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At least 1,225 records · Page 68
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NASA NTRS1982

Interannual atmospheric angular momentum variability 1963-1973 and the Southern Oscillation

The Oort (1981) data set is used to determine the total angular momentum variability of the atmosphere below 75 mbar over the 120-month period from May 1963 to April 1973. These results are in turn compared with the observed length of day variations of the same period. When the mean, annual and semi-annual cycles are removed, the angular momentum displays an irregular oscillation with periods of three to four years and a corresponding relative change in the length of day of about + or - 0.2 x 10 to the -8th. This oscillation, which agrees with the observed interannual length of day variations for the period, appears to be related to the oscillation in tropical air and sea surface temperatures which lags behind the temperature oscillation by a few months. A relative change in the rotation of the earth on the order of 0.2 x 10 to the -8th can be produced by an observed tropical temperature oscillation of 1.0 C, according to a thermal wind argument.

Stefanick, M.↗
DOE OSTI2024

Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

Crespo, Luis G.↗
NASA NTRS2015

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Crespo, Luis G.↗
NASA NTRS1986

Interannual variability study of the earth radiation budget from Nimbus 7 monthly data

The broadband data set on the earth radiation budget recorded by Nimbus-7 instrumentation from 1978-84 is summarized, along with the results of several statistical evaluations. The emitted longwave radiation (W/sq m), albedo and net radiation (W/sq m) are tabulated in terms of yearly means and summer and winter departures from those means. Global maps are also provided for 6-yr averaged values of the same parameters. The largest variabilities were observed over the equatorial Pacific Ocean, the Indian Ocean, and Indonesia, indicating the extent of annual variations of the size of the Intertropical Convergence Zone.

Smith, L. D.↗
NASA NTRS2022

Investigating Stable Oxygen and Carbon Isotopic Variability in Speleothem Records Over the Last Millennium Using Multiple Isotope-Enabled Climate Models

The incorporation of water isotopologues into the hydrology of general circulation models (GCMs) facilitates the comparison between modeled and measured proxy data in paleoclimate archives. However, the variability and drivers of measured and modeled water isotopologues, as well as the diversity of their representation in different models, are not well constrained. Improving our understanding of this variability in past and present climates will help to better constrain future climate change projections and decrease their range of uncertainty. Speleothems are a precisely datable terrestrial paleoclimate archives and provide well-preserved (semi-)continuous multivariate isotope time series in the lower latitudes and mid-latitudes and are therefore well suited to assess climate and isotope variability on decadal and longer timescales. However, the relationships of speleothem oxygen and carbon isotopes to climate variables are influenced by site-specific parameters, and their comparison to GCMs is not always straightforward. Here we compare speleothem oxygen and carbon isotopic signatures from the Speleothem Isotopes Synthesis and Analysis database version 2 (SISALv2) to the output of five different water-isotope-enabled GCMs (ECHAM5-wiso, GISS-E2-R, iCESM, iHadCM3, and isoGSM) over the last millennium (850–1850 CE). We systematically evaluate differences and commonalities between the standardized model simulation outputs. The goal is to distinguish climatic drivers of variability for modeled isotopes and compare them to those of measured isotopes. We find strong regional differences in the oxygen isotope signatures between models that can partly be attributed to differences in modeled surface temperature. At low latitudes, precipitation amount is the dominant driver for stable water isotope variability; however, at cave locations the agreement between modeled temperature variability is higher than for precipitation variability. While modeled isotopic signatures at cave locations exhibited extreme events coinciding with changes in volcanic and solar forcing, such fingerprints are not apparent in the speleothem isotopes. This may be attributed to the lower temporal resolution of speleothem records compared to the events that are to be detected. Using spectral analysis, we can show that all models underestimate decadal and longer variability compared to speleothems (albeit to varying extents). We found that no model excels in all analyzed comparisons, although some perform better than the others in either mean or variability. Therefore, we advise a multi-model approach whenever comparing proxy data to modeled data. Considering karst and cave internal processes, e.g., through isotope-enabled karst models, may alter the variability in speleothem isotopes and play an important role in determining the most appropriate model. By exploring new ways of analyzing the relationship between the oxygen and carbon isotopes, their variability, and co-variability across timescales, we provide methods that may serve as a baseline for future studies with different models using, e.g., different isotopes, different climate archives, or different time periods.

