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At least 109 records · Page 6

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NASA NTRS1985

Binning of satellite magnetic anomalies

Crustal magnetic anomaly signals over satellite orbits were simulated to investigate numerical averaging as an anomaly estimator. Averaging as an anomaly estimator involves significant problems concerning spatial and amplitude smoothing of the satellite magnetic observations. The results of simulations suggest that the error of numerical averaging constitutes a small and relatively minor component of the total error-budget of higher orbital anomaly estimates, whereas for lower orbital estimates numerical averaging error increases substantially. As an alternative to numerical averaging, least-squares collocation was investigated and observed to produce substantially more accurate anomaly estimates, particularly as the orbital elevation of prediction was decreased towards the crustal sources. In contrast to averaging, collocation is a significantly more resource-intensive procedure to apply because of the practical, but surmountable problems related to establishing and inverting the covariance matrix for accurate anomaly prediction. However, collocation may be much more effectively used to exploit the anomaly details contained in the lower orbital satellite magnetic data for geologic analysis.

Goyal, H. K.↗

NASA NTRS1986

The life cycles of persistent anomalies and blocking over the North Pacific

The evolution of persistent anomaly patterns over the central North Pacific is investigated. Composite time evolution fields of the 500-mbar anomaly patterns are constructed from low-pass and unfiltered height anomaly data; the time scales for the development and decay of these persistent anomalies are analyzed. The relationship between zonal flow in the Pacific jet region and the development of the anomaly patterns is examined. The effect of baroclinic instabilities on the development of the anomalies is studied. The vertical structure and synoptic characteristics of the evolution of the anomalies are described. It is noted that the initial rapid growth of the main center may be associated with a propagating, intensifying, synoptic-scale disturbance which originates in the midlatitudes over eastern Asia.

Dole, Randall M.↗

NASA NTRS1989

Trends in environmentally induced spacecraft anomalies

The Spacecraft Anomaly Data Base was useful in identifying trends in anomaly occurrence. Trends alone do not provide quantitative testimony to a spacecraft's reliability, but they do indicate areas that command closer study. An in-depth analysis of a specific anomaly can be expensive and difficult without access to the spacecraft. Statistically verified anomaly trends can provide a good reference point to begin anomaly analysis. Many spacecraft experience an increase in anomalies during the period of several days centered on the solar equinox, a period that is also correlated with sun eclipse at geostationary altitude and an increase in major geomagnetic storms. Increase anomaly occurrence can also be seen during the local time interval between midnight and dawn. This local time interval represents a region in Earth's near space that experiences an enhancement in electron plasma density due to a migration from the magnetotail during or following a geomagnetic substorm.

Wilkinson, Daniel C.↗

NASA NTRS2000

Evolution of Tropical and Extratropical Precipitation Anomalies During the 1997 to 1999 ENSO Cycle

The 1997-1999 ENSO period was very powerful, but also well observed. Multiple satellite rainfall estimates combined with gauge observations allow for a quantitative analysis of precipitation anomalies in the tropics and elsewhere accompanying the 1997-99 ENSO cycle. An examination of the evolution of the El Nino and accompanying precipitation anomalies revealed that a dry Maritime Continent preceded the formation of positive SST anomalies in the eastern Pacific Ocean. 30-60 day oscillations in the winter of 1996/97 may have contributed to this lag relationship. Furthermore, westerly wind burst events may have maintained the drought over the Maritime Continent. The warming of the equatorial Pacific was then followed by an increase in convection. A rapid transition from El Nino to La Nina occurred in May 1998, but as early as October-November 1997 precipitation indices captured substantial changes in Pacific rainfall anomalies. The global precipitation patterns for this event were in good agreement with the strong consistent ENSO-related precipitation signals identified in earlier studies. Differences included a shift in precipitation anomalies over Africa during the 1997-98 El Nino and unusually wet conditions over northeast Australia during the later stages of the El Nino. Also, the typically wet region in the north tropical Pacific was mostly dry during the 1998-99 La Nina. Reanalysis precipitation was compared to observations during this time period and substantial differences were noted. In particular, the model had a bias towards positive precipitation anomalies and the magnitudes of the anomalies in the equatorial Pacific were small compared to the observations. Also, the evolution of the precipitation field, including the drying of the Maritime Continent and eastward progression of rainfall in the equatorial Pacific was less pronounced for the model compared to the observations.

