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NASA NTRS2014

Updated Performance Evaluation of the ISS Water Processor Multifiltration Beds

The ISS Water Processor Assembly (WPA) produces potable water from a waste stream containing humidity condensate and urine distillate. The primary treatment process is achieved in the Multifiltration Beds, which include adsorbent media and ion exchange resin for the removal of dissolved organic and inorganic contaminants. Two Multifiltration Beds (MF Beds) were replaced on ISS in July 2010 after initial indication of inorganic breakthrough of the first bed and an increasing Total Organic Carbon (TOC) trend in the product water. The first bed was sampled and analyzed Sept 2011 through March 2012. The second MF Bed was sampled and analyzed June 2012 through August 2012. The water resident in the both beds was analyzed for various parameters to evaluate adsorbent loading, performance of the ion exchange resin, microbial activity, and generation of leachates from the ion exchange resin. Portions of the adsorbent media and ion exchange resin were sampled and subsequently desorbed to identify the primary contaminants removed at various points in the bed in addition to microbial analysis. Analysis of the second bed will be compared to results from the first bed to provide a comprehensive overview of how the Multifiltration Beds function on orbit. New data from the second bed supplements the analysis of the first bed (previously reported) and gives a more complete picture of breakthrough compounds, resin breakdown products, microbial activity, and difficult to remove compounds. The results of these investigations and implications to the operation of the WPA on ISS are documented in this paper.

Bowman, Elizabeth M.↗

NASA NTRS2014

Designing Liquid Rocket Engine Injectors for Performance, Stability, and Cost

NASA is using computational fluid dynamics (CFD) simulations to lower the cost and increase the performance and stability of fuel injector designs for next-generation liquid rocket engines (LREs). The Loci-STREAM CFD code is used to simulate the complex combustion processes inside the LRE. These analyses enable efficient evaluation of the performance and stability characteristics of injector design concepts, while decreasing reliance on the costly test-fail-fix cycle of traditional design approaches. These injector simulations were recently employed as a key part of the design process for an Advanced Booster concept for NASA's heavy-lift Space Launch System (SLS).

Westra, Douglas G.↗

NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform With Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks [1]. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration [2]. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera [3], Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) [5] is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools [5, 7]. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [8]. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers [5,6]. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗

NASA NTRSDate not supplied

Global 3D Data Visualization and Analysis Platform with Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera, Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗

NASA NTRSDate not supplied

The Nasa SRA Process as It Relates to Open-Source Workflows Developed for GeneLab Data Processing

To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.

Software Release Authorization↗

NASA NTRSDate not supplied

The NASA SRA Process as it Relates to Open-Source Workflows Developed for GeneLab Data Processing

Software Release Authorization↗

NASA NTRS1998

Numerical Simulations of a Lobed Fuel Injector

Numerical modeling of the nonreactive mixing processes associated with a lobed fuel injector in a coflowing air stream is presented. The lobed fuel injector is a device which generates strong streamwise vorticity, producing locally high strain rates which can enhance the molecular mixing of reactants while delaying ignition in a controlled manner. Vortex element modeling is used to simulate flow field evolution and fuel element mixing characteristics for this lobed fuel injector. Quantitative predictions for vorticity generation and qualitative results for streamwise rollup compare well qualitatively with recent experimental investigations of this flow field [Smith et al, Phys. Fluids 9, 667 (1997)]. Parametric studies of the effects of lobe amplitude-to-wavelength ratio, lobe angle, and lobe shape for given flow conditions suggest that geometrical features may be optimized to enhance mixing and control reaction processes.

Strickland, J. H.↗

NASA NTRS2011

First Results from ARTEMIS, a New Two-Spacecraft Lunar Mission: Counter-Streaming Plasma Populations in the Lunar Wake

