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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Peters-Lidard, Christa D.↗
NASA NTRS1999

ASCA Observations of W44

We report the detection, using data from the Advanced Satellite for Cosmology and Astrophysics (ASCA), of a hard X-ray source in the vicinity of the radio pulsar PSR B1853+01, which is located within the supernova remnant (SNR) W44. PSR B1853+01, a 267 ms pulsar, has to date been detected only in the radio band. Previous observations at soft X-ray energies (e.g., with ROSAT HRI) have failed to detect any significant X-ray emission (pulsed or unpulsed) from the pulsar. In addition, no high-energy emission (approx. > 4 keV) has been detected previously from W44. Over the 0.5-4.0 keV band, the ASCA data show soft thermal emission from W44, with a morphology very similar to that observed earlier by Einstein and ROSAT. In the high-energy band (4.0-9.5 keV), the SNR is, for the most part, invisible, although a source coincident with the position of PSR B1853+01 is evident. The observed ASCA spectra are consistent with a power-law origin (photon index approx. 2.3) for the X-ray emission from this source at a flux level (flux density approx. 0.5 micro Jy at I keV) consistent with previous upper limits. The maximum allowed size for the source is determined directiv from the ASCA data (<5 min.), while the minimum size is derived from the nondetection of a point source in the ROSAT HRI data (approx. > 30 sec.). Timing analysis of the hard X-ray source failed to detect pulsations at the pulsar's period. Based on these lines of evidence, we conclude that the new hard source in W44 represents an X-ray synchrotron nebula associated with PSR B1853+01, rather than the beamed output of the pulsar itself. This discoverv adds W44 to the small group of previously known plerionic SNRs This nebula lies at the low end of, but is consistent with, the correlation between X-ray luminosity and pulsar spin-down energy loss found for such objects, lending further support to our interpretation.

Hughes, John P.↗
NASA NTRS2009

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov; Kumar et al., 2006; Peters- Lidard et al.,2007) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected ase co-winner of NASA's 2005 Software of the Year award. LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has evolved from two earlier efforts North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) and Global Land Data Assimilation System (GLDAS; Rodell al. 2004) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations. In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins,". As described in Kumar et al., 2007, and demonstrated in Case et al., 2008, and Santanello et al., 2009, LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling the enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation as described in Peters-Lidard et al. (2008) and Santanello et al. (2007), who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs. LIS has also recently been demonstrated for multi-model data assimilation (Kumar et al., 2008) using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature. Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation. Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeoroogical modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems.

Peters-Lidard, Christa D.↗
NASA NTRS1999

An Integrated Architecture for Onboard Spacecraft

As increasingly complex scientific and environmental observation spacecraft are deployed, the burden on the downlink assets, and ground-based systems complexity and cost is becoming a major problem. Already, the limitations of communications bandwidth and processing throughput limit the science data gathering, both in volume and in rate. This poses a dilemma to the scientist experimenter forcing choices between data collection and bandwidth/processing/archiving. Advances in ground based processing and space-to-Earth links have fallen behind the requirements for observation data, at increasing rates, over the last few decades. As NASA achieves its 40th anniversary, the ability to observe and capture phenomena of theoretical and practical interest to life on Earth far outstrips the ability to transfer, process, or store these data. NASA recognizes the need to invest on technological advancements that will enable both the space and ground systems to address the limitations. Spacecraft onboard computing power is a clear one. The capability of creating data products onboard the spacecraft adds a new level of flexibility to address the more demanding observation needs. Current spacecraft computing power is limited and incapable of addressing the needs of the new generation of observation satellites because extensive onboard data processing is required. Traditional spacecraft architectures only collect, package, and transmit to Earth the data acquired by multiple instruments. Conversely, the experience on developing ground data systems shows the need for high performance computing systems to process and create information from the instrumentation data. The expectation is that supercomputing technology is required to enable spacecraft to create information onboard. Moving supercomputing capability onboard spacecraft requires an approach that considers an integrated data architecture. Otherwise, it may simply convert a compute-bound problem into a communications bound problem, as has been shown numerous times in the context of massively parallel architectures. What is left to determine are the technologies that will enable spacecraft high performance computing.