isotope-enabled simulations↗
NASA NTRS1990

Variations of global mesoscale eddy energy observed from Geosat

Data on oceanic-current variability were extracted from Geosat altimeter observations for 44 17-day repeat cycles, using the Sandwell and Zhang (1989) technique to process the altimeter data and to produce a sea-surface-slope profile having an estimated accuracy of 0.2 microrad. These were used to generate a series of global eddy kinetic energy maps, each averaged over 3 months, together with their mean. It was found that the maximum mean eddy kinetic energy per unit mass exceeds 2000 sq cm/sq sec for most of the western boundary currents; for the Antarctic Circumpolar Current, however, this value reaches only 500 sq cm/sq sec.

Shum, C. K.↗
NASA NTRS1993

Compression through decomposition into browse and residual images

Economical archival and retrieval of image data is becoming increasingly important considering the unprecedented data volumes expected from the Earth Observing System (EOS) instruments. For cost effective browsing the image data (possibly from remote site), and retrieving the original image data from the data archive, we suggest an integrated image browse and data archive system employing incremental transmission. We produce our browse image data with the JPEG/DCT lossy compression approach. Image residual data is then obtained by taking the pixel by pixel differences between the original data and the browse image data. We then code the residual data with a form of variable length coding called diagonal coding. In our experiments, the JPEG/DCT is used at different quality factors (Q) to generate the browse and residual data. The algorithm has been tested on band 4 of two Thematic mapper (TM) data sets. The best overall compression ratios (of about 1.7) were obtained when a quality factor of Q=50 was used to produce browse data at a compression ratio of 10 to 11. At this quality factor the browse image data has virtually no visible distortions for the images tested.

Novik, Dmitry A.↗
NASA NTRS1992

Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

Michaels, Anthony F.↗
NASA NTRS1990

Validation of the geostrophic method for estimating zonal currents at the equator from Geosat altimeter data

The applicability of satellite altimeter data for estimating zonal current variability at the equator is assessed using the meriodionally differenced form of the geostrophic balance. Estimates of geostrophic zonal flow anomalies in the equatorial Pacific have been deduced from 17-day collinear altimeter data during the first year of the Geosat Exact Repeat Mission. Altimeter-derived geostrophic estimates agree well with in situ zonal current variability. Comparison of flow-frequency near-surface zonal current observed from equatorial moorings at 165 deg E, 140 deg W, and 110 deg W yield correlations of 0.83, 0.85, and 0.51, respectively, with a mean rms difference of 23 cm/sec. The inclusion of up to 11 ascending and descending Geosat tracks within the 9-deg band for every 17-day repeat effectively reduced the temporal sampling interval to 1.5 days at 165 deg E and 140 deg W. The 6.8-km along track spacing of the altimeter measurements provides sufficient resolution for the effective filtering of small-scale meridional noise, both instrumental and oceanic.

Picaut, Joel↗
NASA NTRS1993

Eddy and wave dynamics in the South Atlantic as diagnosed from Geosat altimeter data

The characteristics of the sea surface height variability in the South Atlantic are investigated on the basis of almost 3 yr of Geosat data. The nature of the sea surface height field is shown to be nonstationary, heterogeneous, and anisotropic. The wavenumber spectral characteristics of the sea surface height field, such as spectral slopes, shape, breaks in spectral slopes, and energy density values, vary with latitude and longitude within the basin. The spectral slopes exhibit a spectral dependence approaching k exp -5 in the high-energy areas and k exp -3 in the low-energy areas. Along-track characteristic wavelengths are found to decrease from north to south and from west to east. The energy exhibits maximum values in the highly energetic eddy-shedding regions of the Malvinas-Brazil confluence and the Agulhas leakage region.