We present observations from the first passage through the lunar plasma wake by one of two spacecraft comprising ARTEMIS (Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moon's Interaction with the Sun), a new lunar mission that re-tasks two of five probes from the THEMIS magnetospheric mission. On Feb 13, 2010, ARTEMIS probe P1 passed through the wake at 3.5 lunar radii downstream from the Moon, in a region between those explored by Wind and the Lunar Prospector, Kaguya, Chandrayaan, and Chang'E missions. ARTEMIS observed interpenetrating proton, alpha particle, and electron populations refilling the wake along magnetic field lines from both flanks. The characteristics of these distributions match expectations from self-similar models of plasma expansion into vacuum, with an asymmetric character likely driven by a combination of a tilted interplanetary magnetic field and an anisotropic incident solar wind electron population. On this flyby, ARTEMIS provided unprecedented measurements of the interpenetrating beams of both electrons and ions naturally produced by the filtration and acceleration effects of electric fields set up during the refilling process. ARTEMIS also measured electrostatic oscillations closely correlated with counter-streaming electron beams in the wake, as previously hypothesized but never before directly measured. These observations demonstrate the capability of the comprehensively instrumented ARTEMIS spacecraft and the potential for new lunar science from this unique two spacecraft constellation.

Halekas, J. S.↗

NASA NTRS2014

First Results from ARTEMIS, A New Two-Spacecraft Lunar Mission: Counter-Streaming Plasma Populations in the Lunar Wake

We present observations from the first passage through the lunar plasma wake by one of two spacecraft comprising ARTEMIS (Acceleration, Reconnection, Turbulence, and Electrodynamics of the Moon's Interaction with the Sun), a new lunar mission that re-tasks two of five probes from the THEMIS magnetospheric mission. On Feb 13, 2010, ARTEMIS probe P1 passed through the wake at approximately 3.5 lunar radii downstream from the Moon, in a region between those explored by Wind and the Lunar Prospector, Kaguya, Chandrayaan, and Chang'E missions. ARTEMIS observed interpenetrating proton, alpha particle, and electron populations refilling the wake along magnetic field lines from both flanks. The characteristics of these distributions match expectations from self-similar models of plasma expansion into vacuum, with an asymmetric character likely driven by a combination of a tilted interplanetary magnetic field and an anisotropic incident solar wind electron population. On this flyby, ARTEMIS provided unprecedented measurements of the interpenetrating beams of both electrons and ions naturally produced by the filtration and acceleration effects of electric fields set up during the refilling process. ARTEMIS also measured electrostatic oscillations closely correlated with counter-streaming electron beams in the wake, as previously hypothesized but never before directly measured. These observations demonstrate the capability of the comprehensively instrumented ARTEMIS spacecraft and the potential for new lunar science from this unique two spacecraft constellation.

Plasma↗

NASA NTRS2007

Monitoring by Use of Clusters of Sensor-Data Vectors

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Iverson, David L.↗

NASA NTRS2007

ISPATOM: A Generic Real-Time Data Processing Tool Without Programming

Information Sharing Protocol Advanced Tool of Math (ISPATOM) is an application program allowing for the streamlined generation of comps, which subscribe to streams of incoming telemetry data, perform any necessary computations on the data, then send the data to other programs for display and/or further processing in NASA mission control centers. Heretofore, the development of comps was difficult, expensive, and time-consuming: Each comp was custom written manually, in a low-level computing language, by a programmer attempting to follow requirements of flight controllers. ISPATOM enables a flight controller who is not a programmer to write a comp by simply typing in one or more equation( s) at a command line or retrieving the equation(s) from a text file. ISPATOM then subscribes to the necessary input data, performs all of necessary computations, and sends out the results. It sends out new results whenever the input data change. The use of equations in ISPATOM is no more difficult than is entering equations in a spreadsheet. The time involved in developing a comp is thus limited to the time taken to decide on the necessary equations. Thus, ISPATOM is a real-time dynamic calculator.

