Search NASA⌕ Search

SEARCH · Search NASA

Results for “Series Analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

QuakeSim 2.0

QuakeSim 2.0 improves understanding of earthquake processes by providing modeling tools and integrating model applications and various heterogeneous data sources within a Web services environment. QuakeSim is a multisource, synergistic, data-intensive environment for modeling the behavior of earthquake faults individually, and as part of complex interacting systems. Remotely sensed geodetic data products may be explored, compared with faults and landscape features, mined by pattern analysis applications, and integrated with models and pattern analysis applications in a rich Web-based and visualization environment. Integration of heterogeneous data products with pattern informatics tools enables efficient development of models. Federated database components and visualization tools allow rapid exploration of large datasets, while pattern informatics enables identification of subtle, but important, features in large data sets. QuakeSim is valuable for earthquake investigations and modeling in its current state, and also serves as a prototype and nucleus for broader systems under development. The framework provides access to physics-based simulation tools that model the earthquake cycle and related crustal deformation. Spaceborne GPS and Inter ferometric Synthetic Aperture (InSAR) data provide information on near-term crustal deformation, while paleoseismic geologic data provide longerterm information on earthquake fault processes. These data sources are integrated into QuakeSim's QuakeTables database system, and are accessible by users or various model applications. UAVSAR repeat pass interferometry data products are added to the QuakeTables database, and are available through a browseable map interface or Representational State Transfer (REST) interfaces. Model applications can retrieve data from Quake Tables, or from third-party GPS velocity data services; alternatively, users can manually input parameters into the models. Pattern analysis of GPS and seismicity data has proved useful for mid-term forecasting of earthquakes, and for detecting subtle changes in crustal deformation. The GPS time series analysis has also proved useful as a data-quality tool, enabling the discovery of station anomalies and data processing and distribution errors. Improved visualization tools enable more efficient data exploration and understanding. Tools provide flexibility to science users for exploring data in new ways through download links, but also facilitate standard, intuitive, and routine uses for science users and end users such as emergency responders.

Donnellan, Andrea↗
NASA NTRS1987

Periodic extinction of families and genera

Eight major episodes of biological extinction of marine families over the past 250 million years stand significantly above local background (P less than 0.05). These events are more pronounced when analyzed at the level of genus, and generic data exhibit additional apparent extinction events in the Aptian (Cretaceous) and Pliocene (Tertiary) Stages. Time-series analysis of these records strongly suggests a 26-million-year periodicity. This conclusion is robust even when adjusted for simultaneous testing of many trial periods. When the time series is limited to the four best-dated events (Cenomanian, Maestrichtian, upper Eocene, and middle Miocene), the hypothesis of randomness is also rejected for the 26-million-year period (P less than 0.0002).

Raup, David M.↗
NASA NTRS1986

Periodic extinction of families and genera

Eight major episodes of biological extinction of marine families over the past 250 million years stand significantly above local background (P < 0.05). These events are more pronounced when analyzed at the level of genus, and generic data exhibit additional apparent extinction events in the Aptian (Cretaceous) and Pliocene (Tertiary) Stages. Time-series analysis of these records strongly suggests a 26-million-year periodicity. This conclusion is robust even when adjusted for simultaneous testing of many trial periods. When the time series is limited to the four best-dated events (Cenomanian, Maestrichtian, upper Eocene, and middle Miocene), the hypothesis of randomness is also rejected for the 26-million-year period (P < 0.0002).

Non-NASA Center↗
NASA NTRS2017

Re-Organizing Earth Observation Data Storage to Support Temporal Analysis of Big Data

The Earth Observing System Data and Information System archives many datasets that are critical to understanding long-term variations in Earth science properties. Thus, some of these are large, multi-decadal datasets. Yet the challenge in long time series analysis comes less from the sheer volume than the data organization, which is typically one (or a small number of) time steps per file. The overhead of opening and inventorying complex, API-driven data formats such as Hierarchical Data Format introduces a small latency at each time step, which nonetheless adds up for datasets with O(10^6) single-timestep files. Several approaches to reorganizing the data can mitigate this overhead by an order of magnitude: pre-aggregating data along the time axis (time-chunking); storing the data in a highly distributed file system; or storing data in distributed columnar databases. Storing a second copy of the data incurs extra costs, so some selection criteria must be employed, which would be driven by expected or actual usage by the end user community, balanced against the extra cost.

data storage↗
NASA NTRS2019

MARGInS Model-Based Analysis of Realizable Goals in Systems

The high complexity of modern aircraft and spacecraft requires elaborate Verification and Validation (V&V) approaches to make sure that such complex systems work properly and reliably. MARGInS is a framework for the analysis, understanding, and prediction of the behavior of a complex, hybrid system. MARGInS contains a set of machine learning and statistical algorithms for multivariate clustering, treatment learning, critical factor determination, time-series analysis, event prediction, and safety-boundary detection and characterization. The framework supports system testing and can be configured to find novel features in test suites, determine classes of behavior, propose new experiments that can efficiently explore and characterize the boundaries between classes of system behavior, and to create visualizations and reports.

