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At least 1,729 records · Page 96

Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attach s, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Historically, these analog years are visually identified; however, the qualitative nature of this method sometimes precludes the definitive identification of the best analog year. Thus, one goal of this study is to derive a more rigorous, statistical approach for identifying analog years, based on a modified coefficient of determination, termed the analog index (AI). A second goal is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data).

Teng, William↗

Overview and Assessment of Antarctic Ice-Sheet Mass Balance Estimates: 1992-2009

Mass balance estimates for the Antarctic Ice Sheet (AIS) in the 2007 report by the Intergovernmental Panel on Climate Change and in more recent reports lie between approximately ?50 to -250 Gt/year for 1992 to 2009. The 300 Gt/year range is approximately 15% of the annual mass input and 0.8 mm/year Sea Level Equivalent (SLE). Two estimates from radar altimeter measurements of elevation change by European Remote-sensing Satellites (ERS) (?28 and -31 Gt/year) lie in the upper part, whereas estimates from the Input-minus-Output Method (IOM) and the Gravity Recovery and Climate Experiment (GRACE) lie in the lower part (-40 to -246 Gt/year). We compare the various estimates, discuss the methodology used, and critically assess the results. We also modify the IOM estimate using (1) an alternate extrapolation to estimate the discharge from the non-observed 15% of the periphery, and (2) substitution of input from a field data compilation for input from an atmospheric model in 6% of area. The modified IOM estimate reduces the loss from 136 Gt/year to 13 Gt/year. Two ERS-based estimates, the modified IOM, and a GRACE-based estimate for observations within 1992 2005 lie in a narrowed range of ?27 to -40 Gt/year, which is about 3% of the annual mass input and only 0.2 mm/year SLE. Our preferred estimate for 1992 2001 is -47 Gt/year for West Antarctica, ?16 Gt/year for East Antarctica, and -31 Gt/year overall (?0.1 mm/year SLE), not including part of the Antarctic Peninsula (1.07% of the AIS area). Although recent reports of large and increasing rates of mass loss with time from GRACE-based studies cite agreement with IOM results, our evaluation does not support that conclusion

Zwally, H. Jay↗

NASA A-Train and Terra Observations of the 2010 Russian Wildfires

Wildfires raged throughout western Russia and parts of Eastern Europe during a persistent heat wave in the summer of 2010. Anomalously high surface temperatures (35 - 41 C) and low relative humidity (9 - 25 %) from mid- June to mid-August 2010 shown by analysis of radiosonde data from multiple sites in western Russia were ideal conditions for the wildfires to thrive. Measurements of outgoing longwave radiation (OLR) from the Atmospheric Infrared Sounder (AIRS) over western Russian indicate persistent subsidence during the heat wave. Daily three-day back-trajectories initiated over Moscow reveal a persistent anticyclonic circulation for 18 days in August, coincident with the most intense period of fire activity observed by Moderate Resolution Imaging Spectroradiometer (MODIS). This unfortunate meteorological coincidence allowed transport of polluted air from the region of intense fires to Moscow and the surrounding area. We demonstrate that the 2010 Russian wildfires are unique in the record of observations obtained by remote-sensing instruments on-board NASA satellites: Aura and Aqua (part of the A-Train Constellation) and Terra. Analysis of the distribution of MODIS fire products and aerosol optical thickness (AOT), UV aerosol index (AI) and single-scattering albedo (SSA) from Aura's Ozone Monitoring Instrument (OMI), and total column carbon monoxide (CO) from Aqua s Atmospheric Infrared Sounder (AIRS) show that the region in the center of western Russia surrounding Moscow (52-58 deg N, 33 -43 deg E) is most severely impacted by wildfire emissions. Over this area, AIRS CO, OMI AI, and MODIS AOT are significantly enhanced relative to the historical satellite record during the first 18 days in August when the anti-cyclonic circulation persisted. By mid-August, the anti-cyclonic circulation was replaced with westerly transport over Moscow and vicinity. The heat wave

Witte, J. C.↗

Combined Dust Detection Algorithm by Using MODIS Infrared Channels over East Asia

A new dust detection algorithm is developed by combining the results of multiple dust detectionmethods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTDmethods have limitations in identifying the offset values of the BTDto discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10 × 10 pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.

