Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Sparse sensing”

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

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

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Data Assimilation and Regional Forecasts using Atmospheric InfraRed Sounder (AIRS) Profiles

In data sparse regions, remotely-sensed observations can be used to improve analyses, which in turn should lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which, together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with an accuracy comparable to that of radionsondes. The purpose of this poster is to describe a procedure to optimally assimilate AIRS thermodynamic profiles, obtained from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm, into a regional configuration of the Weather Research and Forecasting (WRF) model using WRF-Var. The poster focuses on development of background error covariances for the regional domain and background field type, a methodology for ingesting AIRS profiles as separate over-land and over-water retrievals with different error characteristics, and utilization of level-by-level quality indicators to select only the highest quality data. The assessment of the impact of the AIRS profiles on WRF-Var analyses will focus on intelligent use of the quality indicators, optimized tuning of the WRF-Var, and comparison of analysis soundings to radiosondes. The analyses are used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impact of AIRS profiles on forecast will be assessed against NAM analyses and stage IV precipitation data.

Zabodsky, Brad↗
NASA NTRS2009

Improving Regional Forecast by Assimilating Atmospheric InfraRed Sounder (AIRS) Profiles into WRF Model

In data sparse regions, remotely-sensed observations can be used to improve analyses and produce improved forecasts. One such source comes from the Atmospheric InfraRed Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), represents one of the most advanced space-based atmospheric sounding systems. The purpose of this paper is to describe a procedure to optimally assimilate high resolution AIRS profile data into a regional configuration of the Advanced Research WRF (ARW) version 2.2 using WRF-Var. The paper focuses on development of background error covariances for the regional domain and background type, and an optimal methodology for ingesting AIRS temperature and moisture profiles as separate overland and overwater retrievals with different error characteristics. The AIRS thermodynamic profiles are derived from the version 5.0 Earth Observing System (EOS) science team retrieval algorithm and contain information about the quality of each temperature layer. The quality indicators were used to select the highest quality temperature and moisture data for each profile location and pressure level. The analyses were then used to conduct a month-long series of regional forecasts over the continental U.S. The long-term impacts of AIRS profiles on forecast were assessed against verifying NAM analyses and stage IV precipitation data.

Chou, Shih-Hung↗
NASA NTRS2010

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Precipitation Forecasting

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles in clear and cloudy regions with accuracy which approaches that of radiosondes. The purpose of this paper is to describe an approach to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research WRF (ARW) model using WRF-Var. Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in clear and partly cloudy regions, and uncontaminated portions of retrievals above clouds in overcast regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts resulting from improved thermodynamic fields. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Chou, S.-H.↗
NASA NTRS2010

Impact of Atmospheric Infrared Sounder (AIRS) Thermodynamic Profiles on Regional Weather Forecasting

In data sparse regions, remotely-sensed observations can be used to improve analyses and lead to better forecasts. One such source comes from the Atmospheric Infrared Sounder (AIRS), which together with the Advanced Microwave Sounding Unit (AMSU), provides temperature and moisture profiles with accuracy comparable to that of radiosondes. The purpose of this paper is to describe a procedure to assimilate AIRS thermodynamic profile data into a regional configuration of the Advanced Research Weather Research and Forecasting (WRF-ARW) model using its three-dimension variational (3DVAR) analysis component (WRF-Var). Quality indicators are used to select only the highest quality temperature and moisture profiles for assimilation in both clear and partly cloudy regions. Separate error characteristics for land and water profiles are also used in the assimilation process. Assimilation results indicate that AIRS profiles produce an analysis closer to in situ observations than the background field. Forecasts from a 37-day case study period in the winter of 2007 show that AIRS profile data can lead to improvements in 6-h cumulative precipitation forecasts due to instability added in the forecast soundings by the AIRS profiles. Additionally, in a convective heavy rainfall event from February 2007, assimilation of AIRS profiles produces a more unstable boundary layer resulting in enhanced updrafts in the model. These updrafts produce a squall line and precipitation totals that more closely reflect ground-based observations than a no AIRS control forecast. The location of available high-quality AIRS profiles ahead of approaching storm systems is found to be of paramount importance to the amount of impact the observations will have on the resulting forecasts.

