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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.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2024

Streamflow in the United States: Characteristics, trends, regime shifts, and extremes

Long-term streamflow observations contain essential information for understanding hydrological changes and managing water resources. A continental-scale dataset or analysis of temporal streamflow change is still lacking across hydrologic gauges in the Conterminous United States (CONUS). Here, we compiled 70 years of streamflow records from 1951 to 2021 at ~ 8000 hydrologic stations across the CONUS and characterized temporal trends, regime shifts, and extreme events using a Bayesian time series analysis algorithm. We found that the occurrences of sudden streamflow changes (e.g., regime shifts and extreme events) have been increasing with time across the CONUS. In addition, we derived 181 streamflow indicators that are valuable for hydrological and biological applications, such as the duration and frequency of high or low streamflow events. The Mississippi River Basin, especially the middle and lower parts, was a hot spot of high-frequency high-flow events. Overall, we anticipate the dataset generated here offers valuable information for understanding and quantifying changes in water resources across the CONUS.

54 ENVIRONMENTAL SCIENCES↗
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 NTRS2000

Towards Solving the Mixing Problem in the Decomposition of Geophysical Time Series by Independent Component Analysis

The use of the Principal Component Analysis technique for the analysis of geophysical time series has been questioned in particular for its tendency to extract components that mix several physical phenomena even when the signal is just their linear sum. We demonstrate with a data simulation experiment that the Independent Component Analysis, a recently developed technique, is able to solve this problem. This new technique requires the statistical independence of components, a stronger constraint, that uses higher-order statistics, instead of the classical decorrelation a weaker constraint, that uses only second-order statistics. Furthermore, ICA does not require additional a priori information such as the localization constraint used in Rotational Techniques.

Aires, Filipe↗
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.↗
DOE OSTI2022

Improved Data Interpretation through Identification of Time Series Periodicity Changes

Analysis and interpretation of time series data is easiest when the data values occur at uniform intervals in time, but actual data may have differing data sampling frequencies, such as monthly and daily readings. Applying data analysis techniques, such as smoothing, to such a data set may not give a representative result between time segments. The ability to automatically distinguish time segments of differing data frequency would provide a means for applying data analysis independently to each segment, though a suitable blending at segment boundaries would be required. A method for detecting frequency changes was developed and applied to Gaussian and median smoothing of hydraulic head data from groundwater wells at the U.S. Department of Energy Hanford Site in southeastern Washington state. The process identifies time segments of high-frequency (daily) or low-frequency (greater than daily) data using adjusted-bandwidth Gaussian kernel density estimation and a threshold value, which are further refined to address small blocks of low-frequency data within larger blocks of high-frequency data. User-selectable levels of smoothing are then applied independently to the time segments prior to combining the segment results for a single smoothed data set. This time segment identification approach provides effective low- and high-frequency data separation, which provides a method to apply data analysis independently to each time segment.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2018

NASA’s Mid-Atlantic Communities and Areas at Intensive Risk Demonstration: Translating Compounding Hazards to Societal Risk

Remote sensing provides a unique perspective on our dynamic planet, tracking changes and revealing the course of complex interactions. Long term monitoring and targeted observation combine with modeling and mapping to provide increased awareness of hydro-meteorological and geological hazards. Disasters often follow hazards and the goal of NASA’s Disasters Program is to look at the earth as a highly coupled system to reduce risk and enable resilience. Remote sensing and geospatial science are used as tools to help answer critical questions that inform decisions. Data is not the same as information, nor does understanding of processes necessarily translate into decision support for disaster preparedness, response and recovery. Accordingly, NASA is engaging the scientific and decision-support communities to apply remote sensing, modeling, and related applications in Communities and Areas at Intensive Risk (CAIR). In 2017, NASA’s Applied Sciences Disasters Program hosted a regional workshop to explore these issues with particular focus on coastal Virginia and North Carolina. The workshop brought together partners in academia, emergency management, and scientists from NASA and partnering federal agencies to explore capabilities among the team that could improve understanding of the physical processes related to these hazards, their potential impact to changing communities, and to identify methodologies for supporting emergency response and risk mitigation. The resulting initiative, the mid-Atlantic CAIR project, demonstrates the ability to integrate satellite derived earth observations and physical models into actionable, trusted knowledge. Severe storms and associated storm surge, sea level rise, and land subsidence coupled with increasing populations and densely populated, aging critical infrastructure often leave coastal regions and their communities extremely vulnerable. The integration of observations and models allow for a comprehensive understanding of the compounding risk experienced in coastal regions and enables individuals in all positions make risk-informed decisions. This initiative uses a representative storm surge case as a baseline to produce flood inundation maps. These maps predict building level impacts at current day and for sea level rise (SLR) and subsidence scenarios of the future in order to inform critical decisions at both the tactical and strategic levels. To accomplish this analysis, the mid-Atlantic CAIR project brings together Federal research activities with academia to examine coastal hazards in multiple ways: 1) reanalysis of impacts from 2011 Hurricane Irene, using numerical weather modeling in combination with coastal surge and hydrodynamic, urban inundation modeling to evaluate combined impact scenarios considering SLR and subsidence, 2) remote sensing of flood extent from available optical imagery, 3) adding value to remotely sensed flood maps through depth predictions, and 4) examining coastal subsidence as measured through time-series analysis of synthetic aperture radar observations. Efforts and results are published via ArcGIS story maps to communicate neighborhoods and infrastructure most vulnerable to changing conditions. Story map features enable time-aware flood mapping using hydrodynamic models, photographic comparison of flooding following Hurricane Irene, as well as visualization of heightened risk in the future due to SLR and land subsidence.