East Asia↗

Simulating Wake Vortex Detection with the Sensivu Doppler Wind Lidar Simulator

In support of NASA's Atmospheric Environment Safety Technologies NRA research topic on Wake Vortex Hazard Investigation, Aerospace Innovations (AI) investigated a set of techniques for detecting wake vortex hazards from arbitrary viewing angles, including axial perspectives. This technical report describes an approach to this problem and presents results from its implementation in a virtual lidar simulator developed at AI. Threedimensional data volumes from NASA's Terminal Area Simulation System (TASS) containing strong turbulent vortices were used as the atmospheric domain for these studies, in addition to an analytical vortex model in 3-D space. By incorporating a third-party radiative transfer code (BACKSCAT 4), user-defined aerosol layers can be incorporated into atmospheric models, simulating attenuation and backscatter in different environmental conditions and altitudes. A hazard detection algorithm is described that uses a twocomponent spectral model to identify vortex signatures observable from arbitrary angles.

Ramsey, Dan↗

Future Antarctic Bed Topography and Its Implications for Ice Sheet Dynamics

The Antarctic bedrock is evolving as the solid Earth responds to the past and ongoing evolution of the ice sheet. A recently improved ice loading history suggests that the Antarctic Ice Sheet (AIS) has generally been losing its mass since the Last Glacial Maximum. In a sustained warming climate, the AIS is predicted to retreat at a greater pace, primarily via melting beneath the ice shelves.We employ the glacial isostatic adjustment (GIA) capability of the Ice Sheet System Model (ISSM) to combine these past and future ice loadings and provide the new solid Earth computations for the AIS.We find that past loading is relatively less important than future loading for the evolution of the future bed topography. Our computations predict that the West Antarctic Ice Sheet (WAIS) may uplift by a few meters and a few tens of meters at years AD 2100 and 2500, respectively, and that the East Antarctic Ice Sheet is likely to remain unchanged or subside minimally except around the Amery Ice Shelf. The Amundsen Sea Sector in particular is predicted to rise at the greatest rate; one hundred years of ice evolution in this region, for example, predicts that the coastline of Pine Island Bay will approach roughly 45mmyr−1 in viscoelastic vertical motion. Of particular importance, we systematically demonstrate that the effect of a pervasive and large GIA uplift in the WAIS is generally associated with the flattening of reverse bed slope, reduction of local sea depth, and thus the extension of grounding line (GL) towards the continental shelf. Using the 3-D higher-order ice flow capability of ISSM, such a migration of GL is shown to inhibit the ice flow. This negative feedback between the ice sheet and the solid Earth may promote stability in marine portions of the ice sheet in the future.

ice dynamics↗

Antarctica, Greenland and Gulf of Alaska Land-ice Evolution from an Iterated GRACE Global Mascon Solution

We have determined the ice mass evolution of the Antarctica and Greenland ice sheets (AIS and GIS) and Gulf of Alaska (GOA) glaciers from a new GRACE global solution of equal-area surface mass concentration parcels (mascons) in equivalent height of water. The mascons were estimated directly from the reduction of the inter-satellite K-band range-rate (KBRR) observations, taking into account the full noise covariance, and formally iterating the solution. The new solution increases signal recovery while reducing the GRACE KBRR observation residuals. The mascons were estimated with 10 day and 1 arc degree equal-area sampling, applying anisotropic constraints. An ensemble empirical mode decomposition adaptive filter was applied to the mascon time series to compute annual mass balances. The details and causes of the spatial and temporal variability of the land-ice regions studied are discussed. The estimated mass trend over the total GIS, AIS and GOA glaciers for the time period 1 December 2003 to 1 December 2010 is -380 plus or minus 31 Gt a(exp -1), equivalent to -1.05 plus or minus 0.09 mma(exp -1) sea-level rise. Over the same time period we estimate the mass acceleration to be -41 plus or minus 27 Gt a(exp -2), equivalent to a 0.11 plus or minus 0.08 mm a(exp -2) rate of change in sea level. The trends and accelerations are dependent on significant seasonal and annual balance anomalies.

