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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Extreme Precipitation Over the Southern Slope of the Tibetan Plateau and the Associated Atmospheric Circulation Anomalies

The southern slope of the Tibetan Plateau (SSTP) is one of the rainiest regions in the world where geological hazards caused by extreme precipitation often occur. This study investigates the characteristics and mechanisms of extreme precipitation over SSTP from June to September during 2001–2020 using Global Precipitation Measurement satellite observation. The extreme precipitation days are defined as the days with top 5% of regional-mean daily precipitation over SSTP in this period, which has an average precipitation of 27.2 mm/d. Averaging over the extreme precipitation days, precipitation peaks at an altitude of about 300 m, coinciding with the climatological maximum precipitation, but with a much larger value of 37.2 mm/d than the climatology of 11.9 mm/d. Composite analysis of circulations on extreme days reveals significant circulation anomalies in both the lower and upper troposphere. Specifically, the lower-tropospheric circulations are characterized by significant westerly anomalies over northern India, and the upper-tropospheric circulations are characterized by northerly anomalies over the central Tibetan Plateau, which are statistically independent. The lower-tropospheric westerly anomalies blowing toward SSTP are blocked by the topography, favoring extreme precipitation over SSTP. The upper-tropospheric northerly anomalies, on the other hand, correspond to anomalous northeasterlies north of SSTP in the middle troposphere and southeasterlies to the south in the lower troposphere, and the convergence of these circulation anomalies favors extreme precipitation over SSTP. Lastly, the lower-tropospheric westerly and upper-tropospheric northerly anomalies, respectively, correspond to less precipitation over the South Asian monsoon region and the Tibetan Plateau.

Extreme precipitation, Tibetan Plateau↗
DOE OSTI2025

Effect of Rocky Mountains and Tibetan Plateau 1998 Spring Land Temperature on N. American and East Asian Summer Precipitation Anomalies

This work follows up on the GEWEX/LS4P Phase I (LS4P-I) experiments, a community effort highlighting the spring land surface temperature anomalies in the Tibetan Plateau (TP) as a useful source for subseasonal to seasonal (S2S) prediction of summer precipitation in global hot spot regions, particularly in East Asia and North America. This paper extends the investigation to both the US Rocky Mountain (RM) region and the TP, considering the 1998 summer drought/flood event in North America/East Asia, respectively, as a case study. A previously developed initialization method for land surface temperature/subsurface temperature (LST/SUBT) is used in the NCEP Global Forecast System, coupled with a land model, SSiB2 (GFS/SSiB2), to produce observed RM cold May temperature anomaly. Forward simulation yields June precipitation anomalies at five remote locations. Likewise, the TP warm May temperature anomaly also produces June precipitation anomalies at these five locations. The effects of RM (cold) and TP (warm) temperature anomalies are consistent in the US South Coastal regions and the south Yangtze River Basin, yielding 49% (42%) of observed drought and 34% (44%) of observed flood, respectively. These LST/SUBT effects in RM and TP induce a global large-scale wave train linking North America with the TP, affecting the subtropical westerly jet and thereby modulating summer precipitation. Global SST effect is examined for comparison but does not yield statistically significant June precipitation anomalies in GFS/SSiB2. Furthermore, this study adds to evidence that high-mountain LST effects in the RM and TP are first-order sources of S2S precipitation predictability in summer months.

Nayak, Hara Prasad [University of California, Los ↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Multivariate Time Series Anomaly Detection with Few Positive Samples

Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned representations enable anomaly detection as the normality model is trained to capture certain key underlying data regularities under normal circumstances. In practical settings, particularly industrial time series anomaly detection, we often encounter situations where a large amount of normal operation data is available along with a small number of anomaly events collected over time. This practical situation calls for methodologies to leverage these small number of anomaly events to create a better anomaly detector. In this paper, we introduce two methodologies to address the needs of this practical situation and compared them with recently developed state of the art techniques. Our proposed methods anchor on representative learning of normal operation with autoregressive (AR) model along with loss components to encourage representations that separate normal versus few positive examples. We applied the proposed methods to two industrial anomaly detection datasets and demonstrated effective performance in comparison with approaches from literature. Our study also points out additional challenges with adopting such methods in practical applications.