Rogers, Laura↗
DOE OSTI2025

Evolving physical and mechanical rock properties during exposure at Earth's surface

Rocks at Earth's surface experience progressive damage due to exposure to environmental conditions. Although environmental damage to rocks is recognized as being time-dependent, our quantitative understanding of its effects on physical and mechanical properties remains limited by field and laboratory studies constrained to short timescales. In this study, we aim to rigorously quantify how these properties evolve under longer natural environmental exposure spanning from the Holocene to the late Pleistocene. We conducted a ‘time series’ analysis of key physical and mechanical rock properties using multiple interior samples from 10 cogenetic granitoid boulders exposed for different amounts of time at two sites in Eastern California, USA. We find that over the late Pleistocene (~30 ka), rocks exhibit porosity increases of 40 ± 4 % and seismic velocity decreases of 7 ± 1 % at both sites. Similarly, over the same time period, rock tensile and compressive strengths declined by an average of 11 ± 4 %, and rock permeability increased on average by 104 ± 50 %. Microscopic analysis of thin sections of the same rocks indicates that microfracture density and intensity also progressively increased over the same timescales. These results suggest that time-dependent fracture propagation due to environmental exposure is likely a central contributor to the observed changes in rock properties. This study provides quantitative documentation that pronounced changes in the physical and mechanical properties of rock progressively occur within thousands of years of exposure.

Critical zone↗
NASA NTRS1981

Some analysis on the diurnal variation of rainfall over the Atlantic Ocean

Data collected from the GARP Atlantic Tropical Experiment (GATE) was examined. The data were collected from 10,000 grid points arranged as a 100 x 100 array; each grid covered a 4 square km area. The amount of rainfall was measured every 15 minutes during the experiment periods using c-band radars. Two types of analyses were performed on the data: analysis of diurnal variation was done on each of grid points based on the rainfall averages at noon and at midnight, and time series analysis on selected grid points based on the hourly averages of rainfall. Since there are no known distribution model which best describes the rainfall amount, nonparametric methods were used to examine the diurnal variation. Kolmogorov-Smirnov test was used to test if the rainfalls at noon and at midnight have the same statistical distribution. Wilcoxon signed-rank test was used to test if the noon rainfall is heavier than, equal to, or lighter than the midnight rainfall. These tests were done on each of the 10,000 grid points at which the data are available.

Gill, T.↗
NASA NTRS1995

The IUE MEGA Campaign: The Rotationally Modulated Wind of zeta Puppis

We discuss 16 days of intensive International Ultraviolet Explorer (IUE) observations of the Si iv doublet (lambda1400) in the spectrum of the O4 I(n) f star zeta Pup. The data show continuous variability throughout the greater part of the blueshifted absorption. Time series analysis of these data reveals significant power at periods of 19.2 hr and 5.2 days, which we identify with the mean recurrence time of Discrete Absorption Components (DACs) and the photospheric rotation period, respectively. These results indicate that the wind has a global longitudinal asymmetry (approaching a factor 2 in optical depth), possibly associated with large-scale magnetic structures, but suggest that the DACs are not directly associated with specific stellar longitudes in this star. There is no significant power in the lines at the 8.5 hr period identified in photospheric absorption-line variability, nor at the 16.7 hr period reported in X-ray observations.

