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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Application of GRACE to the Estimation of Groundwater Storage Change in a Data-poor Region: A Case Study of Ngadda Catchment in the Lake Chad Basin

The present study is to explore the feasibility of GRACE-based estimation of a groundwater storage change in a data-poor region using a case study of the Ngadda catchment in the Lake Chad Basin. Although the Ngadda catchment has only one set of in situ time series data of groundwater from 2006 to 2009 and a limited number of groundwater measurements in 2005 and 2009, GRACE-based groundwater storage change can be evaluated against thein situ groundwater measurements combined with specific yield data. The cross-correlation analysis in the Ngadda catchment shows that maximum rainfall reached in July and August, whereas both the maximum total water storage anomaly and the maximum groundwater storage anomaly occurred 2months later. Whereas the mean annual amplitude of total water storage anomaly is about 17cm from both the average total water storage anomaly from three mascon products and the one from three spherical harmonic products, the mean annual amplitude of soil moisture storage anomaly is substantially varied from5.58cm for CLM to about 14cm for NOAH and Mosaic. The goodness-of-fit tests show that CLM soil moisture produces the closest estimation of groundwater storage anomaly to thein situ groundwater measurements. The present study shows that GRACE-based estimation for groundwater storage anomaly can be a cost-effective and alternative tool to observe how groundwater changes in a basin scale under the limitation of modelling and in situ data availability.

GRACE↗
NASA NTRS1975

Snow cover monitoring by machine processing of multitemporal LANDSAT MSS data

LANDSAT frames were geometrically corrected and data sets from six different dates were overlaid to produce a 24 channel (six dates and four wavelength bands) data tape. Changes in the extent of the snowpack could be accurately and easily determined using a change detection technique on data which had previously been classified by the LARSYS software system. A second phase of the analysis involved determination of the relationship between spatial resolution or data sampling frequency and accuracy of measuring the area of the snowpack.

Luther, S. G.↗
NASA NTRS1999

Improvement of TOPEX/POSEIDON and Jason-1 Geophysical Data Record for Global Change Studies and Coastal Applications

The Earth's modem climate change has been characterized by interlinked changes in temperature, CO2, ice sheets and sea level. Global sea level change is a critical indicator for study of contemporary climate change. Sea level rise appears to have accelerated since the ice sheet retreats have stopped some 5000 years ago and it is estimated that the sea level rise has been approx. 15 cm over the last century. Contemporary radar altimeters represent the only technique capable of monitoring global sea level change with accuracy approaching 1 mm/yr and with a temporal scale of days and a spatial scale of 100 km or longer. This report highlights the major accomplishments of the TOPEX/POSEIDON (T/P) Extended Mission and Jason-1 science investigation. The primary objectives of the investigation include the calibration and improvement of T/P and Jason-1 altimeter data for global sea level change and coastal tide and circulation studies. The scientific objectives of the investigation include: (1) the calibration and improvement of T/P and Jason-1 data as a reference measurement system for the accurate cross-linking with other altimeter systems (Seasat, Geosat, ERS-1, ERS-2, GFO-1, and Envisat), (2) the improved determination and the associated uncertainties of the long-term (15-year) global mean sea level change using multiple altimeters, (3) the characterization of the sea level change by analyses of independent data, including tide gauges, sea surface temperature, and (4) the improvement coastal radar altimetry for studies including coastal ocean tide modeling and coastal circulation. Major accomplishments of the investigation include the development of techniques for low-cost radar altimeter absolute calibration (including the associated GPS-buoy technology), coastal ocean tide modeling, and the linking of multiple altimeter systems and the resulting determination of the 15-year (1985-1999) global mean sea level variations. The current rate of 15-year sea level rise observed by multiple satellite altimetry is +2.3 +/- 1.2 mm/yr, which is in general agreement with the analysis of sparsely distributed tide gauge measurements for the same data span, and represents the first such determination of sea level change in its kind.