MASCON↗

Spaceflight Ground Support Equipment Reliability & System Safety Data

Presented were Reliability Analysis, consisting primarily of Failure Modes and Effects Analysis (FMEA), and System Safety Analysis, consisting of Preliminary Hazards Analysis (PHA), performed to ensure that the CoNNeCT (Communications, Navigation, and Networking re- Configurable Testbed) Flight System was safely and reliably operated during its Assembly, Integration and Test (AI&T) phase. A tailored approach to the NASA Ground Support Equipment (GSE) standard, NASA-STD-5005C, involving the application of the appropriate Requirements, S&MA discipline expertise, and a Configuration Management system (to retain a record of the analysis and documentation) were presented. Presented were System Block Diagrams of selected GSE and the corresponding FMEA, as well as the PHAs. Also discussed are the specific examples of the FMEAs and PHAs being used during the AI&T phase to drive modifications to the GSE (via "redlining" of test procedures, and the placement of warning stickers to protect the flight hardware) before being interfaced to the Flight System. These modifications were necessary because failure modes and hazards were identified during the analysis that had not been properly mitigated. Strict Configuration Management was applied to changes (whether due to upgrades or expired calibrations) in the GSE by revisiting the FMEAs and PHAs to reflect the latest System Block Diagrams and Bill Of Material. The CoNNeCT flight system has been successfully assembled, integrated, tested, and shipped to the launch site without incident. This demonstrates that the steps taken to safeguard the flight system when it was interfaced to the various GSE were successful.

Fernandez, Rene↗

A Scala DSL for RETE-Based Runtime Verification

Runtime verification (RV) consists in part of checking execution traces against formalized specifications. Several systems have emerged, most of which support specification notations based on state machines, regular expressions, temporal logic, or grammars. The field of Artificial Intelligence (AI) has for an even longer period of time studied rule-based production systems, which at a closer look appear to be relevant for RV, although seemingly focused on slightly different application domains, such as for example business processes and expert systems. The core algorithm in many of these systems is the Rete algorithm. We have implemented a Rete-based runtime verification system, named LogFire (originally intended for offline log analysis but also applicable to online analysis), as an internal DSL in the Scala programming language, using Scala's support for defining DSLs. This combination appears attractive from a practical point of view. Our contribution is in part conceptual in arguing that such rule-based frameworks originating from AI may be suited for RV.

matching problem↗

Analysis of Terrestrial Interference Protection from UAS CNPC Satellite Transmitters