Forbes, Cristina↗
NASA NTRS2010

On the Long-Term Variability of Jupiter's Winds and Brightness as Observed from Hubble

Hubble Space Telescope Wide Field Planetary Camera 2 imaging data of Jupiter were combined with wind profiles from Voyager and Cassini data to study long-term variability in Jupiter's winds and cloud brightness. Searches for evidence of wind velocity periodicity yielded a few latitudes with potential variability; the most significant periods were found nearly symmetrically about the equator at 0 deg., 10-12 deg. N, and 14-18 deg. S planetographic latitude. The low to mid-latitude signals have components consistent with the measured stratospheric temperature Quasi-Quadrennial Oscillation (QQO) period of-5 years, while the equatorial signal is approximately seasonal and could be tied to mesoscale wave formation, robustness tests indicate that a constant or continuously varying periodic signal near 4.5 years would appear with high significance in the data periodograms as long as uncertainties or noise in the data are not of greater magnitude. However, the lack of a consistent signal over many latitudes makes it difficult to interpret as a QQO-related change. In addition, further analyses of calibrated 410-nm and 953-nm brightness scans found few corresponding changes in troposphere haze and cloud structure on QQO timescales. However, stratospheric haze reflectance at 255-nm did appear to vary on seasonal timescales, though the data do not have enough temporal coverage or photometric accuracy to be conclusive. Sufficient temporal coverage and spacing, as well as data quality, are critical to this type of search.

Simon-Miller, Amy A.↗
NASA NTRS2012

Variability and Trends of Aerosol Properties over Kanpur, Northern India using AERONET Data (2001-10)

Natural and anthropogenic aerosols over northern India play an important role in influencing the regional radiation budget, causing climate implications to the overall hydrological cycle of South Asia. In the context of regional climate change and air quality, we discuss aerosol loading variability and trends at Kanpur AERONET station located in the central part of the Indo-Gangetic plains (IGP), during the last decade (2001-10). Ground-based radiometric measurements show an overall increase in column-integrated aerosol optical depth (AOD) on a yearly basis. This upward trend is mainly due to a sustained increase in the seasonal/monthly averaged AOD during the winter (Dec-Feb) and post-monsoon (Oct-Nov) seasons (dominated by anthropogenic emissions). In contrast, a neutral to weak declining trend is observed during late pre-monsoon (Mar-May) and monsoon (Jun-Sep) months, mainly influenced by inter-annual variations of dust outbreaks. A general decrease in coarse-mode aerosols associated with variable dust activity is observed, whereas the statistically significant increasing post-monsoon/winter AOD is reflected in a shift of the columnar size distribution towards relatively larger particles in the accumulation mode. Overall, the present study provides an insight into the pronounced seasonal behavior in aerosol loading trends and, in general, is in agreement with that associating the findings with those recently reported by satellite observations (MODIS and MISR) over northern India. Our results further suggest that anthropogenic emissions (due mainly to fossil-fuel and biomass combustion) over the IGP have continued to increase in the last decade.

AERONET↗
NASA NTRS2012

Virtual Machine Language 2.1

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that signal is raised. The selected signal then causes all identically named transitions in all present state machines to be taken simultaneously. VML 2.1 has relevance to all potential space missions, both manned and unmanned. It was under consideration for use on Orion.

Riedel, Joseph E.↗
DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗
NASA NTRS1985

Reducing Landsat MSS scene variability

Landsat 1, 2, and 3 MSS data acquired for six different nonvegetated targets over a three-year period were used to determine which of five transformations was most useful for reducing between-scene variability. The following values were calculated from the MSS digital numbers (dn): (1) radiance; (2) reflectance; (3) reflectance corrected for changes in the earth/sun distance; (4) normalized dn (normalization equations proposed by ERIM researchers); and (5) band ratios. Results indicated that reflectance calculations were most effective overall for reducing interscene variability; ratio proved most useful on the bright targets.