Shum, C. K.↗
NASA NTRS2019

Global Carbon Budget 2019

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E(FF)) are based on energy statistics and cement production data, while emissions from land use change (E(LUC)), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S(OCEAN)) and terrestrial CO2 sink (S(LAND)) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B(IM)), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), E(FF) was 9.5±0.5 GtC/yr, E(LUC) 1.5±0.7 GtC/yr, G(ATM) 4.9±0.02 GtC/yr (2.3±0.01 ppm/yr), S(OCEAN) 2.5±0.6 GtC/yr, and S(LAND) 3.2±0.6 GtC/yr, with a budget imbalance B(IM) of 0.4 GtC/yr indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E(FF) was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC/yr, reaching 10 GtC/yr for the first time in history, E(LUC) was 1.5±0.7 GtC/yr, for total anthropogenic CO2 emissions of 11.5±0.9 GtC/yr (42.5±3.3 GtCO2). Also for 2018, G(ATM) was 5.1±0.2 GtC/yr (2.4±0.1 ppm/yr), S(OCEAN) was 2.6±0.6 GtC/yr, and S(LAND) was 3.5±0.7 GtC/yr, with a B(IM) of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in E(FF) of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC/yr persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013).

global carbon budget 2019↗
NASA NTRS2014

Anticipated Changes in Conducting Scientific Data-Analysis Research in the Big-Data Era

A Big-Data environment is one that is capable of orchestrating quick-turnaround analyses involving large volumes of data for numerous simultaneous users. Based on our experiences with a prototype Big-Data analysis environment, we anticipate some important changes in research behaviors and processes while conducting scientific data-analysis research in the near future as such Big-Data environments become the mainstream. The first anticipated change will be the reduced effort and difficulty in most parts of the data management process. A Big-Data analysis environment is likely to house most of the data required for a particular research discipline along with appropriate analysis capabilities. This will reduce the need for researchers to download local copies of data. In turn, this also reduces the need for compute and storage procurement by individual researchers or groups, as well as associated maintenance and management afterwards. It is almost certain that Big-Data environments will require a different "programming language" to fully exploit the latent potential. In addition, the process of extending the environment to provide new analysis capabilities will likely be more involved than, say, compiling a piece of new or revised code.We thus anticipate that researchers will require support from dedicated organizations associated with the environment that are composed of professional software engineers and data scientists. A major benefit will likely be that such extensions are of higherquality and broader applicability than ad hoc changes by physical scientists. Another anticipated significant change is improved collaboration among the researchers using the same environment. Since the environment is homogeneous within itself, many barriers to collaboration are minimized or eliminated. For example, data and analysis algorithms can be seamlessly shared, reused and re-purposed. In conclusion, we will be able to achieve a new level of scientific productivity in the Big-Data analysis environments.

Kuo, Kwo-Sen↗
NASA NTRS1982

Optimal weighting of data to detect climatic change - Application to the carbon dioxide problem

It is suggested that a weighting of surface temperature data, using information about the expected level of warming in different seasons and geographical regions and statistical information about the amount of natural variability in surface temperature, can improve the chances of early detection of carbon dioxide concentration-induced climatic warming. A preliminary analysis of the optimal weighting method presented suggests that it is 25 per cent more effective in revealing surface warming than the conventional method, in virtue of the fact that 25 per cent more data must conventionally be analyzed in order to arrive at a similar probability of detection. An approximate calculation suggests that the warming ought to have already been detected, if the only sources of significant surface temperature variability had time scales of less than one year.

Bell, T. L.↗
NASA NTRS1984

Sputtering processes - Erosion and chemical change

Laboratory data and models of sputter-induced erosion and chemical alterations in ice films are reviewed and the results are applied to icy grains and satellites exposed to magnetospheric ion bombardment. It is shown that the source of the plasma in the inner magnetosphere of Saturn is likely to be the sputter erosion of the icy objects in this region and the sputter erosion and possible stabilization of the E-ring are considered. Ion-induced polymerization is discussed as a source of the darkened rings of Uranus.