Curtis, Scott↗

NASA NTRS2003

Satellite GN and C Anomaly Trends

On-orbit anomaly records for satellites launched from 1990 through 2001 are reviewed to determine recent trends of un-manned space mission critical failures. Anomalies categorized by subsystems show that Guidance, Navigation and Control (GN&C) subsystems have a high number of anomalies that result in a mission critical failure when compared to other subsystems. A mission critical failure is defined as a premature loss of a satellite or loss of its ability to perform its primary mission during its design life. The majority of anomalies are shown to occur early in the mission, usually within one year from launch. GN&C anomalies are categorized by cause and equipment type involved. A statistical analysis of the data is presented for all anomalies compared with the GN&C anomalies for various mission types, orbits and time periods. Conclusions and recommendations are presented for improving mission success and reliability.

Robertson, Brent↗

NASA NTRS2003

Reliability of CHAMP Anomaly Continuations

CHAMP is recording state-of-the-art magnetic and gravity field observations at altitudes ranging over roughly 300 - 550 km. However, anomaly continuation is severely limited by the non-uniqueness of the process and satellite anomaly errors. Indeed, our numerical anomaly simulations from satellite to airborne altitudes show that effective downward continuations of the CHAMP data are restricted to within approximately 50 km of the observation altitudes while upward continuations can be effective over a somewhat larger altitude range. The great unreliability of downward continuation requires that the satellite geopotential observations must be analyzed at satellite altitudes if the anomaly details are to be exploited most fully. Given current anomaly error levels, joint inversion of satellite and near- surface anomalies is the best approach for implementing satellite geopotential observations for subsurface studies. We demonstrate the power of this approach using a crustal model constrained by joint inversions of near-surface and satellite magnetic and gravity observations for Maude Rise, Antarctica, in the southwestern Indian Ocean. Our modeling suggests that the dominant satellite altitude magnetic anomalies are produced by crustal thickness variations and remanent magnetization of the normal polarity Cretaceous Quiet Zone.

vonFrese, Ralph R. B.↗

NASA NTRS2004

Identification of Interplanetary Coronal Mass Ejections at 1 AU Using Multiple Solar Wind Plasma Composition Anomalies

We investigate the use of multiple simultaneous solar wind plasma compositional anomalies, relative to the composition of the ambient solar wind, for identifying interplanetary coronal mass ejection (ICME) plasma. We first summarize the characteristics of several solar wind plasma composition signatures (O(+7)/O(+6), Mg/O, Ne/O, Fe charge states, He/p) observed by the ACE and WIND spacecraft within the ICMEs during 1996 - 2002 identsed by Cane and Richardson. We then develop a set of simple criteria that may be used to identify such compositional anomalies, and hence potential ICMEs. To distinguish these anomalies from the normal variations seen in ambient solar wind composition, which depend on the wind speed, we compare observed compositional signatures with those 'expected' in ambient solar wind with the same solar wind speed. This method identifies anomalies more effectively than the use of fixed thresholds. The occurrence rates of individual composition anomalies within ICMEs range from approx. 70% for enhanced iron and oxygen charge states to approx. 30% for enhanced He/p (> 0.06) and Ne/O, and are generally higher in magnetic clouds than other ICMEs. Intervals of multiple anomalies are usually associated with ICMEs, and provide a basis for the identification of the majority of ICMEs. We estimate that Cane and Richardson, who did not refer to composition data, probably identitied approx. 90% of the ICMEs present. However, around 10% of their ICMEs have weak compositional anomalies, suggesting that the presence of such signatures does not provide a necessary requirement for an ICME. We note a remarkably similar correlation between the Mg/O and O(7)/O(6) ratios in hourly-averaged data both within ICMEs and the ambient solar wind. This 'universal' relationship suggests that a similar process (such as minor ion heating by waves inside coronal magnetic field loops) produces the first-ionization potential bias and ion freezing-in temperatures in the source regions of both ICMEs and the ambient solar wind.