Chou, Shih-Hung↗
DOE OSTI2020

Dynamic compressed sensing for real-time tomographic reconstruction

Electron tomography has achieved higher resolution and quality at reduced doses with recent advances in compressed sensing. Compressed sensing (CS) exploits the inherent sparse signal structure to efficiently reconstruct three-dimensional (3D) volumes at the nanoscale from undersampled measurements. However, the process bottlenecks 3D reconstruction with computation times that run from hours to days. Here we demonstrate a framework for dynamic compressed sensing that produces a 3D specimen structure that updates in real-time as new specimen projections are collected. Researchers can begin interpreting 3D specimens as data is collected to facilitate high-throughput and interactive analysis. Using scanning transmission electron microscopy (STEM), we show that dynamic compressed sensing accelerates the convergence speed by ~3-fold while also reducing its error by 27% for a Au/SrTiO 3 nanoparticle specimen. Before a tomography experiment is completed, the 3D tomogram has interpretable structure within ~33% of completion and fine details are visible as early as ~66%. Upon completion of an experiment, a high-fidelity 3D visualization is produced without further delay. Additionally, we report reconstruction parameters that tune data fidelity can be manipulated throughout the computation without re-running the entire process.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Optical Characterisation of Suspended Particles in the Mackenzie River Plume (Canadian Arctic Ocean) and Implications for Ocean Colour Remote Sensing

Climate change significantly impacts Arctic shelf regions in terms of air temperature, ultraviolet radiation, melting of sea ice, precipitation, thawing of permafrost and coastal erosion. Direct consequences have been observed on the increasing Arctic river flow and a large amount of organic carbon sequestered in soils at high latitudes since the last glacial maximum can be expected to be delivered to the Arctic Ocean during the coming decade. Monitoring the fluxes and fate of this terrigenous organic carbon is problematic in such sparsely populated regions unless remote sensing techniques can be developed and proved to be operational. The main objective of this study is to develop an ocean colour algorithm to operationally monitor dynamics of suspended particulate matter (SPM) on the Mackenzie River continental shelf (Canadian Arctic Ocean) using satellite imagery. The water optical properties are documented across the study area and related to concentrations of SPM and particulate organic carbon (POC). Robust SPM and POC : SPM proxies are identified, such as the light backscattering and attenuation coefficients, and relationships are established between these optical and biogeochemical parameters. Following a semi-analytical approach, a regional SPM quantification relationship is obtained for the inversion of the water reflectance signal into SPM concentration. This relationship is reproduced based on independent field optical measurements. It is successfully applied to a selection of MODIS satellite data which allow estimating fluxes at the river mouth and monitoring the extension and dynamics of the Mackenzie River surface plume in 2009, 2010 and 2011. Good agreement is obtained with field observations representative of the whole water column in the river delta zone where terrigenous SPM is mainly constrained (out of short periods of maximum river outflow). Most of the seaward export of SPM is observed to occur within the west side of the river mouth. Future work will require the validation of the developed SPM regional algorithm based on match-ups with field measurements, then the routine application to ocean colour satellite data in order to better estimate the fluxes and fate of SPM and POC delivered by the Mackenzie River to the Arctic Ocean.

Mackenzie River plume↗
DOE OSTI2020

Provable Convergence of Plug-and-Play Priors With MMSE Denoisers

Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser. While PnP algorithms are well understood for denoisers performing maximum a posteriori probability (MAP) estimation, they have not been analyzed for the minimum mean squared error (MMSE) denoisers. Here we address this gap by establishing the first theoretical convergence result for the iterative shrinkage/thresholding algorithm (ISTA) variant of PnP for MMSE denoisers. We show that the iterates produced by PnP-ISTA with an MMSE denoiser converge to a stationary point of some global cost function. We validate our analysis on sparse signal recovery in compressive sensing by comparing two types of denoisers, namely the exact MMSE denoiser and the approximate MMSE denoiser obtained by training a deep neural net.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Canopy reflectance modeling in a tropical wooded grassland

