injection zone as well as the environment are now being deployed less than it was in research-oriented programs, except in the US where it is required by the permitting process. Expectations of frequent area-wide near surface monitoring have also decreased.

25 ENERGY STORAGE↗

DOE OSTI2021

A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale

Wildfire is an ecological disturbance that disrupts the hydrological cycle. In the past few years, a record number of multiple-and-compounding fires have occurred across urban-wildland gradients in the Western United States. Changes to watershed hydrological partitioning in response to fires (infiltration, runoff, evapotranspiration) presents unprecedented challenges to “Water-in-the-West” through negative impacts to water supply and its quality, and is a direct threat to downstream communities, groundwater, and drinking water supply infrastructure. While much work is being done to advance Artificial Intelligence and Machine Learning (AI/ML) use during fires for emergency response (i.e. predict fire movement, direct evacuations), significant potential exists to use AI/ML to address three scientific grand challenges that are rarely addressed in a convergent science context: 1) how to enhance the potential resiliency of a landscape before fire(s), 2) how to cost-effectively and optimally monitor watershed changes after fires, and 3) how to predict future hydrological and biogeochemical trajectories in fire-impacted watershed given climate change. This whitepaper addresses DOE Focal Area 1: Data acquisition and assimilation enabled by machine learning, AI, and advanced methods.

58 GEOSCIENCES↗

DOE OSTI2025

Heavy metal imprints in Antarctic snow from research and tourism

Antarctica, long regarded as one of the last pristine environments on Earth, is increasingly affected by human activity. As tourism surges and scientific operations expand, air pollution from local emissions is raising new environmental concerns. Here, in this study, we analyze surface snow samples collected along a ~2,000 km transect, from the South Shetland Islands (62°S) to the Ellsworth Mountains (79°S), to map the geochemical fingerprints of aerosol deposition. We identify distinct spatial patterns shaped by crustal, marine, biogenic, and anthropogenic sources. Notably, we detect heavy metal imprints in the snow chemistry of the northern Antarctic Peninsula, where major research stations are concentrated and marine tourism traffic is most intense. Our findings shed light on the extent of the impacts from energy-intensive local activities in Antarctica, underscoring the need for enhanced environmental monitoring and sustainable management strategies in this fragile region.

Cordero, Raúl R. [Univ. of Groningen, Leeuwarden (↗

DOE OSTI2025

Governance and resilience as entry points for transforming food systems in the countdown to 2030

Due to complex interactions, changes in any one area of food systems are likely to impact—and possibly depend on—changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience indicators to be most connected to other indicators across themes, highlighting entry points for action—particularly governance action. Literature review and country case studies add context to the assessed interactions across diets, environment, livelihoods, governance and resilience indicators, helping different actors understand and navigate food systems towards desirable change.

Schneider, Kate R. [Johns Hopkins Univ., Washingto↗

DOE OSTI2024

Photovoltaic Inverter Failure Mechanism Estimation Using Unsupervised Machine Learning and Reliability Assessment

This article introduces a data-driven approach to assessing failure mechanisms and reliability degradation in outdoor photovoltaic (PV) string inverters. The manufacturer's stated PV inverter lifetime can vary due to the impact of operating site conditions. To address limitations in degradation estimation through accelerated testing, condition monitoring, or degradation modeling, we propose a machine learning (ML) oriented approach. Utilizing data from a 1.4 MW PV power plant operational since 2016, with 46 string PV inverters tied to the grid, we employ the unsupervised one-class support vector machine ML technique to analyze inverter and sensor data, capable of classifying humidity cycling and temperature fluctuations as dominant failure mechanisms. Utilizing the anomaly alert relationship and alert details specific to the inverter, the level of PV inverter output is considered as its availability or available reliability. Subsequently, a continuous Markov model is applied to six-month alert data, revealing an average stated reliability of 20% after 20 years of continuous operation. These results support recommendations for time-bound preventive measures to enhance PV inverter reliability under diverse outdoor conditions. Furthermore, the approach provides a nondestructive, top–down, and generalized method for analyzing any commercial PV inverter exposed to outdoor conditions, contingent on the availability of relevant data.

14 SOLAR ENERGY↗

DOE OSTI2024

Impact of Vegetation Assimilation on Flash Drought Characteristics across the Continental United States

Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.

Drought↗

DOE OSTI2026

Effects of White Sturgeon (Acipenser transmontanus) Passage Through a Novel Hydropower Turbine and Implications for Long-Term Survival

Researchers from UC Davis and Natel Energy compared a novel "Restoration Hydropower Turbine" against a conventional turbine design to evaluate their physical and physiological effects on juvenile White Sturgeon. The study found that the new design achieved over 99% survival across all test speeds, whereas the conventional turbine caused significant mortality and injury due to blade strikes. Furthermore, long-term monitoring showed that fish passing through the new turbine experienced no meaningful or persistant negative impacts on their growth, behavior, or stress levels.