Xue, Feng↗
DOE OSTI2022

MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series

In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, being able to detecting anomalies from streams of multivariate time series data is of significant importance. Given the scarcity of anomalies in real-world applications, the majority of literature has been focusing on modeling normality. The learned normal representations can empower anomaly detection as the model has learned to capture certain key underlying data regularities. A typical formulation is to learn a predictive model, i.e., use a window of time series data to predict future data values. In this paper, we propose an alternative self-supervised learning task. By randomly masking a portion of the inputs and training a model to estimate them using the remaining ones, MAD is an improvement over the traditional left-to-right next step prediction (NSP) task. Our experimental results demonstrate that MAD can achieve better anomaly detection rates over traditional NSP approaches when using exactly the same neural network (NN) base models, and can be modified to run as fast as NSP models during test time on the same hardware, thus making it an ideal upgrade for many existing NSP-based NN anomaly detection models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

The HSA is a statistics based anomaly detection model. The model performs unsupervised anomaly detection, based on a datapoint's density and similarity within a dataset. Density and similarity data are encoded into an affinity matrix. The affinity matrix is evolved to summarize the data's structure on greater topographical scales within the data's function space. The set of evolved affinity matrices and an anomaly score vector are passed to a user defined penalized objective function. The penalized objective function of anomaly scores is then minimized. Data points where the absolute value of the z-scores of anomaly scores greater than a specified threshold are predicted as anomalies. A novel multi-filter feature has also been implemented. To reduce false positive rates, the multi-filter records the indexes of the HSA predictions. A new dataset and data loader are instantiated consisting of all the initial HSA predictions and non-anomalous data points in a 10% and 90% split respectively. The HSA is then run through this data set and a count of number of times a data point is predicted is kept. In this way the initial predictions may be compared with data spanning the entire dataset. After the multi-filter is complete, all datapoints will have an associated anomaly score, as well as a multi-filter prediction count to further filter the anomalous predictions.

Rogers, DempseyD [Idaho National Laboratory (INL),↗
NASA NTRS1978

Barium and neodymium isotopic anomalies in the Allende meteorite

The discovery of Ba and Nd isotopic anomalies in two inclusions from the Allende meteorite is reported. The inclusions are Ca-Al-rich objects typical of the type considered as high-temperature condensation products in the solar nebula and contain distinctive Mg and O isotopic anomalies of the FUN (mass Fractionation, Unknown Nuclear processes) type. Mass-spectrometry results are discussed which show that inclusion C1 has anomalies in Ba at masses 134 and 136, while inclusion EK1-4-1 exhibits large marked negative anomalies at 130, 132, 134, and 136, as well as a positive anomaly at 137. It is also found that inclusion EK1-4-1 shows marked negative anomalies in Nd at masses 142, 146, 148, and 150, in addition to a positive anomaly at 145. These isotopic shifts are attributed to addition of r-process nuclei rather than mass fractionation. It is suggested that an onion-shell supernova explosion followed by injection into the solar nebula is the most likely generic model that may explain the observations.

Mcculloch, M. T.↗
NASA NTRS1979

Detailed gravity anomalies from Geos 3 satellite altimetry data

Detailed gravity anomalies are computed from a combination of Geos 3 satellite altimeter and terrestrial gravity data using least-squares principles. The mathematical model used is based on the Stokes' equation modified for a nonglobal solution. Using Geos 3 data in the calibration area, the effects of several anomaly parameter configurations and data densities/distributions on the anomalies and their accuracy estimates are studied. The accuracy estimates for 1 deg x 1 deg mean anomalies from low density altimetry data are of the order of 4 mgal. Comparison of these anomalies with the terrestrial data and also with Rapp's data derived using collocation techniques show rms differences of 7.2 and 4.9 mgal, respectively. Indications are that the anomaly accuracies can be improved to about 2 mgal with high density data. Estimation of 30 in. x 30 in. mean anomalies indicates accuracies of the order of 5 mgal. Proper verification of these results will be possible only when accurate ground truth data become available.

Gopalapillai, G. S.↗
NASA NTRS1981

MAGSAT anomaly map and continental drift

Anomaly maps of high quality are needed to display unambiguously the so called long wave length anomalies. The anomalies were analyzed in terms of continental drift and the nature of their sources is discussed. The map presented confirms the thinness of the oceanic magnetized layer. Continental magnetic anomalies are characterized by elongated structures generally of east-west trend. Paleomagnetic reconstruction shows that the anomalies found in India, Australia, and Antarctic exhibit a fair consistency with the African anomalies. It is also shown that anomalies are locked under the continents and have a fixed geometry.

Lemouel, J. L.↗
NASA NTRS1985

A GCM Study of the Atmospheric Response to Tropical SST Anomalies

The purpose is to understand the evolution of the atmospheric anomalies associated with the most recent warm episode by the use of simulation studies with the UCLA general circulation model (GCM). The approach is to integrate the model using the observed sequence of Sea Surface Temperature (SST) anomalies during 1982 to 1983, and compare it with a control run in which all boundary conditions vary from month to month as in the climatology. The control for the experiment was a three year simulation using seasonally varying climatological SSTs. The anomaly calculation was initialized from 15 June of the first year of the control. From 15 June to 1 July, the SST was gradually modified by the anomalies observed during June and July of 1982. From 1 July on, the run was continued using the control's SST plus the 1982 to 1983 anomalies. SSTs were varied daily, interpolating between monthly means. This second integration as carried to the end of February of the second year (1983 in the anomaly). Only anomalies over the tropical Pacific were used.

Suarez, M. J.↗
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.↗