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NASA NTRSDate not supplied

BLOOD-BASED MULTI-SCALE MODEL FOR CANCER RISK FROM GCR IN GENETICALLY DIVERSE POPULATIONS

OBJECTIVES AND METHODS This project addresses the challenge of understanding and predicting individual radiation sensitivity by integrating genetics, demographics and biomarker characteristics across species (mice and humans). We hypothesize that ex vivo DNA repair response to GCR components is a central determinant of cancer risk from space radiation and can serve as a biomarker of radiation risk in combination with genetics. Automated image quantification of 53BP1+ radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET in non-immortalized primary skin fibroblasts derived from 76 mice across 15 strains (5 inbred reference strains and 10 collaborative-cross strains) exposed to X rays (0.1, 1 and 4 Gy), 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100μm2), as well as in peripheral blood mononuclear cells (PBMCs) from 768 healthy donors (matched ethnicity, 50/50 male/female, 18-70 years old) exposed to gamma rays (0.1 and 1 Gy), 350 MeV/n 28Si, 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100μm2). QUANTIFICATION OF 53BP1+ FOCI IN VITRO AND ASSOCIATIONS TO IN VIVO RADIATION SUSCEPTIBILITY IN 15 MOUSE STRAINS We reported in vitro repair kinetic and repairable fractions of RIF for the 15 mouse strains and introduced a mathematical model for RIF as a function of time, dose and LET. We noted that the metabolic activity of cells modulates the RIF response, and we introduced the open access tool terRIFic (Tool for Enhanced Results of RIF In Cells, https://radbiolab.shinyapps.io/terrific/) to correct for such bias using confluence level. Notably, at 4h post-irradiation, RIF/Gy decreased with dose or LET: as the dose or LET increases, so does the proximity of DNA double-strand-breaks (DSB) and our data suggest that proximal DSBs are brought together inside isolated RIF for repair. The RIF/Gy trend was inverted at 24h, suggesting RIF with high DSB content are more difficult to repair. We showed that in vitro metrics correlate with in vivo measurements in the same 15 mouse strains, such as survival levels of immune cells or spontaneous cancer incidence, suggesting a relationship between the efficiency of DSB repair and cancer risk or radiation toxicity. In addition to the efficiency of repair and persistent RIF, the amount of spontaneous foci before irradiation was also found to be strain dependent. Finally, we performed genome-wide association study in the same 15 mouse strains using all RIF phenotypes measured in vitro, identifying genes of interest and validating RIF as an ideal biomarker for individual radiation sensitivity. BASELINE 53BP1+ FOCI PREDICTS INDIVIDUAL HUMAN RESPONSE TO GCR COMPONENTS Based on the analysis of radiation responses of 576 donor PBMCs (using quantification of 53BP1+ foci, oxidative stress and cell death), we observed a wide variability of subject- and LET-dependent radiation responses, with radiation-induced DNA repair foci increasing with LET, though oxidative stress being notably reduced by high-LET irradiation, potentially due to a switch between hydrogen peroxide and oxygen radical-based mechanisms. We identified a relationship between few spontaneous DNA foci at baseline and increased DNA repair after irradiation, accompanied by an alteration in immunoregulatory cytokine secretion, which might be adapted as biomarkers to predict ionizing radiation sensitivity. Among demographic variables, only latent cytomegalovirus infection and age were predictive of high baseline foci formation. Finally, we have performed low-throughput whole genome sequencing of all samples and are currently in the process of identifying the genes and pathways associated with low and high-LET ionizing radiation sensitivity in humans.