13 HYDRO ENERGY↗

DOE OSTI2024

How Earth System Models Can Inform Key Dimensions of Marine Food Security in the Alaskan Arctic

The Arctic is home to several groups of Indigenous Peoples, each with distinct ways of interacting with their environment and ways of life. Arctic, Indigenous Peoples’ food sovereignty is tightly linked with food security. Subsistence harvesting activities provide nutritious and culturally vital foods for Alaska Native households and communities. Climate change is causing rapid and more unpredictable shifts in environmental conditions that impact three of the key aspects of food security, availability, stability, and accessibility. While communities monitor the abundance and health of food webs and environments as part of subsistence harvest practices, anticipating major transformations and changes in these systems is challenging. We explored the potential of Earth System Model output in helping anticipate or project physical or ecosystem changes relevant to Alaska Indigenous peoples’ food security needs. Through examples of model products, that provide measures of accessibility and availability of marine resources, we show that modern models, such as the Energy Exascale Earth System Model presented here, can provide estimates of a broad suite of variables relevant to food security. We investigate how Earth System Model output could contribute to exploring questions related to aspects of Arctic food security such as accessibility and availability and highlight present model shortcomings that, if addressed, would move Earth System Models closer to being a useful tool for understanding environmentally driven changes to the availability and accessibility of harvestable food resources. Our example model-derived food security indicators illustrate how Earth System Model output could be combined with relevant, non-model, information sources; These model products are meant only as a starting point and a tool for engaging community members and to present, in an accessible way, the model’s potential utility, or current lack thereof, to rights holders and stakeholders concerned about food security. We are hopeful that with example products in hand, additional model development efforts will have a higher likelihood of success in achieving an iterative discussion with stakeholders regarding feasible and desired products.

Gibson, Georgina A.↗

NASA NTRS1990

Photovoltaic array space power plus diagnostics experiment

The objective of the Photovoltaic Array Space Power Plus Diagnostics (PASP Plus) experiment is to measure the effects of the interaction of the low- to mid-altitude space environment on the performance of a diverse set of small solar-cell arrays (planar and concentrator, representative of present and future military technologies) under differing conditions of velocity-vector orientation and simulated (by biasing) high-voltage operation. Solar arrays to be tested include Si and GaAs planar arrays and several types of GaAs concentrator arrays. Diagnostics (a Langmuir probe and a pressure gauge) and a transient pulse monitor (to measure radiated and conducted EMI during arcing) will be used to determine the impact of the environment on array operation to help verify various interactions models. Results from a successful PASP Plus flight will furnish answers to important interactions questions and provide inputs for design and test standards for photovoltaic space-power subsystems.

Guidice, Donald A.↗

NASA NTRS1991

Overview of space propulsion systems for identifying nondestructive evaluation and health monitoring opportunities

The next generation of space propulsion systems will be designed to incorporate advanced health monitoring and nondestructive inspection capabilities. As a guide to help the nondestructive evaluation (NDE) community impact the development of these space propulsion systems, several questions should be addressed. An overview of background and current information on space propulsion systems at both the programmatic and technical levels is provided. A framework is given that will assist the NDE community in addressing key questions raised during the 2 to 5 April 1990 meeting of the Joint Army-Navy-NASA-Air Force (JANNAF) Nondestructive Evaluation Subcommittee (NDES).

Generazio, Edward R.↗

NASA NTRS2016

Automated Segmentation and Classification of Coral using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the world's coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85%. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗

NASA NTRS2018

Robust Bonding Through Process Control

Process control has been proven to be the most reliable means of safeguarding the quality of adhesive bonds according to Federal Aviation Administration (FAA). A method for implementing process control for reduction in risk in a bonded joint fabrication process is demonstrated in this study using a selected bonding system. The stepwise method included risk analysis to identify defects with the highest impact and likelihood to occur, evaluation of various pre-bond surface analysis tools to monitor for the selected defects, and demonstration of the benefits of in-process monitoring utilizing threshold limits determined from bond performance tests. The bonded system selected for investigation was an aerospace carbon fiber epoxy composite substrate surface prepared with random orbital sanding using 180 grit aluminum oxide sand paper. A series of portable, pre-bond surface analysis tools were investigated for their ability to be used for in-line bond process control. Results and threshold limits are presented from roughness, ballistic water contact angle (WCA), color, gloss, and Fourier transform infrared spectroscopy (FTIR) surface analysis tools. Results demonstrated how in-process inspection methods can be used to ensure quality of a surface preparation for a selected bonding system. A framework is provided for implementation of bond process control for robust bonding.

