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NASA NTRS2011

Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

Reichle, Rolf H.↗

NASA NTRS2007

Detailed Analysis of Indian Summer Monsoon Rainfall Processes with Modern/High-Quality Satellite Observations

We examine, in detail, Indian Summer Monsoon rainfall processes using modernhigh quality satellite precipitation measurements. The focus here is on measurements derived from three NASA cloud and precipitation satellite missionslinstruments (TRMM/PR&TMI, AQUNAMSRE, and CLOUDSATICPR), and a fourth TRMM Project-generated multi-satellite precipitation measurement dataset (viz., TRMM standard algorithm 3b42) -- all from a period beginning in 1998 up to the present. It is emphasized that the 3b42 algorithm blends passive microwave (PMW) radiometer-based precipitation estimates from LEO satellites with infi-ared (IR) precipitation estimates from a world network of CEO satellites (representing -15% of the complete space-time coverage) All of these observations are first cross-calibrated to precipitation estimates taken from standard TRMM combined PR-TMI algorithm 2b31, and second adjusted at the large scale based on monthly-averaged rain-gage measurements. The blended approach takes advantage of direct estimates of precipitation from the PMW radiometerequipped LEO satellites -- but which suffer fi-om sampling limitations -- in combination with less accurate IR estimates from the optical-infrared imaging cameras on GEO satellites -- but which provide continuous diurnal sampling. The advantages of the current technologies are evident in the continuity and coverage properties inherent to the resultant precipitation datasets that have been an outgrowth of these stable measuring and retrieval technologies. There is a wealth of information contained in the current satellite measurements of precipitation regarding the salient precipitation properties of the Indian Summer Monsoon. Using different datasets obtained from the measuring systems noted above, we have analyzed the observations cast in the form of: (1) spatially distributed means and variances over the hierarchy of relevant time scales (hourly I diurnally, daily, monthly, seasonally I intra-seasonally, and inter-annually), (2) time series at these different time scales taken as area-averages over the hierarchy of relevant space scales (Indian sub-Division, Indian sub-continent, and Circumambient Indian Ocean), (3) principal autocorrelation and cross-correlation structures over various monsoon space-time domains, (4) diurnally modulated amplitude-phase properties of rain rates over different monsoon space-time domains, (5) foremost rain rate probability distributions intrinsic to monsoon precipitation, and (6) behavior of extreme events including occurrences of flood and drought episodes throughout the course of inter-annual monsoon processes.

Smith, Eric A.↗

NASA NTRS1988

Phase transitions as the origin of large scale structure in the universe

A review of the formation of large scale structure through gravitational growth of primordial perturbations is given. This is followed by a discussion of how symmetry breaking phase transitions in the early universe might have produced the required perturbations, in particular through the formation and evolution of a network of cosmic strings. Finally, the statistical mechanics of string networks, for both cosmic and fundamental strings is discussed, leading to some more speculative ideas on the possible role of fundamental strings (superstrings or heterotic strings) in the very early universe.

Turok, Neil↗

NASA NTRS1989

Phase transitions as the origin of large scale structure in the universe

Turok, Neil↗

NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗

NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Yury Lebedev↗

NASA NTRSDate not supplied

Air Quality Forecasting at Sub-City Scale by Combining Models, Satellites, and Surface Measures

While there are a variety of sources for air quality information, no one source simultaneously allows for high accuracy, low bias, fine spatial resolution, wide spatial coverage, high temporal frequency, and the capability for near-term forecasting of air quality. Global models, like the NASA’s Goddard Earth Observing System -Composition Forecasting (GEOS-CF) model, provide global coverage and forecasting capabilities, but operate at relatively coarse spatial resolution and require ground-truthing with in-situ data. Polar-orbiting satellite data products, like those of the ESA TROPOspheric Monitoring Instrument (TROPOMI), provide higher-spatial-resolution remote sensing of atmospheric composition, but are limited by cloud cover and overpass times and report column-integrated quantities. Surface measurements, both from regulatory-grade monitors and low-cost networks, measure “nose-level” air quality, but may not represent concentration variability across large spatial domains, and (in the case of low-cost sensors) are subject to interference and biases. There exists a great potential to combine these diverse data sources together, using the strengths of some to offset the weaknesses of others to build a more comprehensive picture of air quality. This presentation will summarize results from ongoing efforts to produce such a combined forecast, with application case studies for surface-level Nitrogen Dioxide forecasting in several major US cities. Furthermore, we will examine the relative impacts and benefits of different data sources on the forecasting accuracy at different spatial and temporal scales. Finally, we will examine the potential for integrating low-cost sensors into such a system, both in terms of using these integrated air quality estimates as a baseline from which to calibrate networks of low-cost sensors in the field, and in terms of using dense networks of low-cost sensors to refine the spatial resolution of integrated air quality forecasts.