53BP1↗

NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

NASA’s GeneLab helps scientists understand how the fundamental building blocks of life – DNA, RNA, proteins, and metabolites – change from exposure to the space environment including microgravity and cosmic radiation exposure. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data (collectively known as omics data) alongside essential metadata describing each spaceflight and space-relevant experiment. The open-access GeneLab repository currently consists of over 300 omics datasets generated by biological experiments, involving various model organisms, that are relevant to spaceflight. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab has started processing and analyzing these datasets to generate differential gene expression data and identify biological and physiological pathways that are dysregulated as a result of spaceflight. To aide GeneLab’s efforts to harmonize and democratize space-relevant omics data, over 130 scientists have joined one of four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG) and together helped develop and adopted standard data analysis workflows for all data types available in GeneLab. Currently, the GeneLab Data System includes a data repository with federated search capability, an online controlled-access toolshed powered by "Galaxy" for users to process data with vetted standard workflows, a workspace for data sharing, a data submission portal, and the ability to browse and visualize transcriptomics processed data. The user interface was designed to be accessible to a broad variety of users, including high school and college students who can use it to learn about omics data analysis and space biology. The visualization portal enhances GeneLab’s ability to democratize omics data by removing the need for bioinformatics expertise to interpret transcriptomics data hosted on GeneLab. This presentation will provide an over-view of NASA’s GeneLab including how to navigate the GeneLab Data System and will conclude by providing resources for opportunities to work with GeneLab and NASA at large.

Amanda M Saravia-Butler↗

NASA NTRS2022

Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

crop phenology↗

NASA NTRSDate not supplied

GL4U: Training the next generation of bioinformaticians, one omics datatype at a time

Spaceflight modifies gene expression in every organism examined to date, including humans. Understanding how these gene expression changes affect physiology is crucial for the development of countermeasures to enable long-duration manned missions. NASA’s GeneLab project provides researchers open access to multi-omics data, including genetic and gene expression data, from spaceflight experiments that can be mined to understand the effects of spaceflight on biological systems. To ensure new knowledge generation through data re-use, it is important to maximize the number of scientists who utilize GeneLab data. Training students on the GeneLab platform is the best way to create long-term adopters of this NASA database and its tools. Turning students into future instructors and advocates will also accelerate the dissemination of these data and tools to the broader scientific community. Therefore, in collaboration with the GeneLab Educational Working Group (EWG), GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. During the bootcamp, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U direct training pilot program was conducted in June 2021 in collaboration with USRA and San Jose State University (SJSU). During the pilot, SJSU students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrates the capacity of GL4U for training young scientists and encouraging data re-use.

Jonathan Matthew Galazka↗

NASA NTRSDate not supplied

A Method for Validating Causal Diagrams of Human Health Risk in Space Flight

The complexity of cause-and-effect relationships between spaceflight hazards and resulting health conditions clouds understanding of the totality of human system risk in space. In response, NASA has introduced Directed Acyclic Graphs (causal diagrams) into the human systems risk management process. These diagrams allow for a common understanding of the mechanisms that lead from unique hazards of spaceflight to the health outcomes important to agencies and astronauts. However, the paucity of available biomedical data from spaceflight creates a need for methods of validating causal models that can accommodate data from spaceflight model analogs. Here we outline one approach utilizing open-access rodent bone datasets from the Ames Life Sciences Data Archive. The properties of directed acyclic graphs themselves can provide an epistemological and statistical framework for validation of a priori causal representations of human system risk in space flight. The assumed causal connections on the graph creates sets of logical implications: variables that – if the causal diagram is correct – should be correlated, as well as sets that should be conditionally independent. By testing these implied correlations and conditional independencies both statistically and heuristically, we can provide evidence for or against specific causal pathways on the causal diagram. In addition to validation of expert-generated causal diagrams, machine learning techniques can learn the most likely structure of a causal diagram from a given dataset. Comparison with and reconciliation between machine-learned causal diagrams and expert-generated diagrams is another technique for challenging assumptions and improving our understanding of causal mechanisms. Accurately representing complex causation is essential to systemic understanding of human health risks in space travel. Having a robust system of validating causal diagrams helps us arrive at more accurate representations of causal systems. This process will be integral to developing the countermeasures necessary for extended exploration of the moon and Mars.