Johnson, R. E.↗
NASA NTRS1986

Theory of optimal weighting of data to detect climatic change

A search for climatic change predicted by climate models can easily yield unconvincing results because of 'climatic noise,' the inherent, unpredictable variability of time-average atmospheric data. A weighted average of data that maximizes the probability of detecting predicted climatic change is presented. To obtain the optimal weights, an estimate of the covariance matrix of the data from a prior data set is needed. This introduces additional sampling error into the method. This is presently taken into account. A form of the weighted average is found whose probability distribution is independent of the true (but unknown) covariance statistics of the data and of the climate model prediction.

Bell, T. L.↗
NASA NTRS1981

The Development and Use of a Computer-Interactive Data Acquisition and Display System in a Flight Environment

The flight test data requirements at the NASA Dryden Flight Research Center increased in complexity, and more advanced instrumentation became necessary to accomplish mission goals. This paper describes the way in which an airborne computer was used to perform real-time calculations on critical flight test parameters during a flight test on a winglet-equipped KC-135A aircraft. With the computer, an airborne flight test engineer can select any sensor for airborne display in several formats, including engineering units. The computer is able to not only calculate values derived from the sensor outputs but also to interact with the data acquisition system. It can change the data cycle format and data rate, and even insert the derived values into the pulse code modulation (PCM) bit stream for recording.

Computer Systems↗
NASA NTRS2004

AveBoost2: Boosting for Noisy Data

AdaBoost is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step in AdaBoost is constructing a distribution over the training examples to create each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by the pre- vious base model in the sequence. The idea is to make the next base model's errors uncorrelated with those of the previous model. In previous work, we developed an algorithm, AveBoost, that constructed distributions orthogonal to the mistake vectors of all the previous models, and then averaged them to create the next base model s distribution. Our experiments demonstrated the superior accuracy of our approach. In this paper, we slightly revise our algorithm to allow us to obtain non-trivial theoretical results: bounds on the training error and generalization error (difference between training and test error). Our averaging process has a regularizing effect which, as expected, leads us to a worse training error bound for our algorithm than for AdaBoost but a superior generalization error bound. For this paper, we experimented with the data that we used in both as originally supplied and with added label noise-a small fraction of the data has its original label changed. Noisy data are notoriously difficult for AdaBoost to learn. Our algorithm's performance improvement over AdaBoost is even greater on the noisy data than the original data.

Oza, Nikunj C.↗
NASA NTRS2017

Kepler Planet Reliability Metrics: Astrophysical Positional Probabilities for Data Release 25

This document is very similar to KSCI-19092-003, Planet Reliability Metrics: Astrophysical Positional Probabilities, which describes the previous release of the astrophysical positional probabilities for Data Release 24. The important changes for Data Release 25 are:1. The computation of the astrophysical positional probabilities uses the Data Release 25 processed pixel data for all Kepler Objects of Interest.2. Computed probabilities now have associated uncertainties, whose computation is described in x4.1.3.3. The scene modeling described in x4.1.2 uses background stars detected via ground-based high-resolution imaging, described in x5.1, that are not in the Kepler Input Catalog or UKIRT catalog. These newly detected stars are presented in Appendix B. Otherwise the text describing the algorithms and examples is largely unchanged from KSCI-19092-003.

APP↗
NASA NTRS2011

Easing the Discovery of NASA and International Near-Real-Time Data Using the Global Change Master Directory

The Global Change Master Directory (GCMD) provides an extensive directory of descriptive and spatial information about data sets and data-related services, which are relevant to Earth science research. The directory's data discovery components include controlled keywords, free-text searches, and map/date searches. The GCMD portal for NASA's Land Atmosphere Near-real-time Capability for EOS (LANCE) data products leverages these discovery features by providing users a direct route to NASA's Near-Real-Time (NRT) collections. This portal offers direct access to collection entries by instrument name, informing users of the availability of data. After a relevant collection entry is found through the GCMD's search components, the "Get Data" URL within the entry directs the user to the desired data. http://gcmd.nasa.gov/r/p/gcmd_lance_nrt.