Figueiredo, Marco A.↗
DOE OSTI2023

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). Furthermore, the results demonstrate the overall advantage of GMT in translation quality and generalization ability.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Aerosol Remote Sensing from Space -- What We've Learned, Where We're Heading

The MISR and MODIS instruments aboard the NASA Earth Observing System's Terra Satellite have been collecting data containing information about the state of Earth's atmosphere and surface for over ten years. Among the retrieved quantities are amount and type of wildfire smoke, desert dust, volcanic effluent, urban and industrial pollution particles, and other aerosols. However, the broad scientific challenges of understanding aerosol impacts on climate and health place different, and very exacting demands on our measurement capabilities. And these data sets, though much more advanced in many respects than previous aerosol data records, are imperfect. In this presentation, I will summarize current understanding of MISR and MODIS aerosol product strengths and limitations, discuss how they relate to the bigger aerosol science questions we must address, and give my view of what we will need to do to progress.

Kahn, Ralph↗
NASA NTRS2011

Aerosol Remote Sensing from Space - Where We Stand, Where We're Heading

The MISR and MODIS instruments aboard the NASA Earth Observing System's Terra Satellite have been collecting data containing information about the state of Earth's atmosphere and surface for over eleven years. Among the retrieved quantities are amount and type of wildfire smoke, desert dust, volcanic effluent, urban and industrial pollution particles, and other aerosols. However, the broad scientific challenges of understanding aerosol impacts on climate and health place different, and very exacting demands on our measurement capabilities. And these data sets, though much more advanced in many respects than previous aerosol data records, are imperfect. In this presentation, I will summarize current understanding of MISR and MODIS aerosol product strengths and limitations, discuss how they relate to the bigger aerosol science questions we must address, and give my view of the way forward.

Kahn, Ralph↗
NASA NTRS2012

Aerosol Remote Sensing from Space - Where We Stand, Where We're Heading

The MISR and MODIS instruments aboard the NASA Earth Observing System's Terra Satellite have been collecting data containing information about the state of Earth's atmosphere and surface for over twelve years. Among the retrieved quantities are the amount and type of wildfire smoke, desert dust, volcanic effluent, urban and industrial pollution particles, and other aerosols. Data from these instruments have been used to develop a global, monthly climatology of aerosol amount that is widely used as a constraint on climate models, including those used for the 2007 IPCC assessment report. However, the broad scientific challenges of understanding aerosol impacts on climate and health place different, and very exacting demands on our measurement capabilities. And these data sets, though much more advanced in many respects than previous aerosol data records, are imperfect. The next frontier in assessing aerosol radiative forcing of climate is aerosol type, and in particular, the absorption properties of major aerosol air masses. In this presentation, I will summarize current understanding of MISR and MODIS aerosol product strengths and limitations, discuss how they relate to the bigger aerosol science questions we must address, and give my view of the way forward.

Kahn, Ralph↗
NASA NTRS2013

Aerosol Remote Sensing from Space - Where We Stand, Where We're Heading

The MISR and MODIS instruments aboard the NASA Earth Observing System's Terra Satellite have been collecting data containing information about the state of Earth's atmosphere and surface for over eleven years. Among the retrieved quantities are amount and type of wildfire smoke, desert dust, volcanic effluent, urban and industrial pollution particles, and other aerosols. However, the broad scientific challenges of understanding aerosol impacts on climate and health place different, and very exacting demands on our measurement capabilities. And these data sets, though much more advanced in many respects than previous aerosol data records, are imperfect. In this presentation, I will summarize current understanding of MISR and MODIS aerosol product strengths and limitations, discuss how they relate to the bigger aerosol science questions we must address, and give my view of the way forward.