Robert Reynolds↗

NASA NTRSDate not supplied

Biological Insights at the Interface of Multiple Arabidopsis Legacy Datasets

The NASA GeneLab database includes an open-access collection of datasets yielded by space biology experiments. Six Gene Lab Data Sets (GLDS’s) performed in Arabidopsis were selected for analysis (7/17/44/121/205/213), all of which included transcriptome data from spaceflight and ground control environments. Hardware, ecotype, environmental conditions, and other experimental conditions varied, allowing the observations of overarching gene expression impacts of microgravity on plant life without focusing on effects of specific experimental conditions. Using GeneLab pre-processed datasets as the basis for the study, RNA microarray data were analyzed to identify genes that showed altered expression in microgravity when compared to control samples for each individual GLDS. All differentially expressed genes were compared to locate differentially expressed genes common between spaceflight experiments. The most noteworthy result is that not one gene shared differential expression among the six GLDS’s. However, gene expression was not influenced randomly by the microgravity environment, as there were several gene ontology terms that were significantly enriched across all experiments. These included 20 significantly enriched biological processes, and although the genes which enriched each term varied, there were many cases of specific genes common to clusters of multiple GLDS’s. Gene expression such as NAC92 and ERF011 or membrane structural element FFP6 provide insight and direction toward understanding the plant response to spaceflight. Characterizing these common processes and the shared differentially expressed genes has demonstrated potential targets for further study to understand and modulate the biological response of plants in microgravity. Life on Earth has never been subjected to the absence of gravity as a selective pressure, so observing how life forms react to a microgravity environment could provide insight to our shared fundamental biological processes. It is also feasible that the genetic modification of specific genes linked to the microgravity response could improve health and yield of space crops.

Joseph Emhof↗

NASA NTRSDate not supplied

AstroAmpSeq: Microbial Bioinformatics Education with NASA GeneLab’s Amplicon Pipeline

The prevalence and importance of large sequencing datasets in microbiology has led to a movement to share microbial ecology experimental data through open-access databases. This is particularly true of experiments that are difficult to replicate, such as those conducted in the spaceflight environment and shared via NASA GeneLab. It is now possible and indeed valuable for students to access and re-analyze these shared datasets for educational and research purposes. To provide students with experience utilizing microbial bioinformatics tools, GeneLab for Colleges and Universities (GL4U) has designed AstroAmpSeq, a week-long, virtually implemented project-based learning (PBL) minicourse to instruct undergraduate students on 16S amplicon sequencing. AstroAmpSeq was created to be accessible to students without prior bioinformatics or microbial ecology experience. During the minicourse students work in teams to process, analyze, and visualize a subsample of GeneLab dataset GLDS-280 using GeneLab’s standard amplicon processing pipeline, which is based in R. Students develop a hypothesis related to the dataset then generate and analyze figures to evaluate their hypothesis. Formative assessment of student learning is determined via pre- and post-evaluations, peer feedback, and self-reflection. Project and presentation rubrics serve as a summative assessment of student learning. GL4U AstroAmpSeq not only meets American Society for Microbiology Curriculum Guidelines, but also incites student interest in research by an inquiry-based approach and can be made part of a larger semester-long curriculum. GL4U AstroAmpSeq raises awareness of space microbiology and bioinformatics as a field and career path among undergraduates. Further, by using a GeneLab dataset and nesting microbiology techniques into the real-world application of space biology, AstroAmpSeq enforces deeper and longer-lasting student learning.

microbiology↗

NASA NTRSDate not supplied

Enabling Biological Discovery Through Biospecimen Sharing: The Nasa Biological Institutional Scientific Collection

Understanding biological impacts from spaceflight hazards and the subsequent development of countermeasures are a high priority to enable humanity to venture back to the Moon, and then to Mars and beyond. Experiments have been conducted with model organisms flown to space and analogous investigations terrestrially, to identify biological mechanistic impacts from spaceflight hazards and to develop mitigation countermeasures, thus contributing towards basic and applied science goals. However, sending organisms into space is a costly endeavor. To maximize scientific return, all biospecimens not required by spaceflight-relevant Principal Investigators are harvested, preserved, and archived in the NASA Biological Institutional Scientific Collection (NBISC). Biospecimens are collected and preserved according to well-established standard operating procedures to maintain scientific quality and are available on-request by the international scientific community. NBISC currently stores over 32,000 biospecimens from Shuttle, International Space Station, and ground-based space analog investigations. Tissue sharing has resulted in at least 33 publications since 2011 and 51 requests since 2016. Many requests for NBISC biospecimens come from first-time investigators who subsequently submit grants as their point-of-entry into the field of spaceflight biology and health. The NBISC biorepository is part of the NASA ‘Open Science for Life in Space’ collaborative group of projects, which includes NASA Genelab, the Space Biology Program’s Biospecimen Sharing Program, Physical Sciences Informatics, and the Ames Life Sciences Data Archive. NBISC biospecimens have been awarded to NASA Genelab, who then generated various open access science ‘omics datasets through the GeneLab Sample Processing laboratory, with resulting data widely used for biological study. Other NBISC biospecimen awards have led to studies on fecal microbiome analysis, DNA damage analysis using single-cell DNA sequencing, enzymatic-pathway identification involved in spaceflight muscle atrophy, and characterization of ocular morphological changes. Of note, NBISC is expanded to include a new Space Microbial Culture Collection (SMCC) for the collection, identification, documentation, long-term preservation, and distribution of space-related microbial isolates.