Air Quality↗

NASA NTRS2019

Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16)

The NASA's Soil Moisture Active Passive (SMAP) mission conducted a field experiment with its partners over two 40-km agricultural domains in Iowa and Manitoba in the summer of 2016 to address concerns observed in SMAP soil moisture (SM) retrievals over agricultural areas. The experiment featured airborne Passive Active L-band System (PALS) flights over each domain with intensive ground measurements and dense networks of SM monitoring stations. With two intensive observation periods separated in time (May 28–June 20 and July 14–August 16), the flights captured both early-season/low vegetation and later-season/high-vegetation conditions. The comparison of the PALS brightness temperature (TB) measurements to the SMAP TB observed over the sites resulted in root mean square difference (RMSD) of 2.8 K and 4.0 K for vertical and horizontal polarizations, respectively. The subsequent SM analysis rescaled the PALS TB with the SMAP TB to allow equitable comparisons between the SM retrievals from the two instruments. The PALS SM retrieval algorithm used the SM sampled by the ground teams during the overpass days for tuning, and was parameterized by a high-resolution vegetation water content product calibrated using vegetation samples collected during the experiment. The tuning process was not able to find a satisfactory result with a temporally constant set of parameters in the single channel algorithm for the two intensive observation periods of the experiment. This result indicated that the rapid change in the vegetation structure during the growth stages and likely variation in the surface roughness conditions were not compatible with rigid parameterization over the entire period. However, using seasonally variable parameters we found that it was possible to retrieve soil moisture with satisfactory accuracy. Comparative analysis with the SMAP SM product included aggregation of the PALS SM to the SMAP pixel-scale. The RMSD between the PALS SM and the aggregated manual field samples was <0.04 m3/m3 with Pearson correlation >0.85 for both sites. The comparison between different in situ sources indicated that the soil moisture network measurements were not the source of the large biases observed for SMAP over the sites reported in earlier studies. Therefore, the results suggested the rapidly growing vegetation and the early-season surface condition changes not captured by the SMAP algorithm caused the SMAP retrieval errors. In addition, the significant deviations of the vegetation water content used by the SMAP product from the calibrated vegetation water content obtained during the experiment compounds the problem.

Andreas Colliander↗

NASA NTRS2023

Impacts of SLR Ground Station Geographic Distribution on Time-Variable Gravity Recovery

Satellite laser ranging (SLR) continues to be an essential technique for estimating Earth's low-degree gravity field. The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) do not accurately recover certain coefficients, which are needed for quantifying large-scale mass redistribution as part of Earth's time-variable gravity (TVG) field. In case of an inter-mission gap, as with GRACE and GRACE-FO, SLR can continue to provide mass-change measurements. The current SLR ground network consists of around 40 stations. However, these are not uniformly distributed and the quantity and quality of data varies widely between sites. We anticipate that future stations will improve SLR-derived gravity estimates and we investigate their potential impact on the solution in this simulation study. A single new station that enhances network geometry, particularly in the Southern hemisphere, increases the sensitivity of the network by up to 12% for certain coefficients. The addition of 5 new future stations improves certain coefficients up to 18%. We find new stations are limited in their ability to decorrelate certain coefficients regardless of their location, but that adding more data and increasing network uniformity still benefits the overall solution.

E S Tucker↗

NASA NTRS2011

Tracking Climate Effects on Plant-Pollinator Interaction Phenology with Satellites and Honey Bee Hives

Background/Question/Methods: The complexity of plant-pollinator interactions, the large number of species involved, and the lack of species response functions present challenges to understanding how these critical interactions may be impacted by climate and land cover change on large scales. Given the importance of this interaction for terrestrial ecosystems, it is desirable to develop new approaches. We monitor the daily weight change of honey bee (Apis mellifera) colonies to record the phenology of the Honey Bee Nectar Flow (HBNF) in a volunteer network (honeybeenet.gsfc.nasa.gov). The records document the successful interaction of a generalist pollinator with a variety of plant resources. We extract useful HBNF phenology metrics for three seasons. Sites currently exist in 35 states/provinces in North America, with a concentration in the Mid-Atlantic region. HBNF metrics are compared to standard phenology metrics derived from remotely sensed vegetation indices from NASA's MODIS sensor and published results from NOAA's A VHRR. At any given time the percentage of plants producing nectar is usually a sma11 fraction of the total satellite sensor signal. We are interested in determining how well the 'bulk' satellite vegetation parameters relate to the phenology of the HBNF, and how it varies spatially on landscape to continental scales. Results/Conclusions: We found the median and peak seasonal HBNF dates to be robust, with variation between replicate scale hives of only a few days. We developed quality assessment protocols to identify abnormal colony artifacts. Temporally, the peak and median of the HBNF in the Mid-Atlantic show a significant advance of 0.58 d/y beginning about 1970, very similar to that observed by the A VHRR since 1982 (0.57 d/y). Spatially, the HBNF metrics are highly correlated with elevation and winter minimum temperature distribution, and exhibit significant but regionally coherent inter-annual variation. The relationship between median of the spring HBNF with the "Green-up" metric from the 500 meter MODIS NDVI phenology product, for sites throughout the Eastern US 2000-2009, is well described by a single linear fit (r(exp 2) = 0.72). We conclude.that for the tree-dominated areas of the Eastern US at least the spring HBNF can be tracked very well by MODIS phenology. Analysis of other regions and seasons is presently underway but with more limited data. Spatial patterns in the eastern US and management implications will be presented and discussed.