Richardson, I. G.↗

NASA NTRS2005

Discovering System Health Anomalies Using Data Mining Techniques

We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

Sriastava, Ashok, N.↗

NASA NTRS2011

Intercomparison of Recent Anomaly Time-Series of OLR as Observed by CERES and Computed Using AIRS Products

This paper compares recent spatial and temporal anomaly time series of OLR as observed by CERES and computed based on AIRS retrieved surface and atmospheric geophysical parameters over the 7 year time period September 2002 through February 2010. This time period is marked by a substantial decrease of OLR, on the order of +/-0.1 W/sq m/yr, averaged over the globe, and very large spatial variations of changes in OLR in the tropics, with local values ranging from -2.8 W/sq m/yr to +3.1 W/sq m/yr. Global and Tropical OLR both began to decrease significantly at the onset of a strong La Ni a in mid-2007. Late 2009 is characterized by a strong El Ni o, with a corresponding change in sign of both Tropical and Global OLR anomalies. The spatial patterns of the 7 year short term changes in AIRS and CERES OLR have a spatial correlation of 0.97 and slopes of the linear least squares fits of anomaly time series averaged over different spatial regions agree on the order of +/-0.01 W/sq m/yr. This essentially perfect agreement of OLR anomaly time series derived from observations by two different instruments, determined in totally independent and different manners, implies that both sets of results must be highly stable. This agreement also validates the anomaly time series of the AIRS derived products used to compute OLR and furthermore indicates that anomaly time series of AIRS derived products can be used to explain the factors contributing to anomaly time series of OLR.

Susskind, Joel↗

NASA NTRS2011

The Relationship Between El Nino/La Nina Oscillations and Recent Anomaly Time Series of OLR Determined by CERES and AIRS

This paper compares recent spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLRCLR (Clear Sky OLR) as determined using CERES and AIRS observations over the time period September 2002 through June 2010. We find excellent agreement in OLR anomaly time series of both data sets in almost every detail, down to the 1 x 1 spatial grid point level. This extremely close agreement of OLR anomaly time series derived from observations by two different instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper then examines anomaly time series of AIRS derived products over the extended time period September 2002 through April 2011. We show that OLR anomalies during this period are closely in phase with those of an El Nino index, and that the recent global and tropical mean decreases in OLR and OLRCLR are a result of a transition from an El Nino condition at the beginning of the data record to La Nina conditions toward the end of the data period. We show that the relationship between global mean, and especially tropical mean, OLR anomalies to the El Nino index can be explained by temporal changes of the distribution of mid-tropospheric water vapor and cloud cover in two spatial regions that are in direct response to El Nino/La Nina activity which occurs outside these spatial regions.

Susskind, Joel↗

NASA NTRS2010

Atmospheric Circulation Anomalies During Two Persistent North American Droughts: 1932-1939 and 1948-1957