Biospecimens↗

NASA NTRS2021

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

crop yield↗

NASA NTRS2020

Terrain Relative Navigation for Guided Descent on Titan

Titan’s dense atmosphere, low gravity, and high winds at high altitudes create descent times of >90 minutes with standard entry/descent/landing (EDL) architectures and result in large unguided landing ellipses, with 99% values of 110x110 km and 149x72 km in recent Titan lander proposals. Enabling precision landing on Titan could increase science return for the types of missions proposed to date and make additional types of landing sites accessible, opening up new possibilities for science investigations. Precision landing on Titan has unique challenges, because the hazy atmosphere makes it difficult to see the surface and because it requires guided descent with divert ranges that are one to two orders of magnitude larger than needed for other target bodies, i.e. up to on the order of 100 km. It is conceivable that such a divert capability could be provided economically by a parafoil or other steerable aerodynamic decelerator deployed several 10s of km above the surface. The long descent times lead to large inertial navigation errors, hence a need for terrain relative navigation (TRN). This would require a TRN capability that can operate at such altitudes, despite challenges of seeing the surface sufficiently clearly and of depending on map products that are two orders of magnitude lower in spatial resolution than those for Mars and airless bodies. We then develop algorithms for map matching and feature tracking with descent images and test these with synthetic images created from Cassini/Huygens data sets and our radiative transfer model. We also introduce new possibilities for TRN based on the potential to discriminate some specific types of terrain onboard in descent imagery, such as lake vs adjacent ground and dune vs interdune. We use sensor measurement noise models in simulations of state estimation with an extended Kalman filter that includes coordinates of a set of tracked features in the state vector. Case studies were done for two notional landing sites, one in a site with only dry ground and one in a Titan lake district. In both cases, the filter error model shows 3 position error at touchdown on the order of 2 km. More work is needed to validate these results with higher fidelity camera models and larger data sets, but this is very promising.

Matthies, Larry↗

NASA NTRSDate not supplied

GL4U: Bioinformatics training for students and educators using space omics data

Amanda Marie Saravia-butler↗

NASA NTRSDate not supplied

Earth System Digital Twins (ESDT) Technology for NASA Earth Science

For NASA's Advanced Information Systems Technology (AIST) Program, an Earth System Digital Twin (ESDT) is defined as an interactive and integrated multidomain, multiscale, digital replica of the state and temporal evolution of Earth systems. It dynamically integrates: relevant Earth system models and simulations; other relevant models (e.g., related to the world's infrastructure); continuous and timely (including near real time and direct readout) observations (e.g., space, air, ground, over/underwater, Internet of Things (IoT), socioeconomic); long-time records; as well as analytics and artificial intelligence tools. Effective ESDTs enable users to run hypothetical scenarios to improve the understanding, prediction of and mitigation/response to Earth system processes, natural phenomena and human activities as well as their many interactions. An ESDT is a type of integrated information system that, for example, enables continuous assessment of impact from naturally occurring and/or human activities on physical and natural environments. AIST ESDT strategic goals are to: 1. Develop information system frameworks to provide continuous and accurate representations of systems as they change over time; 2. Mirror various Earth Science systems and utilize the combination of Data Analytics, Artificial Intelligence, Digital Thread, and state-of-the-art models to help predict the Earth’s response to various phenomena; 3. Provide the tools to conduct "what if" investigations that can result in actionable predictions. The AIST ESDT thrust is developing capabilities toward the development of future digital twins of the Earth or of subcomponents of the Earth. This will enable the development of an overarching framework that will integrate New Observing Strategies (NOS) to enable new observation measurements, i.e., multi-source, coordinated, dynamic and responsive to needs and requests defined by Analytic Collaborative Frameworks (ACF) that enable agile science investigations fusing and analyzing very large amounts of diverse data. NOS and ACF capabilities along with open access to various science, infrastructure and human data, interconnected modeling, data assimilation, simulations, surrogate modeling, high-performance computing and advanced visualization, will define a powerful framework that could be utilized for local, regional or global and/or thematic digital twins. This presentation will describe a general overview of the AIST ESDT vision including prior work done in the areas of NOS and ACF as well as current and upcoming ESDT projects.