Geometric/optical canopy reflectance modeling and spatial/spectral pattern recognition are used to study the form and structure of savanna in West Africa. An invertible plant canopy reflectance model is tested for its ability to estimate the amount of woody vegetation cover in areas of sparsely wooded grassland from remotely sensed data. Dry woodlands and wooded grasslands, commonly referred to as savannas, are important ecologically and economically in Africa, and cover approximately forty percent of the continent by some estimates. The Sahelian and Sudanian savanna make up the important and sensitive transition zone between the tropical forests and the arid Saharan region. The depletion of woody cover, used for fodder and fuel in these regions, has become a very severe problem for the people living there. LANDSAT Thematic Mapper (TM) data is used to stratify woodland and wooded grassland into areas of relatively homogeneous canopy cover, and then by applying an invertible forest canopy reflectance model to estimate directly the height and spacing of the trees in the stands. Since height and spacing are proportional to biomass in some cases, a successful application of the segmentation/modeling techniques will allow direct estimation of woody biomass, as well as cover density, over significant areas of these valuable and sensitive ecosystems. Sahelian savanna sites in the Gourma area of Mali being used by the NASA/GIMMS project (Global Inventory Modeling and Monitoring System, at Goddard Space Flight Center), in conjunction with CIPEA/Mali (Centre International pour l'Elevage en Afrique) will be used for testing the canopy model. The model will also be tested in a Sudanian zone crop/woodland area in the Region of Segou, Mali.

Simonett, D.↗
NASA NTRS1986

Canopy reflectance modeling in a tropical wooded grassland

Geometric/optical canopy reflectance modeling and spatial/spectral pattern recognition is used to study the form and structure of savanna in West Africa. An invertible plant canopy reflectance model is tested for its ability to estimate the amount of woody vegetation from remotely sensed data in areas of sparsely wooded grassland. Dry woodlands and wooded grasslands, commonly referred to as savannas, are important ecologically and economically in Africa, and cover approximately forty percent of the continent by some estimates. The Sahel and Sudan savannas make up the important and sensitive transition zone between the tropical forests and the arid Sahara region. The depletion of woody cover, used for fodder and fuel in these regions, has become a very severe problem for the people living there. LANDSAT Thematic Mapper (TM) data is used to stratify woodland and wooded grassland into areas of relatively homogeneous canopy cover, and then an invertible forest canopy reflectance model is applied to estimate directly the height and spacing of the trees in the stands. Because height and spacing are proportional to biomass in some cases, a successful application of the segmentation/modeling techniques will allow direct estimation of tree biomass, as well as cover density, over significant areas of these valuable and sensitive ecosystems. The model being tested in sites in two different bioclimatic zones in Mali, West Africa, will be used for testing the canopy model. Sudanian zone crop/woodland test sites were located in the Region of Segou, Mali.

Simonett, David↗
DOE OSTI2020

Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.

47 OTHER INSTRUMENTATION↗
NASA NTRS1993

Discrete range clustering using Monte Carlo methods

For automatic obstacle avoidance guidance during rotorcraft low altitude flight, a reliable model of the nearby environment is needed. Such a model may be constructed by applying surface fitting techniques to the dense range map obtained by active sensing using radars. However, for covertness, passive sensing techniques using electro-optic sensors are desirable. As opposed to the dense range map obtained via active sensing, passive sensing algorithms produce reliable range at sparse locations, and therefore, surface fitting techniques to fill the gaps in the range measurement are not directly applicable. Both for automatic guidance and as a display for aiding the pilot, these discrete ranges need to be grouped into sets which correspond to objects in the nearby environment. The focus of this paper is on using Monte Carlo methods for clustering range points into meaningful groups. One of the aims of the paper is to explore whether simulated annealing methods offer significant advantage over the basic Monte Carlo method for this class of problems. We compare three different approaches and present application results of these algorithms to a laboratory image sequence and a helicopter flight sequence.

Chatterji, G. B.↗
NASA NTRS2008

Comparison of Satellite-Derived and In-Situ Observations of Ice and Snow Surface Temperatures over Greenland

The most practical way to get a spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land-surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived "clear-sky" LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air-temperature observations from Greenland Climate Network (GC-Net) automatic-weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from -40 to 0 C. The satellite-derived LSTs agree within a relative RMS uncertainty of approx.0.5 C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a "point" while the satellite instruments record data over an area varying in size from: 57 X 57 m (ETM+), 90 X 90 m (ASTER), or to 1 X 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty approx.2 C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.