Unmanned aircraft (UA) are projected to have a major impact on future aviation. Larger UA operating at altitudes above 3000 feet will require at least occasional access to non-segregated, that is, controlled airspace. In order for unmanned aircraft to be integrated into the airspace and operate with other commercial aircraft, a very reliable command and control (a. k. a. control and non-payload communications, (CNPC)) link is required. For operations covering large distances or over remote locations, a beyond-line-of-sight (BLOS) CNPC link implemented through a satellite will almost always be required. Protected aviation spectrum (aeronautical mobile satellite (route) service, or AMS(R)S) would normally be used for such a safety-critical link, however studies have shown that currently available aviation safety satellite spectrum is inadequate to support the projected BLOS CNPC link bandwidth requirements. To address this inadequacy, the 2015 World Radio communication Conference studied the possible use of the Fixed Satellite Service (FSS) to provide CNPC, including possible allocations in Ku-Band and Ka-Band, under Agenda Item (AI) 1.5. Although UA CNPC satellite links in these bands were shown to meet operational availability and continuity requirements, a serious complication exists in that there are also terrestrial service allocations in these bands, in particular, Fixed Service (FS) point-to-point and point-to-multipoint microwave digital links. During the WRC-15 study cycle, much opposition to AI 1.5 was generated based on fears that UA CNPC satellite transmitters in these bands would impose unacceptable levels of interference to the FS receivers. NASA analyzed the possible interference from the UA transmitters based on probable UA transmission and FS receiver characteristics, and UA traffic distributions and densities to determine conditions under which UA could operate without imposing unacceptable interference levels to the FS. Ultimately, UA power flux density transmission limits were proposed as a way to insure protection of FS receivers and further studies were prepared on the various proposals. This paper presents the results of these studies and discusses possible implications on future UA BLOS operations.

aircraft communications↗

Space Technology - Game Changing Development NASA Facts: Autonomous Medical Operations

The AMO (Autonomous Medical Operations) Project is working extensively to train medical models on the reliability and confidence of computer-aided interpretation of ultrasound images in various clinical settings, and of various anatomical structures. AI (Artificial Intelligence) algorithms recognize and classify features in the ultrasound images, and these are compared to those features that clinicians use to diagnose diseases. The acquisition of clinically validated image assessment and the use of the AI algorithms constitutes fundamental baseline for a Medical Decision Support System that will advise crew on long-duration, remote missions.

Autonomous Medical Operations Project↗

A Machine-Learning Approach to Assess Aircraft Engine System Performance

Artificial intelligence (AI)/machine learning, and big data are transforming the global business environment. They have become the most disruptive technologies for organizations to improve workplace efficiency and productivity. This work explored the application of machine learning-based predictive analytics that would enable aircraft engine designers to estimate engine system performance quickly during the conceptual design stage. Supervised machine-learning algorithm was employed to study patterns in an existing database of production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using turbofan design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks API (application program interface) written in Python, with TensorFlow (an open-source artificial AI library developed by Google) serving as the backend engine. The current engine-weight prediction results, together with those for the TSFC (thrust specific fuel consumption) and core-size predictions that were studied previously by the author, show that machine learning-based predictive analytics can be an effective, time-saving tool for aircraft engine design-space exploration during the conceptual design stage. It would enable expeditious identification of the best engine design amongst several candidates.

Michael T Tong↗

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗

Crew Time Requirements in Future Space Greenhouses - What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Lucie Poulet↗

Crew Time Requirements in Future Space Greenhouses: What Can We Infer from Current Analog and Space Missions?

Efficient crop production will be required to advance humanity’s presence in space, and for this, accurate predictions of crew time in future space greenhouse modules will be crucial to design and operate these plant growth systems, and schedule crop production. Crew time estimates will also be critical for deciding priorities of automating different aspects of space crop production. Because it is difficult to capture in operational environments, crew time for plant cultivation has only been sporadically recorded in past analog and space missions. We propose a methodology for efficient categorizing and reporting of crew time in space plant growth systems: first identify the different tasks needed to operate the greenhouse module, second define a representative time period for data collection, third accurately report crew time for individual tasks - and their occurrence, and fourth use collected data to improve greenhouse modules and plant growth system designs. Using data from various analog facilities and from the Veggie hardware on ISS, and assumptions for different mission scenarios, we discuss how crew time for plant cultivation can be reduced with adequate choices of crops, automation, artificial intelligence (AI) and virtual assistants, and sufficient crew training. This has major implications for the design of future space greenhouse modules. For example, missions on future space stations or during interplanetary travel would save significant crew time by including leafy greens and microgreens for astronaut’s diet supplement, with automated watering, health and environmental checks, as well as AI managing maintenance schedules, and a virtual assistant for repair activities. This work was funded by NASA Space Biology through NASA postdoctoral program / USRA, by NASA’s Space Biology and Human Research Programs, and by the European Union Horizon 2020 program via the COMPET-07-2014 - Space exploration – Life-support subprogram (reference number: 636501).