He, Yuning↗
NASA NTRSDate not supplied

Nonlinear Dynamic Control Derivative Analysis for Aircraft with Application to Transonic Truss-Braced Wing

This paper presents the development of a nonlinear dynamic control derivative estimation method. A nonlinear aerodynamic model is developed to account for the effects of large control surface deflections on aircraft aerodynamic forces and moments. A series of unsteady RANS CFD simulations is performed to simulate the control surface oscillations at various reduced frequencies. The time-domain data are transformed into the frequency-domain data by a Fourier series analysis. Transfer functions of the dynamic control derivatives are then estimated by a frequency-domain regression. The method is applied to the Transonic Truss-Braced Wing (TTBW) to estimate the dynamic control derivatives for the elevator, aileron, and rudder.

Aircraft Control Derivatives↗
NASA NTRS1993

Plio-Pleistocene time evolution of the 100-kyr cycle in marine paleoclimate records

A new time-series analysis technique (called envelope inversion), which is based on multiple taper spectral analysis, is used to address the question of an abrupt versus a gradual onset of the 100-kyr ice-age periodicity in the middle Pleistocene. Three long (greater than 2.6 m.y.) time series from Deep Sea Drilling Project site 607 (midlatitude Atlantic) and Ocean Drilling Program site 677 (equatorial Pacific) were analyzed using delta(O-18) records. Results do not yield compelling evidence for an abrupt change in the 100-kyr delta(O-18) signal. Rather, the results suggest that the 100-kyr delta(O-18) cycle is phase-locked with the 124-kyr eccentricity cycle some 300-400 kyr prior to its late Pleistocene growth in amplitude and phase lock with the 95-kyr eccentricity cycle.

Park, Jeffrey↗
NASA NTRS2004

The Latitude Dependence of the Effect of Pinatubo on Stratospheric Ozone

Statistical analysis of TOMS and SBLT total ozone data indicate that the eruption of Pinatubo in 1991 led to a significant decrease in ozone at northern midlatitudes with little or no effect at southern midlatitudes. We argue that this puzzling absence of a southern hemisphere effect may be an artifact of the statistical analysis. We have run a 3D CTM simulation of the past 30 years of stratospheric photochemistry with variable forcing due to chlorine/bromine compounds, solar ultraviolet radiation, and volcanic aerosols. This integration used winds from the FVGCM, which has similar interannual variability to the atmosphere. When this CTM output was examined with a standard time-series analysis, we found an effect of Pinatubo in the southern hemisphere, but not in the northern hemisphere. We then reran the CTM without volcanic aerosols. The subtraction of the two simulations indicated that, as expected, that Pinatubo affected both hemispheres in the model. This means that the northern hemisphere effect was in the model but did not show up in the statistical analysis. We also had an on-line parameterized chemical ozone tracer with seasonally repeating production and loss over the simulation. We used this as a dynamical surrogate to remove interannual variability from the original model output. The residual time series was then analyzed for the Pinatubo effect and we were able to find it in both hemispheres. We suggest that the combination of the two volcanoes, El Chichon and Pinatubo, with the solar cycle and interannual variability led to this problem of analysis in the northern hemisphere of our model. We furthermore suggest that a similar think may be occurring in the southern hemisphere of the data. An analysis of the atmosphere's southern hemisphere with a good dynamical surrogate may solve the mystery of the missing southern hemisphere effect of Pinatubo on ozone.

Stolarski, Richard S.↗
NASA NTRS1995

Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Smid, J.↗
NASA NTRS1978

Process air quality data

Air quality sampling was conducted. Data for air quality parameters, recorded on written forms, punched cards or magnetic tape, are available for 1972 through 1975. Computer software was developed to (1) calculate several daily statistical measures of location, (2) plot time histories of data or the calculated daily statistics, (3) calculate simple correlation coefficients, and (4) plot scatter diagrams. Computer software was developed for processing air quality data to include time series analysis and goodness of fit tests. Computer software was developed to (1) calculate a larger number of daily statistical measures of location, and a number of daily monthly and yearly measures of location, dispersion, skewness and kurtosis, (2) decompose the extended time series model and (3) perform some goodness of fit tests. The computer program is described, documented and illustrated by examples. Recommendations are made for continuation of the development of research on processing air quality data.