Esaias, Wayne E.↗

NASA NTRS2016

Cyber-Threat Assessment for the Air Traffic Management System: A Network Controls Approach

Air transportation networks are being disrupted with increasing frequency by failures in their cyber- (computing, communication, control) systems. Whether these cyber- failures arise due to deliberate attacks or incidental errors, they can have far-reaching impact on the performance of the air traffic control and management systems. For instance, a computer failure in the Washington DC Air Route Traffic Control Center (ZDC) on August 15, 2015, caused nearly complete closure of the Centers airspace for several hours. This closure had a propagative impact across the United States National Airspace System, causing changed congestion patterns and requiring placement of a suite of traffic management initiatives to address the capacity reduction and congestion. A snapshot of traffic on that day clearly shows the closure of the ZDC airspace and the resulting congestion at its boundary, which required augmented traffic management at multiple locations. Cyber- events also have important ramifications for private stakeholders, particularly the airlines. During the last few months, computer-system issues have caused several airlines fleets to be grounded for significant periods of time: these include United Airlines (twice), LOT Polish Airlines, and American Airlines. Delays and regional stoppages due to cyber- events are even more common, and may have myriad causes (e.g., failure of the Department of Homeland Security systems needed for security check of passengers, see [3]). The growing frequency of cyber- disruptions in the air transportation system reflects a much broader trend in the modern society: cyber- failures and threats are becoming increasingly pervasive, varied, and impactful. In consequence, an intense effort is underway to develop secure and resilient cyber- systems that can protect against, detect, and remove threats, see e.g. and its many citations. The outcomes of this wide effort on cyber- security are applicable to the air transportation infrastructure, and indeed security solutions are being implemented in the current system. While these security solutions are important, they only provide a piecemeal solution. Particular computers or communication channels are protected from particular attacks, without a holistic view of the air transportation infrastructure. On the other hand, the above-listed incidents highlight that a holistic approach is needed, for several reasons. First, the air transportation infrastructure is a large scale cyber-physical system with multiple stakeholders and diverse legacy assets. It is impractical to protect every cyber- asset from known and unknown disruptions, and instead a strategic view of security is needed. Second, disruptions to the cyber- system can incur complex propagative impacts across the air transportation network, including its physical and human assets. Also, these implications of cyber- events are exacerbated or modulated by other disruptions and operational specifics, e.g. severe weather, operator fatigue or error, etc. These characteristics motivate a holistic and strategic perspective on protecting the air transportation infrastructure from cyber- events. The analysis of cyber- threats to the air traffic system is also inextricably tied to the integration of new autonomy into the airspace. The replacement of human operators with cyber functions leaves the network open to new cyber threats, which must be modeled and managed. Paradoxically, the mitigation of cyber events in the airspace will also likely require additional autonomy, given the fast time scale and myriad pathways of cyber-attacks which must be managed. The assessment of new vulnerabilities upon integration of new autonomy is also a key motivation for a holistic perspective on cyber threats.

Complex Networks↗

NASA NTRS2014

Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Modeling↗

NASA NTRS1985

Feed System Design Considerations for Large Space Antenna Systems. Part 2: Single Aperture with Overlapping Feeds

Contiguous multiple beams are used to make use of the set of frequency bands at different beams at different geographical locations and achieve the most efficient use of precious frequency allocation. This is feasible only if the interference among cochannel beams is below some acceptable level, by carrier/interference (C/I) ratio. Individual beam patterns of very low sidelobes, which in turn necessitates narrow feed are required. A physically realizable solution to this problem entails either more than one reflector, more complicated optics, or breaking of each feed into a cluster of smaller elements, some of which would then be shared by adjacent beams. The latter, however, requires a complicated beamforming network (BFN) for the proper feeding of the elements. The feedpacking problem is not unique to reflector antennas and exists in lens type antennas as well. The BFN and associated problems are present in the phased array antennas and on a much larger scale. Poor scan capabilities are associated with reflector systems. The scan properties of offset fed reflector systems can be improved by choosing a very large focal length to parent reflector diameter ((F/Dp) ratio, which requires a longer boom to support the feed. In the case of reflectors with cluster feed arrangements, the scan capability for smaller F/Dp ratios is improved by proper adjustment of cluster element excitations. Such a system seems to be appropriate for up to 10 beamwidths scan. For a larger number of beams, systems with wider scan capabilities, such as phased arrays or phased array/reflector combinations, become more appealing.