MISR↗
DOE OSTI2020

Strong QCD from Hadron Structure Experiments

The topical workshop Strong QCD from Hadron Structure Experiments took place at Jefferson Lab from November 6–9, 2019. Impressive progress in relating hadron structure observables to the strong QCD mechanisms has been achieved from the ab initio QCD description of hadron structure in a diverse array of methods in order to expose emergent phenomena via quasi-particle formation. The wealth of experimental data and the advances in hadron structure theory make it possible to gain insight into strong interaction dynamics in the regime of large quark–gluon coupling (the strong QCD regime), which will address the most challenging problems of the Standard Model on the nature of the dominant part of hadron mass, quark–gluon confinement, and the emergence of the ground and excited state hadrons, as well as atomic nuclei, from QCD. This workshop aimed to develop plans and to facilitate the future synergistic efforts between experimentalists, phenomenologists, and theorists working on studies of hadron spectroscopy and structure with the goal to connect the properties of hadrons and atomic nuclei available from data to the strong QCD dynamics underlying their emergence from QCD. These results pave the way for a future breakthrough extension in the studies of QCD with an Electron–Ion Collider in the U.S.

Brodsky, S. J.↗
NASA NTRS1983

Evaluation of procedures to correct for variable viewing and illumination geometry when observing a non-Lambertian surface through the atmosphere

Data from the Advanced Very High Resolution Radiometer aboard the NOAA polar orbiting satellite are being operationally applied to provide estimates of vegetation cover and/or condition over a large part of the earth by the USDA. The wide scan angle (+ or - 54 deg) of this system permits daily views of the earth when used to its limits. Five-day repetitive coverage is acquired, assuming cloud-free conditions, in current operations which limit the use of the scan to the center + or - 14 deg of swath. While use of the full scan width would provide clear acquisitions frequent enough to monitor crop development and condition even with normal cloudiness, these off-nadir data are made difficult to interpret due to the non-Lambertian nature of the surface, enhanced effect of the atmosphere, inclusion of subpixel and thin invisible clouds in the scene, and differences in illumination across the scene; all of which contribute to variations in observed reflected radiation. Some approaches to provide corrections for these effects are discussed here.

Whitehead, V. S.↗
NASA NTRS2015

Parameter Estimation for Compact Binaries with Ground-Based Gravitational-Wave Observations Using the LALInference

The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star (BNS), a neutron star - black hole binary (NSBH) and a binary black hole (BBH), where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence (CBC) parameter space.

gravitational wave↗
NASA NTRS2007

Assimilation of GRACE Terrestrial Water Storage Data into a Land Surface Model: Results for the Mississippi River Basin

NASA's GRACE mission has the potential to be extremely valuable for water resources applications and global water cycle research. What makes GRACE unique among Earth Science satellite systems is that it is able to monitor variations in water stored in all forms, from snow and surface water to soil moisture to groundwater in the deepest aquifers. However, the space and time resolutions of GRACE observations are coarse. GRACE typically resolves water storage changes over regions the size of Nebraska on a monthly basis, while city-scale, daily observations would be more useful for water management, agriculture, and weather prediction. High resolution numerical (computer) hydrology models have been developed, which predict the fates of water and energy after they strike the land surface as precipitation and sunlight. These are similar to weather and climate forecast models, which simulate atmospheric processes. We integrated the GRACE observations into a hydrology model using an advanced technique called data assimilation. The results were new estimates of groundwater, soil moisture, and snow variations, which combined the veracity of GRACE with the high resolution of the model. We tested the technique over the Mississippi River basin, but it will be even more valuable in parts of the world which lack reliable data on water availability.

Zaitchik, Benjamin F.↗
DOE OSTI2022

TSL Nuclear Fuel Evaluations and Capabilities at NC State University [Slides]

This presentation discusses the purpose of this project which is to provide thermal scattering law (TSL) and cross section data to support advanced reactor modeling and criticality safety. The presentation also examines modeling and simulations, evaluation updates, and benchmark applications in connection with the project. In summation, new and updated uranium fuel evaluations have been submitted to the ENDF/B libraries. These include vital fuel materials U-metal, UC, UN, and UO 2 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1993