Hall, Dorothy K.↗
NASA NTRS2018

Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion

The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r(sup 2) = 0.91), BA (r(sup 2) = 0.79), and AGB (r(sup 2) = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/−1%) and aspen BA (−2%) to misallocation of aspen AGB (+118%) and white spruce AGB (−50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error.

aboveground biomass↗
NASA NTRS2023

Auroral, Ionospheric and Ground Magnetic Signatures of Magnetopause Surface Modes

Surface waves on Earth's magnetopause have a controlling effect upon global magnetospheric dynamics. Since spacecraft provide sparse in situ observation points, remote sensing these modes using ground-based instruments in the polar regions is desirable. However, many open conceptual questions on the expected signatures remain. Therefore, we provide predictions of key qualitative features expected in auroral, ionospheric, and ground magnetic observations through both magnetohydrodynamic theory and a global coupled magnetosphere-ionosphere simulation of a magnetopause surface eigenmode. These show monochromatic oscillatory field-aligned currents (FACs), due to both the surface mode and its non-resonant Alfvén coupling, are present throughout the magnetosphere. The currents peak in amplitude at the equatorward edge of the magnetopause boundary layer, not the open-closed boundary as previously thought. They also exhibit slow poleward phase motion rather than being purely evanescent. We suggest the upward FAC perturbations may result in periodic auroral brightenings. In the ionosphere, convection vortices circulate the poleward moving FAC structures. Finally, surface mode signals are predicted in the ground magnetic field, with ionospheric Hall currents rotating perturbations by approximately (but not exactly) 90° compared to the magnetosphere. Thus typical dayside magnetopause surface modes should be strongest in the East-West ground magnetic field component. Overall, all ground-based signatures of the magnetopause surface mode are predicted to have the same frequency across L-shells, amplitudes that maximize near the magnetopause's equatorward edge, and larger latitudinal scales than for field line resonance. Implications in terms of ionospheric Joule heating and geomagnetically induced currents are discussed.

M. O. Archer↗
DOE OSTI2025

Sparse optimization of two-dimensional terahertz spectroscopy

Two-dimensional terahertz spectroscopy (2DTS) is a low-frequency analog of two-dimensional optical spectroscopy that is rapidly maturing as a probe of a wide variety of condensed matter systems. However, a persistent problem with 2DTS is the long experimental acquisition times, which prevent its broader adoption. A potential solution, requiring no increase in experimental complexity, is signal reconstruction via compressive sensing. In this work, we apply the sparse exponential mode analysis (SEMA) technique to 2DTS of a cuprate superconductor. We benchmark the performance of the algorithm in reconstructing terahertz nonlinearities and find that SEMA reproduces the asymmetric photon echo line shapes at sampling rates as low as 10%, reaching the reconstruction noise floor at sampling rates beyond 20%–30%. The success of SEMA in reproducing such subtle, asymmetric line shapes confirms compressive sensing as a general method to accelerate 2DTS and multidimensional spectroscopies more broadly.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1973

Objective sea-level pressure analysis for data-sparse areas.

A computer procedure, described in an earlier study, uses the wind speed field near the ocean surface in combination with a small number of observations of pressure and wind velocity to specify the maritime sea-level pressure field. An improved version was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient midlatitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution.

Druyan, L. M.↗
NASA NTRS1972

Objective sea level pressure analysis for sparse data areas

A computer procedure was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February, 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient mid-latitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution. A generalization of the results indicates that the average lower limit for analysis errors is between 2 and 2.5 mb based on the perfect specification of the magnitude of the sea-level pressure gradient from a known verification analysis. A less than perfect specification will derive from wind-pressure relationships applied to satellite observed wind speeds.

Druyan, L. M.↗
NASA NTRS2007

Timber Volume and Biomass Estimates in Central Siberia from Satellite Data

Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. The biggest deficiency of the existing ground based forest inventories is the uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse, lacking, and often decades old. Remote sensing methods can help overcome these problems. In this joint US and Russian study, we used the moderate resolution imaging spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) and produced a map of timber volume for a 10degx12deg area in Central Siberia. Using these methods, the mean timber volume for the forested area in the total study area was 203 m3/ ha. The new remote sensing methods used in this study provide a truly independent estimate of forest structure, which is not dependent on traditional ground forest inventory methods.

Ranson, K. Jon↗