Dershowitz, Adam↗

NASA NTRS2011

Fischer-Tropsch Catalyst for Aviation Fuel Production

As the oil supply declines, there is a greater need for cleaner alternative fuels. There will undoubtedly be a shift from crude oil to non-petroleum sources as a feedstock for aviation (and other transportation) fuels. The Fischer-Tropsch process uses a gas mixture of carbon monoxide and hydrogen which is converted into various liquid hydrocarbons; this versatile gas-to-liquid technology produces a complex product stream of paraffins, olefins, and oxygenated compounds such as alcohols and aldehydes. The Fischer-Tropsch process can produce a cleaner diesel oil fraction with a high cetane number (typically above 70) without any sulfur and aromatic compounds. It is most commonly catalyzed by cobalt supported on alumina, silica, or titania or unsupported alloyed iron powders. Cobalt is typically used more often than iron, in that cobalt is a longer-active catalyst, has lower water-gas shift activity, and lower yield of modified products. Promoters are valuable in improving Fischer-Tropsch catalyst as they can increase cobalt oxide dispersion, enhance the reduction of cobalt oxide to the active metal phase, stabilize a high metal surface area, and improve mechanical properties. Our goal is to build up the specificity of the Fischer-Tropsch catalyst while adding less-costly transition metals as promoters; the more common promoters used in Fischer-Tropsch synthesis are rhenium, platinum, and ruthenium. In this report we will describe our preliminary efforts to design and produce catalyst materials to achieve our goal of preferentially producing C8 to C18 paraffin compounds in the NASA Glenn Research Center Gas-To-Liquid processing plant. Efforts at NASA Glenn Research Center for producing green fuels using non-petroleum feedstocks support both the Sub-sonic Fixed Wing program of Fundamental Aeronautics and the In Situ Resource Utilization program of the Exploration Technology Development and Demonstration program.

deLaRee, Ana B.↗

NASA NTRS2012

Fischer-Tropsch Catalyst for Aviation Fuel Production

As the oil supply declines, there is a greater need for cleaner alternative fuels. There will undoubtedly be a shift from crude oil to nonpetroleum sources as a feedstock for aviation (and other transportation) fuels. The Fischer-Tropsch process uses a gas mixture of carbon monoxide and hydrogen which is converted into various liquid hydrocarbons; this versatile gas-to-liquid technology produces a complex product stream of paraffins, olefins, and oxygenated compounds such as alcohols and aldehydes. The Fischer-Tropsch process can produce a cleaner diesel oil fraction with a high cetane number (typically above 70) without any sulfur and aromatic compounds. It is most commonly catalyzed by cobalt supported on alumina, silica, or titania or unsupported alloyed iron powders. Cobalt is typically used more often than iron, in that cobalt is a longer-active catalyst, has lower water-gas shift activity, and lower yield of modified products. Promoters are valuable in improving Fischer-Tropsch catalyst as they can increase cobalt oxide dispersion, enhance the reduction of cobalt oxide to the active metal phase, stabilize a high metal surface area, and improve mechanical properties. Our goal is to build up the specificity of the Fischer-Tropsch catalyst while adding less-costly transition metals as promoters; the more common promoters used in Fischer-Tropsch synthesis are rhenium, platinum, and ruthenium. In this report we will describe our preliminary efforts to design and produce catalyst materials to achieve our goal of preferentially producing C8 to C18 paraffin compounds in the NASA Glenn Research Center Gas-To-Liquid processing plant. Efforts at NASA Glenn Research Center for producing green fuels using non-petroleum feedstocks support both the Sub-sonic Fixed Wing program of Fundamental Aeronautics and the In Situ Resource Utilization program of the Exploration Technology Development and Demonstration program.

DeLaRee, Ana B.↗

NASA NTRS2013

Contemplating Synergistic Algorithms for the NASA ACE Mission

ACE is a proposed Tier 2 NASA Decadal Survey mission that will focus on clouds, aerosols, and precipitation as well as ocean ecosystems. The primary objective of the clouds component of this mission is to advance our ability to predict changes to the Earth's hydrological cycle and energy balance in response to climate forcings by generating observational constraints on future science questions, especially those associated with the effects of aerosol on clouds and precipitation. ACE will continue and extend the measurement heritage that began with the A-Train and that will continue through Earthcare. ACE planning efforts have identified several data streams that can contribute significantly to characterizing the properties of clouds and precipitation and the physical processes that force these properties. These include dual frequency Doppler radar, high spectral resolution lidar, polarimetric visible imagers, passive microwave and submillimeter wave radiometry. While all these data streams are technologically feasible, their total cost is substantial and likely prohibitive. It is, therefore, necessary to critically evaluate their contributions to the ACE science goals. We have begun developing algorithms to explore this trade space. Specifically, we will describe our early exploratory algorithms that take as input the set of potential ACE-like data streams and evaluate critically to what extent each data stream influences the error in a specific cloud quantity retrieval.