Veggie↗

Embracing the fourth Industrial Revolution - Challenges, Opportunities and Path Forward for Propulsion

The aerospace industry is at a point where components are reaching design maturity and performance improvements are incremental. Aggressive goals to achieve sustainability and the threat of climate change necessitate a new paradigm. An artificial intelligence (AI) approach that enables revolutionary changes in system architecture, mission analysis and performance metrics is needed. The growing interest and development in the field of machine learning presents an opportunity to speed up by 10X or more the discovery, analysis and development of aerospace systems using artificial intelligence. Through IDEAS (Intelligent Design and Engineering of Aerospace Systems) we are embarking on a research and development effort that addresses this opportunity. The objective of IDEAS is to enable design of systems based on requirements. We discuss possible approaches to generating data, training models and applying them to near term applications. Results of recent workshops with industry, academia and other agencies to identify challenges to adopting AI and machine learning are presented.

Machine learning↗

Embracing the Fourth Industrial Revolution - Challenges, Opportunities and Path Forward for Propulsion

The aerospace industry is at a point where components are reaching design maturity and performance improvements are incremental. Aggressive goals to achieve sustainability and the threat of climate change necessitate a new paradigm. An artificial intelligence (AI) approach that enables revolutionary changes in system architecture, mission analysis and performance metrics is needed. The growing interest and development in the field of machine learning presents an opportunity to speed up by 10X or more the discovery, analysis and development of aerospace systems using artificial intelligence. Through IDEAS (Intelligent Design and Engineering of Aerospace Systems) we are embarking on a research and development effort that addresses this opportunity. The objective of IDEAS is to enable design of systems based on requirements. We discuss possible approaches to generating data, training models and applying them to near term applications. Results of recent workshops with industry, academia and other agencies to identify challenges to adopting AI and machine learning are presented.

Machine learning↗

Celestial Mapping System and Digital Lunar Library Initiative

We are preparing to create an interactive, global 3D lunar environment with integrated dataset and AI/ML tools to provide unique value to mission planners, scientists and the entire lunar community. This lunar environment will be based on NASA Ames Celestial Mapping System (CMS) [1] and Digital Lunar Library (DLL) Initiative. CMS provides a 3D virtual Lunar Globe with extensive user friendly tool sets, that include high resolution terrain visualization, elevation profiles, measurement kits, slope analysis, path optimization, line of sight analysis, equipment planning and placement tools and many other functionalities [1]. It has a thick client with less overhead to access hardware resources. This allows features such as terrain profiling and distance calculations to be performed on the client and on the fly. The application is developed to provide situational and domain awareness on the Lunar surface, planning capabilities for equipment placements and traverse path optimization. As data becomes available, CMS has the capabilities to integrate data sets that change dynamically in real-time, which will be useful for monitoring satellites and remotely-sensed data on the Lunar surface. CMS supports importing synthetic features in a variety of 3D, 2D, vector and raster formats. In the future, these capabilities will be enhanced by incorporating AI/ML tools and a plug-in architecture to enable customization by the user groups. With the help of DLL we will be able to : 1) Amplify the value of lunar information with AI-powered data enhancements 2) Acquire and integrate lunar data with AI-assisted georectification and homogenization 3) Analyze lunar data with advanced 3D visualization, intelligent search-by-example 4) Apply lunar data insights to specific use cases with an open plug-in architecture. The CMS-DLL initiative will have several potential use cases for NASA and the lunar community in general, including subsurface lava tube visualization and analysis, soil analysis, in-situ lunar resource visualization and representation on 3D globe, and data analytics for utilization. REFERENCES: [1] https://celestial.arc.nasa.gov/

3D Globe↗