We use an early twentieth century (1908-1958) atmospheric reanalysis, based on assimilation of surface and sea level pressure observations, to contrast atmospheric circulation during two periods of persistent drought in North America: 1932-1939 (the Dust Bowl) and 1948-1957. Primary forcing for both droughts is believed to come from anomalous sea surface temperatures (SSTs): a warm Atlantic and a cool eastern tropical Pacific. For boreal winter (October-March) in the 1950s, a stationary wave pattern originating from the tropical Pacific is present, with positive centers over the north Pacific and north Atlantic ocean basins and a negative center positioned over northwest North America and the tropical/subtropical Pacific. This wave train is largely absent for the 1930s drought; boreal winter height anomalies are organized much more zonally, with positive heights extending across northern North America. For boreal summer (April-September) during the 1930s, a strong upper level ridge is centered over the Great Plains; this feature is absent during the 1950s and appears to be linked to a weakening of the Great Plains low-level jet (GPLLJ). Subsidence anomalies are co-located over the centers of each drought: in the central Great Plains for the 1930s and in a band extending from the southwest to the southeastern United States for the 1950s. The location and intensity of this subsidence during the 1948-1957 drought is a typical response to a cold eastern tropical Pacific, but for 1932-1939 deviates in terms of the expected intensity, location, and spatial extent. Overall, circulation anomalies during the 1950s drought appear consistent with the expected response to the observed SST forcing. This is not the case for the 1930s, implying some other causal factor may be needed to explain the Dust Bowl drought anomalies. In addition to SST forcing, the 1930s were also characterized by massive alterations to the land surface, including regional-scale devegetation from crop failures and intensive wind erosion and dust storms. Incorporation of these land surface factors into a general circulation model greatly improves the simulation of precipitation and subsidence anomalies during this drought, relative to simulations with SST forcing alone. Even with additional forcing from the land surface, however, the model still has difficulty reproducing some of the other circulation anomalies, including weakening of the GPLLJ and strengthening of the upper level ridge during AMJJAS. This may be due to either weaknesses in the model or uncertainties in the boundary condition estimates. Still, analysis of the circulation anomalies supports the conclusion of an earlier paper (Cook et al. in Proc Natl Acad Sci 106:4997, 2009), demonstrating that land degradation factors are consistent with the anomalous nature of the Dust Bowl drought.

Cook, Benjamin↗

NASA NTRS2011

Does Terrestrial Drought Explain Global CO2 Flux Anomalies Induced by El Nino?

The El Nino Southern Oscillation is the dominant year-to-year mode of global climate variability. El Nino effects on terrestrial carbon cycling are mediated by associated climate anomalies, primarily drought, influencing fire emissions and biotic net ecosystem exchange (NEE). Here we evaluate whether El Nino produces a consistent response from the global carbon cycle. We apply a novel bottom-up approach to estimating global NEE anomalies based on FLUXNET data using land cover maps and weather reanalysis. We analyze 13 years (1997-2009) of globally gridded observational NEE anomalies derived from eddy covariance flux data, remotely-sensed fire emissions at the monthly time step, and NEE estimated from an atmospheric transport inversion. We evaluate the overall consistency of biospheric response to El Nino and, more generally, the link between global CO2 flux anomalies and El Nino-induced drought. Our findings, which are robust relative to uncertainty in both methods and time-lags in response, indicate that each event has a different spatial signature with only limited spatial coherence in Amazonia, Australia and southern Africa. For most regions, the sign of response changed across El Nino events. Biotic NEE anomalies, across 5 El Nino events, ranged from -1.34 to +0.98 Pg Cyr(exp -1, whereas fire emissions anomalies were generally smaller in magnitude (ranging from -0.49 to +0.53 Pg C yr(exp -1). Overall drought does not appear to impose consistent terrestrial CO2 flux anomalies during El Ninos, finding large variation in globally integrated responses from 11.15 to +0.49 Pg Cyr(exp -1). Despite the significant correlation between the CO2 flux and El Nino indices, we find that El Nino events have, when globally integrated, both enhanced and weakened terrestrial sink strength, with no consistent response across events

Schwalm. C. R.↗

NASA NTRS2018

Determination of Total Magnetic Anomalies and Their Vertical Gradients of Swarm-A Satellite over Central Europe and Pannonian Basin

Our paper discusses the determination of total magnetic field anomalies derived from the Swarm–A satellite data; one of two low orbiting satellites of the three Swarm formations. This procedure requires several modifications. The first step is the conversion of the measured CDF data to the ASCII format. This step is followed by the selection of the data with K(sub p) index ≤ 1(sub +). The anomalies are determined by the removal of the IGRF from the resulting satellite data. There are two Swarm–A data sets: descending (6000) orbits and ascending (5688) orbits. For our study the descending orbits were used. The next step of calculations is to determine the difference of the two-dimensional linear field fitted to the Swarm–A anomalies and the anomalies given. These anomalies are filtered by Gaussian low-pass filter. The last step of the corrections is the removal of the direct component, zero spatial frequency, from the descending anomalies. The anomalies and their vertical gradients are qualitatively interpreted over Central Europe and the Pannonian Basin.