Jacqueline Le Moigne↗

NASA NTRSDate not supplied

GL4U: Using Space Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. In June 2022, GL4U partnered with Jet Propulsion Laboratory’s (JPL) Planetary Protection Center of Excellence to conduct the indirect training pilot program by training educators at historically black colleges and universities (HBCUs) and minority serving institutions (MSIs). During the educator pilot, participants received materials, training, and will be provided the necessary compute resources to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U training program provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. Pre- and post-bootcamp surveys were completed by all participants and show the overwhelming success of the bootcamps.

Amanda M. Saravia-Butler↗

NASA NTRSDate not supplied

GL4U: Bioinformatics Training for Students and Educators Using Space Omics Data

Amanda Marie Saravia-butler↗

NASA NTRS2023

Greenland and Canadian Arctic Ice Temperature Profiles Database

Here, we present a compilation of 95 ice temperature profiles from 85 boreholes from the Greenland ice sheet and peripheral ice caps, as well as local ice caps in the Canadian Arctic. Profiles from only 31 boreholes (36 %) were previously available in open-access data repositories. The remaining 54 borehole profiles (64 %) are being made digitally available here for the first time. These newly available profiles, which are associated with pre-2010 boreholes, have been submitted by community members or digitized from published graphics and/or data tables. All 95 profiles are now made available in both absolute (meters) and normalized (0 to 1 ice thickness) depth scales and are accompanied by extensive metadata. These metadata include a transparent description of data provenance. The ice temperature profiles span 70 years, with the earliest profile being from 1950 at Camp VI, West Greenland. To highlight the value of this database in evaluating ice flow simulations, we compare the ice temperature profiles from the Greenland ice sheet with an ice flow simulation by the Parallel Ice Sheet Model (PISM). We find a cold bias in modeled near-surface ice temperatures within the ablation area, a warm bias in modeled basal ice temperatures at inland cold-bedded sites, and an apparent underestimation of deformational heating in high-strain settings. These biases provide process level insight on simulated ice temperatures.

Greenland↗

NASA NTRSDate not supplied

Data-Driven Art

In Fall 2023, Katie Baldwin (UAH) and Helen Parache (NASA) will follow up on their pilot activity from the spring that focused on collaboration between the arts and sciences at the UAH Art Department. Ms. Parache will present on open access data and artists that incorporate scientific data in their work, e.g. Tali Weinberg and Sarah Bryant (University of Alabama). Ms. Baldwin will demonstrate printmaking and mark making techniques. The students in Ms. Baldwin’s Book Arts class will participate in a series of generative activities and engage with data to develop content. The focus on the Art Department stems from the importance of Art as a cultural pillar. Tapping into the communication and social relevance of art could be an avenue to pursue Environmental Justice goals of interest to NASA. A creative perspective on data can bring about creative questions and solutions. The workshop incorporates changes based on feedback from the spring workshop.

data science↗

NASA NTRS2023

Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An Assessment of Amospheric Correction Processors for Landsat 8 and Sentinel-2 Over Land

The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD < 0.2) with uncertainties <0.08, while the overall uncertainty values were typically 0.23 ± 0.15. Better performances were observed for WV retrievals with >90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals.

Atmospheric correction↗

DOE OSTI2019

Large-Scale Grid Models: Synthetic Data and Co-Simulation

Power grid modernization and wide-spread distributed energy resource (DER) integration are driving an explosion of interest in advanced algorithms and new optimization schemes. However, there has historically been a lack of open-access realistically sized and detailed datasets-particularly for the distribution system-and simulation tools able to model these systems. This talk will present recent advances with very large-scale (>>1M nodes) distribution data sets and the use of co-simulation to tractably model large integrated transmission-distribution grids and their interactions with new controls/optimization.

24 POWER TRANSMISSION AND DISTRIBUTION↗