Olsen, Lola↗
NASA NTRSDate not supplied

On the Advantages of Using Harmonized Landsat Sentinel-2 Data for Monitoring Environmental Change

NASA coordinates the Satellite Needs Working Group, dedicated to identifying, communicating, and addressing Earth observation needs of federal agencies. In 2016, the Harmonized Landsat Sentinel-2 (HLS) dataset was formulated and implemented to fulfill multiple needs. The combination of acquisitions from the Landsat and Sentinel-2 platforms results in a global dataset of surface reflectance with a temporal resolution of two days, while retaining the geometry and 30-meter spatial resolution of Landsat data. This harmonization allows for seamless integration with the 40-year archive of Landsat data. The HLS dataset is now available on the Google Earth Engine, enabling HLS utilization in various algorithms and frameworks essential for monitoring environmental change worldwide. During this presentation, we will demonstrate and discuss the advantages of using HLS data in comparison to using separate streams of Landsat and Sentinel-2 data in existing time series-based frameworks for change monitoring. Specifically, we will explore the application of HLS for continuous monitoring of deforestation using time series-based algorithms traditionally run with Landsat data. Additionally, we will showcase the benefits of HLS data for near real-time monitoring of forest disturbance in tropical regions. These examples underscore the value and utility of the HLS dataset for environmental monitoring and analysis.

Pontus Olofsson↗
NASA NTRS1974

Systematic monitoring and evaluation of M7 scanner performance and data quality

An investigation was conducted to provide the information required to maintain data quality of the Michigan M7 Multispectral scanner by systematic checks on specific system performance characteristics. Data processing techniques which use calibration data gathered routinely every mission have been developed to assess current data quality. Significant changes from past data quality are thus identified and attempts made to discover their causes. Procedures for systematic monitoring of scanner data quality are discussed. In the solar reflective region, calculations of Noise Equivalent Change in Radiance on a permission basis are compared to theoretical tape-recorder limits to provide an estimate of overall scanner performance. M7 signal/noise characteristics are examined.

Stewart, S.↗
NASA NTRS1991

Data principles for the U.S. Global Change Research Program

The U.S. Interagency Working Group on Data Management for Global Change has developed a set of data management and access principles. The overall purpose of these statements of principle is to stimulate responsible stewardship for data and related information and to facilitate full and open access to them. These statements have been accepted by the U.S. Agencies responsible for the Global Change Research Program. The statements of principle are presented and discussed.

Ludwig, George H.↗
NASA NTRS1982

Evaluation of land cover change detection techniques using Landsat MSS data

Four techniques for detecting land cover change with Landsat MSS data were evaluated for a test site in east central Louisiana where forestland is being converted to cropland and pasture. The four techniques were: (1) post-classification differencing, (2) composite classification, (3) radiance value shift, and (4) regression modeling. Three of the techniques gave acceptable accuracies. The main differences were the amount of computer time and the level of complexity.

Burns, G. S.↗
NASA NTRS1974

Mach disk from underexpanded axisymmetric nozzle flow

The flowfield associated with the underexpanded axisymmetric nozzle freejet flow including the appearance of a Mach disk has been studied. It is shown that the location and size of the Mach disk are governed by the appearance of a triple-point shock configuration and the condition that the central core flow will reach a state of 'choking at a throat'. It is recognized that coalescence of waves requires special attention and the reflected wave, as well as the vorticity generated from these wave interactions, have to be taken accurately into account. The theoretical results obtained agreed well with the experimental data.

Chang, I.-S.↗
NASA NTRS1978

Microwave emission from polar firn

The microwave emission from a half-space medium, characterized by coordinate dependent scattering and absorbing centers, was calculated by numerically solving the radiative transfer equation by the method of invariant imbedding. Rayleigh scattering phase functions and scattering induced polarization of the radiation were included in the calculation. Using the scattering and extinction data of polar firn the brightness temperature was calculated for the 1.55 cm wavelength. This study was the first quantitative comparison of the results of numerical calculation using the actual measured information of crystal size with the observed data.

Chang, A. T. C.↗