Butler, C. M.↗
NASA NTRS2018

PYSAT: Python Satellite Data Analysis Toolkit

A common problem in space science data analysis is combining complementary data sources that are provided and analyzed in different formats and programming languages. The Python Satellite Data Analysis Toolkit (pysat) addresses this issue by providing an open source toolkit that implements the general process of space science data analysis, from beginning to end, in an instrumentindependent manner. This toolkit uses an Instrument object that enables systematic analysis of science data from a variety of platforms within a single interface. Basic functions such as downloading, loading, and cleaning are included for all supported instruments. Common analysis routines are also included, which are instrument and data source independent. A nanokernel is used to provide instrument independence, it is attached to the Instrument object and mediates the systematic and arbitrary modification of loaded data. Pysat uses the nanokernel to improve the rigor of time series analysis, support onthefly orbit determination, and cleanly span file breaks. Pysat's functions and higherlevel scientific analysis features are validated through the use of unit testing. Further adoption by the community provides a set of scientific results produced by a common core, constituting a distributed heritage that supports the validity of the underlying processing and scientific output. These features are used to demonstrate consistency between derived electron density profiles and measured ion drifts, particularly downward ion drifts in the afternoon hours during extreme solar minimum. Pysat builds upon open source Python software that is freely available and encourages communitydriven development.

Stoneback, R.A.↗
NASA NTRS1974

Program for the analysis of time series

A digital computer program for the Fourier analysis of discrete time data is described. The program was designed to handle multiple channels of digitized data on general purpose computer systems. It is written, primarily, in a version of FORTRAN 2 currently in use on CDC 6000 series computers. Some small portions are written in CDC COMPASS, an assembler level code. However, functional descriptions of these portions are provided so that the program may be adapted for use on any facility possessing a FORTRAN compiler and random-access capability. Properly formatted digital data are windowed and analyzed by means of a fast Fourier transform algorithm to generate the following functions: (1) auto and/or cross power spectra, (2) autocorrelations and/or cross correlations, (3) Fourier coefficients, (4) coherence functions, (5) transfer functions, and (6) histograms.

Brown, T. J.↗
NASA NTRS1995

Heart rate and respiratory rhythm dynamics on ascent to high altitude

OBJECTIVE--To investigate the alterations in autonomic control of heart rate at high altitude and to test the hypothesis that hypoxaemic stress during exposure to high altitude induces non-linear, periodic heart rate oscillations, similar to those seen in heart failure and the sleep apnoea syndrome. SUBJECTS--11 healthy subjects aged 24-64. MAIN OUTCOME MEASURES--24 hour ambulatory electrocardiogram records obtained at baseline (1524 m) and at 4700 m. Simultaneous heart rate and respiratory dynamics during 2.5 hours of sleep by fast Fourier transform analysis of beat to beat heart rate and of an electrocardiographically derived respiration signal. RESULTS--All subjects had resting hypoxaemia at high altitude, with an average oxyhaemoglobin saturation of 81% (5%). There was no significant change in mean heart rate, but low frequency (0.01-0.05 Hz) spectral power was increased (P < 0.01) at high altitude. Time series analysis showed a complex range of non-linear sinus rhythm dynamics. Striking low frequency (0.04-0.06 Hz) heart rate oscillations were observed during sleep in eight subjects at high altitude. Analysis of the electrocardiographically derived respiration signal indicated that these heart rate oscillations correlated with low frequency respiratory oscillations. CONCLUSIONS--These data suggest (a) that increased low frequency power during high altitude exposure is not simply attributable to increased sympathetic modulation of heart rate, but relates to distinctive cardiopulmonary oscillations at approximately 0.05 Hz and (b) that the emergence of periodic heart rate oscillations at high altitude is consistent with an unstable cardiopulmonary control system that may develop on acute exposure to hypoxaemic stress.

Non-NASA Center↗
NASA NTRS2016

Assessing Spontaneous Combustion Instability with Recurrence Quantification Analysis

Spontaneous instabilities can pose a significant challenge to verification of combustion stability, and characterizing its onset is an important avenue of improvement for stability assessments of liquid propellant rocket engines. Recurrence Quantification Analysis (RQA) is used here to explore nonlinear combustion dynamics that might give insight into instability. Multiple types of patterns representative of different dynamical states are identified within fluctuating chamber pressure data, and markers for impending instability are found. A class of metrics which describe these patterns is also calculated. RQA metrics are compared with and interpreted against another metric from nonlinear time series analysis, the Hurst exponent, to help better distinguish between stable and unstable operation.