Remote sensing of multilevel clouds during FIRE IFO 2

An unresolved difficulty in the remote sensing of clouds concerns the inability of the cloud retrieval algorithms to adequately recognize and analyze scenes containing overlapping cloud layers. Most cloud retrieval schemes, such as that used by the International Satellite Cloud Climatology Project (ISCCP) assume that each picture element (pixel) contains a single cloud layer. The current study begins to address the complexities of multilayered cloud property retrieval through the application of a modified multispectral, multiresolution (MSMR) method, first detailed in Baum et al. (1992), which merges 1.1 -km (at nadir) spectral data from the Advanced Very High Resolution Radiometer (AVHRR) with 17.4-km (at nadir) High Resolution Infrared Radiometer Sounder (HIRS/2, henceforth HIRS). Both instruments are flown aboard the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting platforms. An ideal case study for this investigation is provided by the NOAA-11 overpass at 20:48 UTC on November 28, 1991. At this time, a large-scale cirrostratus veil overlaid a low-level stratus deck over much of the IFO region. There were both surface lidar and radar observations of the clouds as well as University of North Dakota (UND) Citation aircraft measurements. The presence of overlapping cloud layers within a HIRS FOV is determined from colocated AVHRR spectral data through the use of a fuzzy logic expert system. Conventional algorithms such as spatial coherence and CO2 slicing are used to retrieve cloud pressure and height for each identified cloud layer. The results from the satellite cloud retrieval analysis are compared to results from both surface- and aircraft-based measurements.

Baum, Bryan A.↗
NASA NTRS2009

Uncertainty Management in Remote Sensing of Climate Data. Summary of A Workshop

Great advances have been made in our understanding of the climate system over the past few decades, and remotely sensed data have played a key role in supporting many of these advances. Improvements in satellites and in computational and data-handling techniques have yielded high quality, readily accessible data. However, rapid increases in data volume have also led to large and complex datasets that pose significant challenges in data analysis (NRC, 2007). Uncertainty characterization is needed for every satellite mission and scientists continue to be challenged by the need to reduce the uncertainty in remotely sensed climate records and projections. The approaches currently used to quantify the uncertainty in remotely sensed data, including statistical methods used to calibrate and validate satellite instruments, lack an overall mathematically based framework.

Remote sensing↗
NASA NTRS1992

Atmosphere and climate studies of Mars using the Mars Observer pressure modulator infrared radiometer

Studies of the climate and atmosphere of Mars are limited at present by a lack of meteorological data having systematic global coverage with good horizontal and vertical resolution. The Mars Observer spacecraft in a low, nearly circular, polar orbit will provide an excellent platform for acquiring the data needed to advance significantly our understanding of the Martian atmosphere and its remarkable variability. The Mars Observer pressure modulator infrared radiometer (PMIRR) is a nine-channel limb and nadir scanning atmospheric sounder which will observe the atmosphere of Mars globally from 0 to 80 km for a full Martian year. PMIRR employs narrow-band radiometric channels and two pressure modulation cells to measure atmospheric and surface emission in the thermal infrared. PMIRR infrared and visible measurements will be combined to determine the radiative balance of the polar regions, where a sizeable fraction of the global atmospheric mass annually condenses onto and sublimes from the surface. Derived meteorological fields, including diabatic heating and cooling and the vertical variation of horizontal winds, are computed from the globally mapped fields retrieved from PMIRR data.

Mccleese, D. J.↗
NASA NTRSDate not supplied

Life After Launch: A Snapshot of the First 6 Months of NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission launched from Kennedy Space Center in the early morning of February 8, 2024. Just 63 days later, data from NASA’s newest Earth-observing satellite became available to the public. These data will extend and improve upon NASA’s 20+ years of global satellite observation of our living oceans, atmospheric aerosols, and cloud and initiate an advanced set of climate-relevant data records. Ultimately, PACE is the first mission to provide daily, global measurements that will enable prediction of the “boom-bust” cycle of fisheries, the appearance of harmful algae, and other factors that affect commercial and recreational industries. PACE also observes clouds and tiny airborne particles known as aerosols that influence air quality and absorb and reflect sunlight, thus warming and cooling the atmosphere. In the months since launch and initial data release, the PACE Project pursued instrument temporal and system vicarious calibrations, executed cross-instrument comparisons, conducted performance assessments, explored synergies with other missions, and released advanced science data products. In parallel, the PACE Validation Science Team left for the field and the Post-launch Airborne eXperiment (PACE-PAX) prepared for its mission. And, most importantly, preliminary science results were realized. Here, we present a snapshot of these activities and their impacts and outcomes, encompassing the first half year of the PACE mission.

PACE↗