Jamnejad, V.↗

NASA NTRS1972

Comments on ocean circulation with regard to satellite altimetry

Basic features of sea surface topography are reviewed, to show those oceanographic results which may be of value to a geodetic satellite program: (1) the shape and magnitude of the large scale features of the mean sea surface, relative to a level surface; (2) the position and magnitude of the slopes across the western boundary currents, from a variety of data; (3) an estimate of the position of the geoid, tied into the U.S. leveling network; and (4) a documented change of 60 to 70 cm in mean sea level, with respect to the geoid, between the U.S. east and west coasts.

Sturges, W.↗

NASA NTRS1997

Differential Magnetic Field Shear in an Active Region

The three-dimensional extrapolation of magnetic field lines from a magnetogram obtained at Kitt Peak allows us to understand the global structure of the NOAA active region 6718, as observed in X-rays with the Normal Incidence X-ray Telescope (NIXT) and in Ha with the Multichannel Subtractive Double Pass spectrograph (MSDP) in Meudon on 1991 July 11. This active region was in a quiet stage. Bright X-ray loops connect plages having field strengths of approx. 300 G, while H-alpha fibriles connect penumbrae having strong spot fields to the surrounding network. Small, intense X-ray features in the moat region around a large spot, which could be called X-ray-bright points, are due mainly to the emergence of magnetic flux and merging of these fields with surrounding ones. A set of large-scale, sheared X-ray loops is observed in the central part of the active region. Based on the fit between the observed coronal structure and the field configurations (and assuming a linear force-free field), we propose a differential magnetic field shear model for this active region. The decreasing shear in outer portions of the active region may indicate a continual relaxation of the magnetic field to a lower energy state in the progressively older portions of the AR.

Schmeider, B.↗

NASA NTRS2002

A Grid Infrastructure for Supporting Space-based Science Operations

Emerging technologies for computational grid infrastructures have the potential for revolutionizing the way computers are used in all aspects of our lives. Computational grids are currently being implemented to provide a large-scale, dynamic, and secure research and engineering environments based on standards and next-generation reusable software, enabling greater science and engineering productivity through shared resources and distributed computing for less cost than traditional architectures. Combined with the emerging technologies of high-performance networks, grids provide researchers, scientists and engineers the first real opportunity for an effective distributed collaborative environment with access to resources such as computational and storage systems, instruments, and software tools and services for the most computationally challenging applications.

Bradford, Robert N.↗

NASA NTRS2009

Technology Infusion of CodeSonar into the Space Network Ground Segment

This slide presentation reviews the applicability of CodeSonar to the Space Network software. CodeSonar is a commercial off the shelf system that analyzes programs written in C, C++ or Ada for defects in the code. Software engineers use CodeSonar results as an input to the existing source code inspection process. The study is focused on large scale software developed using formal processes. The systems studied are mission critical in nature but some use commodity computer systems.

Benson, Markland J.↗

NASA NTRS2018

Assessing C2 Communications for UAS Traffic Management

The National Aeronautics and Space Administration's (NASA) Unmanned Aircraft Systems (UAS) Traffic Management (UTM) project works to develop tools and technologies essential for safely enabling civilian low-altitude UAS operations. Currently there is no established infrastructure to enable and safely manage the widespread use of low-altitude airspace and UAS operations, regardless of the type of UAS. The UTM technical challenge will develop comprehensive and validated airspace operations and integration requirements to safely enable large-scale persistent access to visual line of sight and autonomous beyond visual line of sight small UAS in low-altitude airspace. Within the UTM project, a number of communications technologies to support UTM command and control (C2) are under investigation. In particular, commercial networked cellular systems are being tested and assessed for their ability to meet the reliability, scalability, cybersecurity and redundancy required. NASA Glenn Research Center is studying some of the aspects of employing such networks for UTM C2 communications. This includes the development of a test platform for sensing and characterizing the airborne C2 communications environment at various altitudes and in various terrains and topologies, measuring such aspects as received signal strength and interference. System performance aspects such as latency in the link, handover performance, packet error loss rate, drop outs, coverage gaps and other aspects impacting UTM operation will also be assessed. In this paper we explore some of the C2 approaches being proposed and demonstrated in the UTM project, the reliability, availability and other general C2 performance requirements, and approaches to evaluating and analyzing UTM C2 links based on commercial cellular networks.

air traffic control↗