Eberhart, Chad J.↗
NASA NTRS2004

Interannual Variations In the Low-Degree Components of the Geopotential derived from SLR and the Connections With Geophysical/Climatic Processes

Recent Satellite Laser Ranging derived long wavelength gravity time series analysis has focused to a large extent on the effects of the recent large changes in the Earth s 52, and the potential causes. However, it is difficult to determine whether there are corresponding signals in the shorter wavelength zonals from the existing SLR-derived time variable gravity results, although it appears that geophysical fluid transport is being observed. For example, the recovered J3 time series shows remarkable agreement with NCEP-derived estimates of atmospheric gravity variations. Likewise, some of the non-zonal spherical harmonic coefficient series have significant interannual signal that appears to be related to mass transport. The non-zonal degree 2 terms show reasonable correlation with atmospheric signals, as well as climatic effects such as El Nino Southern Oscillation. While the formal uncertainty of these terms is significantly higher than that for J2, it is also clear that there is useful signal to be extracted. Consequently, the SLR time series is being reprocessed to improve the time variable gravity field recovery. We will present recent updates on the J2 evolution, as well as a look at other components of the interannual variations of the gravity field, complete through degree 4, and possible geophysical and climatic causes.

Chao, Benjamin F.↗
NASA NTRS2004

Virtual Sensors: Using Data Mining to Efficiently Estimate Spectra

Detecting clouds within a satellite image is essential for retrieving surface geophysical parameters, such as albedo and temperature, from optical and thermal imagery because the retrieval methods tend to be valid for clear skies only. Thus, routine satellite data processing requires reliable automated cloud detection algorithms that are applicable to many surface types. Unfortunately, cloud detection over snow and ice is difficult due to the lack of spectral contrast between clouds and snow. Snow and clouds are both highly reflective in the visible wavelen,ats and often show little contrast in the thermal Infrared. However, at 1.6 microns, the spectral signatures of snow and clouds differ enough to allow improved snow/ice/cloud discrimination. The recent Terra and Aqua Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensors have a channel (channel 6) at 1.6 microns. Presently the most comprehensive, long-term information on surface albedo and temperature over snow- and ice-covered surfaces comes from the Advanced Very High Resolution Radiometer ( AVHRR) sensor that has been providing imagery since July 1981. The earlier AVHRR sensors (e.g. AVHRR/2) did not however have a channel designed for discriminating clouds from snow, such as the 1.6 micron channel available on the more recent AVHRR/3 or the MODIS sensors. In the absence of the 1.6 micron channel, the AVHRR Polar Pathfinder (APP) product performs cloud detection using a combination of time-series analysis and multispectral threshold tests based on the satellite's measuring channels to produce a cloud mask. The method has been found to work reasonably well over sea ice, but not so well over the ice sheets. Thus, improving the cloud mask in the APP dataset would be extremely helpful toward increasing the accuracy of the albedo and temperature retrievals, as well as extending the time-series of albedo and temperature retrievals from the more recent sensors to the historical ones. In this work, we use data mining methods to construct a model of MODIS channel 6 as a function of other channels that are common to both MODIS and AVHRR. The idea is to use the model to generate the equivalent of MODIS channel 6 for AVHRR as a function of the AVHRR equivalents to MODIS channels. We call this a Virtual Sensor because it predicts unmeasured spectra. The goal is to use this virtual channel 6. to yield a cloud mask superior to what is currently used in APP . Our results show that several data mining methods such as multilayer perceptrons (MLPs), ensemble methods (e.g., bagging), and kernel methods (e.g., support vector machines) generate channel 6 for unseen MODIS images with high accuracy. Because the true channel 6 is not available for AVHRR images, we qualitatively assess the virtual channel 6 for several AVHRR images.

Srivastava, Ashok↗
NASA NTRS2012

Using NASA's Giovanni System to Simulate Time-Series Stations in the Outflow Region of California's Eel River

Oceanographic time-series stations provide vital data for the monitoring of oceanic processes, particularly those associated with trends over time and interannual variability. There are likely numerous locations where the establishment of a time-series station would be desirable, but for reasons of funding or logistics, such establishment may not be feasible. An alternative to an operational time-series station is monitoring of sites via remote sensing. In this study, the NASA Giovanni data system is employed to simulate the establishment of two time-series stations near the outflow region of California s Eel River, which carries a high sediment load. Previous time-series analysis of this location (Acker et al. 2009) indicated that remotely-sensed chl a exhibits a statistically significant increasing trend during summer (low flow) months, but no apparent trend during winter (high flow) months. Examination of several newly-available ocean data parameters in Giovanni, including 8-day resolution data, demonstrates the differences in ocean parameter trends at the two locations compared to regionally-averaged time-series. The hypothesis that the increased summer chl a values are related to increasing SST is evaluated, and the signature of the Eel River plume is defined with ocean optical parameters.

Acker, James G.↗
NASA NTRS2017

Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS) were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction) processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R (sup 2) equals 0.36-0.8), which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R (sup 2) is greater than 0.6 in all cases). The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

Ulsig, Laura↗