Nelson, R.↗
NASA NTRSDate not supplied

Governing Data Findability, Accessibility, Interoperability and Reusability (FAIR) Compliance

The most recent data strategy documents at both the federal and NASA levels stipulate that systems should strive for the data they manage to be Findable, Accessible, Interoperable, and Reusable (FAIR). The NASA Life Sciences Portal (NLSP) has already begun leading efforts in this area for HRP, initiating efforts to comply with the FAIR principles. The broad interpretation of the FAIR principles has led to a plethora of tools that use a splay of metrics specifically but variably developed to judge how compliant data and systems are with the principles. A recent review [3] identified and studied 1,180 metrics across 20 publicly available tools for checking FAIR compliance of data and systems. Because of their very recent development, many organizations and data systems managers and developers have not yet had adequate time or resources to understand these FAIR compliance tools and metrics, their variations in design, accuracy or ease of application to their specific data sets and systems. Thus, it would be best for larger organizations like NASA to approach formulating a strategy for governance of FAIR compliance that can be flexibly applied and is adaptable to an evolving awareness knowledge of FAIR compliance methods and tools. In September 2024, the NASA Science Mission Directorate(SMD) organized a workshop on NASA science data repositories, including the topics of implementing FAIR and governing FAIR compliance across SMD. The initial part of these FAIR discussions focused on developing consensus around required science metadata fields. This is challenging given the diverse nature of NASA’s scientific data portfolio, the variety of metadata models and vocabularies used, and variable level of resources available to curate these data. Later discussion focused on three possible approaches to governing FAIR compliance: distributed, in which various programs, projects or systems define their own methods for assessing FAIR compliance, reporting results up appropriate management lines; centralized, in which higher-level organization(s) specify compliance tools or methods for the various data systems; and multi-level, in which a group comprised of individuals with expertise from multiple levels with organizations is formed to provide guidance and/or specifications for governing FAIR compliance. We report on the recommendations this session yielded, and how these might be shaped specifically to help implement and govern the compliance with FAIR of Human Research Program data and systems.

governance↗
NASA NTRS1997

The Climate Signal in Regional Moisture Fluxes: A Comparison of Three Global Data Assimilation Products

This study assesses the quality of estimates of climate variability in moisture flux and convergence from three assimilated data sets: two are reanalysis products generated at the Goddard Data Assimilation Office (DAO) and the National Centers for Environmental Prediction/National Centers for Atmospheric Research (NCEPJNCAR), and the third consists of the operational analyses generated at the European Center for Medium Range Forecasts (ECMWF). The regions under study (the United States Great Plains, the Indian monsoon region, and Argentina east of the Andes) are characterized by frequent low level jets (LLJs) and other interannual low level wind variations tied to the large-scale flow. While the emphasis is on the reanalysis products, the comparison with the operational product is provided to help assess the improvements gained from a fixed analysis system. All three analyses capture the main moisture flux anomalies associated with selected extreme climate (drought and flood) events during the period 1985-93. The correspondence is strongest over the Great Plains and weakest over the Indian monsoon region reflecting differences in the observational coverage. For the reanalysis products, the uncertainties in the lower tropospheric winds is by far the dominant source of the discrepancies in the moisture flux anomalies in the middle latitude regions. Only in the Indian Monsoon region, where interannual variability in the low level winds is comparatively small, does the moisture bias play a substantial role. In contrast, the comparisons with the operational product show differences in moisture which are comparable torhe differences in the wind in all three regions. Compared with the fluxes, the anomalous moisture convergences show substantially larger differences among the three products. The best agreement occurs over the Great Plains region where all three products show vertically-integrated moisture convergence during the floods and divergence during the drought with differences in magnitude of about 25%. The reanalysis products, in particular, show good agreement in depicting the different roles of the mean flow and transients during the flood and drought periods. Differences between the three products in the other two regions exceed 100% reflecting differences in the low level jets and the large scale circulation patterns. The operational product tends to have locally larger amplitude convergence fields which average out in area-mean budgets: this appears to be at least in part due to errors in the surface pressure fields and aliasing from the higher resolution of the original ECMWF fields. On average, the reanalysis products show higher coherence with each other than with the operational product in the estimates of interannual variability. This result is less clear in the Indian monsoon region where differences in the input observations appears to be an important factor. The agreement in the anomalous convergence patterns is, however, still rather poor even over relatively data dense regions such as the United States Great Plains. These differences are attributed to deficiencies in the assimilating GCM's representations of the planetary boundary layer and orography, and a global observing system incapable of resolving the highly confined low level winds associated with the climate anomalies.

Min, Wei↗