Kutscha, Eileen O.↗

NASA NTRSDate not supplied

Aerosol Physics for the Lunar Environment: Equations for Lunar Dust Control and Mitigation Technologies

Sticky and jagged dust was ubiquitous during the Apollo missions, causing soiling and abrasion problems with seals, coatings and equipment, in addition to eye irritation and breathing discomfort in the cabin. The Artemis Program of NASA aims to place astronauts on the lunar surface by 2024 and establish a sustainable presence in the following decade. Returning to the Moon requires controlling and mitigating the dust which will be inevitably brought inside the cabins. The state-of-the-science for effective collection of aerosols is based on dynamics of airborne particulate matter under terrestrial conditions. However, the governing physics does not apply to extra-vehicular activity in the hard-vacuum lunar condition. For example, the substantial difference in gravity will dictate particle transport both outside and inside the cabin. In this study, we revisited the aerosol physical phenomena that are assumed in the design of Earth-based aerosol instruments and extend the applicability to different scenarios in lunar missions. As shown, long-term lunar habitats, transfer vehicles to lunar orbital platforms, and low pressure cabin atmospheres have different aerosol dynamics. In all cases, the impact of dust control strategies using gravitational, electrical, and thermal techniques for various mitigation and monitoring hardware is explored. The guidelines provided through this study will show how terrestrial aerosol equipment can translate to lunar dust applications.

Nima Afshar-Mohajer↗

NASA NTRSDate not supplied

Early Radiometric Performance of Landsat-9 Thermal Infrared Sensor

Landsat-9, launched on September 27, 2021, carries the Thermal Infrared Sensor (TIRS). The Landsat-9 TIRS is a close copy of the Landsat-8 TIRS instrument; it is a two spectral-band, pushbroom sensor with three Sensor Chip Assemblies (SCAs) that cover the 15-degree field-of-view. The primary radiometric change between the instruments is the addition of baffling in the Landsat-9 TIRS telescope to mitigate the stray light issue that has impacted the radiometric quality of Landsat-8 TIRS. The on-orbit radiometric performance is monitored using the on-board variable temperature blackbody and views of deep space. Maneuvers to look at and around the moon have provided an assessment of the stray light. The absolute calibration is monitored by vicarious calibration techniques by teams at NASA/Jet Propulsion Lab and the Rochester Institute of Technology. Landsat-9 completed a three-month commissioning phase in January 2022 and has been operational since February 2022. The instrument has demonstrated excellent radiometric performance, as assessed from the on-orbit measurements. The TIRS instrument is radiometrically stable to 0.1% within a power cycle, and has noise levels below 0.1K. The lunar scans and the vicarious calibration data provide evidence that the stray light has been effectively mitigated.

Landsat-9↗

NASA NTRSDate not supplied

End-User Assessment of the NASA SPoRT Lightning AI Product

The NASA Short-term Prediction Research and Transition (SPoRT) Center has begun to develop products to address operational challenges and enhance safety during the lifecycle of lightning activity, and conduct evaluations in concentrated R2O/O2R efforts. The product evaluated for this study is Lightning Artificial Intelligence (A.I.), which predicts the probability of lightning out to 15 minutes in advance and spatially maps it using filled color contours. Lightning A.I. uses reflectivity, differential reflectivity, and correlation coefficient data from a subset of radars within the NEXRAD network to generate lightning probabilities at approximately 2 km resolution. The domains, spanning 145 x 145 km are centered over NASA-affiliated centers across the CONUS and use radar data which are closest in proximity. Lightning A.I. was developed with intended use by emergency managers at NASA centers. However, National Weather Service (NWS) offices are also often tasked with monitoring and forecasting the threat for lightning within their County Warning Areas for various impact-based decision support services. These forecasts are typically provided for aviation operations and large-scale, outdoor events, which may have varying safety requirements for decision-making based on lightning proximity and recency. The NASA SPoRT center conducted an assessment of the operational uses of Lightning A.I by various collaborative NWS Offices from late July into early September. This assessment included feedback from participants on both the product itself as well as its accessibility within the new, interactive NASA SPoRT Lightning Viewer. This presentation will include background information about Lightning A.I. and highlight results from this assessment. The feedback from the assessment will be used to inform research on any necessary modifications to this and future lightning products to assist end users.

Kelley Murphy↗

NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

This work presents an analytical approach to address situations in drone operations when strategic coordination alone cannot ensure safety, requiring support from the Conformance Monitoring for Situation Awareness service and potentially other deconfliction services. This study focuses on evaluating the impact of off-nominal operations on both UA-to-UA collision risk and ground injury risk. It first establishes a relationship between the likelihood of off-nominal flights and the effectiveness of strategic coordination. The analysis then derives operational conditions where strategic coordination alone is insufficient to meet target safety requirements, particularly when the likelihood of off-nominal operations exceeds a certain threshold. The results indicate that higher operational tempo or density requires a lower likelihood of offnominal operations for strategic coordination to remain effective and meet safety targets. These findings provide quantitative operational thresholds for when CMSA services are necessary. This work contributes to the safety case for both strategic coordination and CMSA services, offering analysis on when CMSA should be employed to support strategic coordination in achieving the desired safety levels. It also establishes a framework for incorporating other deconfliction services in future safety case analyses.

Strategic deconfliction↗

NASA NTRSDate not supplied

Safety Benefit Analysis of Conformance Monitoring for Situation Awareness in UTM

Strategic deconfliction↗