Kis, K.↗

NASA NTRS2019

GRAIL-Identified Gravity Anomalies in Oceanus Procellarum: Insight into Subsurface Impact and Magmatic Structures on the Moon

Four, quasi-circular, positive Bouguer gravity anomalies (PBGAs) that are similar in diameter (~90-190 km) and gravitational amplitude (> 140 mGal contrast) are identified within the central Oceanus Procellarum region of the Moon. These spatially associated PBGAs are located south of Aristarchus Plateau, north of Flamsteed crater, and two are within the Marius Hills volcanic complex (north and south). Each is characterized by distinct surface geologic features suggestive of ancient impact craters and/or volcanic/plutonic activity. Here, we combine geologic analyses with forward modeling of high-resolution gravity data from the Gravity Recovery and Interior Laboratory (GRAIL) mission in order to constrain the subsurface structures that contribute to these four PBGAs. The GRAIL data presented here, at spherical harmonic degrees 6–660, permit higher resolution analyses of these anomalies than previously reported, and reveal new information about subsurface structures. Specifically, we find that the amplitudes of the four PBGAs cannot be explained solely by mare-flooded craters, as suggested in previous work; an additional density contrast is required to explain the high-amplitude of the PBGAs. For Northern Flamsteed (190 km diameter), the additional density contrast may be provided by impact-related mantle uplift. If the local crust has a density ~2800 kg/cu.m, then ~7 km of uplift is required for this anomaly, although less uplift is required if the local crust has a lower mean density of ~2500 kg/cu.m. For the Northern and Southern Marius Hills anomalies, the additional density contrast is consistent with the presence of a crustal complex of vertical dikes that occupies up to ~50% of the regionally thin crust. The structure of Southern Aristarchus Plateau (90 km diameter), an anomaly with crater-related topographic structures, remains ambiguous. Based on the relatively small size of the anomaly, we do not favor mantle uplift; however, understanding mantle response in a region of especially thin crust needs to be better resolved. It is more likely that this anomaly is due to subsurface magmatic material given the abundance of volcanic material in the surrounding region. Overall, the four PBGAs analyzed here are important in understanding the impact and volcanic/plutonic history of the Moon, specifically in a region of thin crust and elevated temperatures characteristic of the Procellarum KREEP Terrane.

Deutsch, Ariel N.↗

NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗

NASA NTRSDate not supplied

Air Data Probe Anomalies in Flight through Measured High Ice Water Content Conditions

High concentrations of ice crystals in convective storms have caused anomalous air temperature and airspeed readings during commercial and research flight operations. These anomalies occur when ice crystals are ingested in the heated probe inlet, melt or partially melt to liquid water, and then refreeze or remain in a liquid state depending on the probe heat and cloud conditions. In pitot probes, the refreezing may cause complete blockage of the total pressure, which causes airspeed anomalies. In total air temperature probes, the melted ice water may flow near the temperature sensing element and cause the total air temperature reading to approach 0 degree Celsius. During the High Ice Water Content (HIWC) RADAR and HIWC-2022 flight campaigns and the Convective Process Experiment (CPEX-CV) flight campaign, a total of 71 anomalies were recorded on the NASA DC-8 pitot probes when subjected to specific flight and cloud conditions. Similarly, a research TAT probe mounted near the pitot probes had 19 anomalies. This paper presents analyses of the measured natural conditions that led to these TAT and pitot anomalies, identifies two types of pitot probe anomalies, applies a concentration factor to estimate local TWC conditions near the TAT and pitot probes, and identifies the static air temperature and pressure altitude where the anomalies occurred on the Part 33 Appendix D envelope.

Aircraft Icing↗

NASA NTRSDate not supplied

Air Data Probe Anomalies in Flight through Measured High Ice Water Content Conditions

Aircraft Icing↗

DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