Howarth, Ian D.↗
NASA NTRS2023

Machine Learning Framework for Hazard Extraction and Analysis of Trends (HEAT) in Wildfire Response

This research proposes a natural language processing enabled risk analysis framework, named Hazard Extraction andAnalysis of Trends (HEAT), and applies the framework to the ICS-209-PLUS data set of wildfire incident responseforms. The HEAT framework produces safety- and risk- relevant analyses, consisting of: (1) a set of hazards extractedfrom text data, (2) a primary analysis using hazard-relevant metrics, such as rate and severity, to form an FMEA-styletable and risk matrix, (3) a time series analysis of metric trends, and (4) a secondary analysis examining potentialpredictors for hazards. Results from HEAT provide quantitative risk-relevant information for high-level hazards doc-umented in existing-state operations. Because of the generalizability of the steps and limited data requirements, HEATcan be applied to any dataset containing narrative text, thus providing a framework for data-driven machine learning-enabled quantitative risk analysis across a variety of domains. To demonstrate HEAT in a case study, we apply theframework to the ICS-209-PLUS dataset of wildland fire incident response forms. Hazards identified in wildfire re-sponse arise from environmental conditions, the mission, and the wildland urban interface. The resulting risk matrixidentifies evacuations as high-risk hazards, while all other identified hazards are medium or serious risk.

natural language processing↗
NASA NTRS2008

Global Characterization of Biomass-Burning Patterns using Satellite Measurements of Fire Radiative Energy

Remote sensing is the most practical means of measuring energy release from large open-air biomass burning. Satellite measurement of fire radiative energy (FRE) release rate or power (FRP) enables distinction between fires of different strengths. Based on a 1-km resolution fire data acquired globally by the MODerate-resolution Imaging Spectro-radiometer (MODIS) sensor aboard the Terra and Aqua satellites from 2000 to 2006, instanteaneous FRP values ranged between 0.02 MW and 1866 MW, with global daily means ranging between 20 and 40 MW. Regionally, at the Aqua-MODIS afternoon overpass, the mean FRP values for Alaska, Western US, Western Australia, Quebec and the rest of Canada are significantly higher than these global means, with Quebec having the overall highest value of 85 MW. Analysis of regional mean FRP per unit area of land (FRP flux) shows that a peak fire season in certain regions, fires can be responsible for up to 0.2 W/m(sup 2) at peak time of day. Zambia has the highest regional monthly mean FRP flux of approximately 0.045 W/m(sup 2) at peak time of day and season, while the Middle East has the lowest value of approximately 0.0005 W/m(sup 2). A simple scheme based on FRP has been devised to classify fires into five categories, to facilitate fire rating by strength, similar to earthquakes and hurricanes. The scheme uses MODIS measurements of FRP at 1-km resolution as follows: catagory 1 (less than 100 MW), category 2 (100 to less than 500 MW), category 3 (500 to less than 1000 MW), category 4 (1000 to less than 1500 MW), catagory 5 (greater than or equal to 1500 MW). In most regions of the world, over 90% of fires fall into category 1, while only less than 1% fall into each of categories 3 to 5, although these proportions may differ significantly from day to day and by season. The frequency of occurence of the larger fires is region specific, and could not be explained by ecosystem type alone. Time-series analysis of the propertions of higher category fires based on MODIS measured FRP from 2002 to 2006 does not show any moticeable trend because of the short time period.

Ichoku, Charles↗
NASA NTRS1985

X ray microbursts and VLF chorus

On January 4, 1978, at 1140 UT, a SuperArcas sounding rocket was launched from Siple Station, Antarctica (L = 4.2, 76 deg S, 84 deg W), during a geomagnetically disturbed period (Kp = 6-) with intense X-ray and VLF chorus activity. The parachuted payload observed an intense microburst precipitation event of 10-minute duration. These data have been correlated with measurements of FLF chorus by receivers on the ground at both Siple and its magnetic conjugate point, Roberval, Quebec. Detailed one-to-one correspondence between the microbursts and the chorus was not a consistent feature of the data. Time series analysis fo the data did indicate a significant correlation between the Siple X-ray precipitation and the Roberval VLF waves with an arrival time delay of 0.1 + or 0.3.

Roeder, J. L.↗
NASA NTRS1976

Evidence for the regulation of solar wind heat flux at 1 AU

Observational evidence favoring the local regulation of solar-wind heat flux at 1 AU is reviewed, and four months of IMP 6 plasma and magnetic-field data are merged and analyzed in order to investigate what might be regulating the heat flux. A statistical analysis of the data shows that the solar-wind Alfven speed is probably regulating the heat flux locally at 1 AU and that the Alfven speed, the velocity difference between the peak of low-energy electrons and the bulk plasma velocity, and the solar-wind velocity component projected along the local spiral angle are statistically well correlated for Alfven speeds not exceeding about 70 km/s. A time-series analysis of the data indicates that only the Alfven speed and the velocity difference between the peak of low-energy electrons and the bulk plasma velocity are well correlated both qualitatively and quantitatively on a microscopic time scale. It is strongly suggested that, at times, the solar-wind heat flux is locally regulated by the magnitude of the Alfven speed at 1 AU. Uncertainties in the results are discussed.

Feldman, W. C.↗
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 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 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.↗