precipitation↗

NASA NTRS2018

Land Surface Reflectances from Geostationary Sensors

GEONEX is a processing pipeline that produces a suite of satellite land surface products using data streams from the latest geostationary (GEO) sensors including the GOES016/ABI and the Himawari-8/AHI. The suite, created collaboratively by scientists from NASA and NOAA, includes top-of-atmosphere (TOA) reflectances, land surface reflectances (LSRs), vegetation indices, LAI/fPAR, and other downstream products. As a key component of the GEONEX product processing, we have adapted the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce LSRs from the TOA data. Because the algorithm depends on building "stacks" of images, we first run internal geo-registration checks to ensure geo-spatial accuracy and consistency of the input (L1B) data before transferring them from the geostationary projection into a tile system in geographic grids. Scan-time is inferred from metadata and applied to calculate the sun-sensor angles for each grid cell. The MAIAC algorithm is run to detect clouds/shadows, estimate aerosol optical thickness (AOT), perform atmospheric corrections, and generate LSRs. We have processed 18-months (from 2016/04 onward) of AHI data over East Asia and Oceania at a 10-minute time step and 10-months (from 2018/01 onward) of ABI data over North and South Americas at a 15-minute time step. As a verification measure, we compare the GEONEX (AHI/ABI) surface reflectances with the standard MODIS products (MOD09GA) and the MODIS MAIAC products over pixels that have similar sun-view geometries. The results indicate general linear relationships between GEONEX and corresponding MODIS LSRs. In particular, the RMSEs between GEONEX and MOD09 data are comparable to those between MOD09 and MODIS MAIAC products, suggesting that the uncertainties of GEONEX LSRs fall into an acceptable range. However, direct comparisons of LSRs over pixels with different sun-view angles are not as straightforward and require more modeling efforts to correct the directional effects. Evaluation of such angular influences on the downstream products (e.g., vegetation indices) is also under investigation.

Geostationary satellite; Remote Sensing; Atmospher↗

NASA NTRS2015

Evidence for a Dynamic Nanodust Cloud Enveloping the Moon

The exospheres that surround airless bodies such as the Moon are tenuous, atmosphere-like layers whose constituent particles rarely collide with one another. Some particles contained within such exospheres are the product of direct interactions between airless bodies and the space environment, and offer insights into space weathering processes. NASAs Lunar Atmosphere and Dust Environment Explorer (LADEE) mission studied the Moons exospheric constituents in situ and detected a permanent dust exosphere1 of particles with radii as small as 300 nm. Here we present evidence from LADEE spectral data for an additional fluctuating nanodust exosphere at the Moon containing a population of particles sufficiently dense to be detectable via scattered sunlight. We compare two anti-Sun spectral observations: one near the peak of the Quadrantidmeteoroid stream, the other during a period of comparativelyweak stream activity. The former shows a negative spectralslope consistent with backscattering of sunlight by nanodustgrains with radii less than 20 to 30 nm; the latter has a flatterspectral slope. We hypothesize that a spatially and temporallyvariable nanodust exosphere may exist at the Moon, and thatit is modulated by changes in meteoroid impact rates, suchas during encounters with meteoroid streams. The findingssuggest that similar nanodust exospheresand the particle ejection and transport processes that form themmay occurat other airless bodies.

Moon↗

NASA NTRS2026

The Trash Compaction Processing Systems (TCPS) Ground Unit Control Sample Testing

The Trash Compaction Processing System (TCPS) is being developed by NASA and Sierra Space to process crew trash for long-duration missions. The system compacts and thermally processes mixed spacecraft waste to reduce volume and stabilize the material while managing gas and liquid effluents. A Ground Unit (GU) located at Sierra Space in Madison, Wisconsin was used to run a series of tests using standardized control samples representing different trash conditions, including nominal, high liquid, high cloth, benign, and foam. Gas grab samples were collected during processing and analyzed to identify the compounds present in the effluent stream and compare the concentrations to the NASA spacecraft maximum allowable concentrations (SMACs). Additional testing included odor testing at White Sands Test Facility, aerosol measurements, microbiology, and tile characterization. Overall, the compounds detected in the gas samples were well below the SMAC limits for all trash models tested. The results from this testing are being used to help guide the verification approach and test planning for the TCPS Flight Unit that is planned for on-orbit testing on the International Space Station.

Control Samples↗

NASA NTRS2017

Mining Twitter Data to Augment NASA GPM Validation

The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value of Twitter by the science research communities.

validatio↗