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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation: Soil Moisture Active Passive (SMAP) Project Assessment Report for the Beta-Release L4_SM Data Product - Volume 40

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public beta release scheduled for 30 October 2015. The primary objective of the beta release is to allow users to familiarize themselves with the data product before the validated product becomes available. The beta release also allows users to conduct their own assessment of the data and to provide feedback to the L4_SM science data product team. The assessment of the L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to upscaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 1 validation and supports the beta release of the data. The validation against sparse network measurements and the evaluation of the assimilation diagnostics address Stage 2 validation criteria by expanding the assessment to regional and global scales.

ubRMSE↗
NASA NTRS1987

A hypermatrix formulation for subspace iteration

The computational efficiency of subspace iteration is addressed relative to the data structures adopted for the very large and generally sparse coefficient matrices. The frequent triangulations and matrix multiplications demand that access to the terms in the coefficient matrices be unbiased. Reliance on virtual memory (paging) operating systems with no special considerations for localized data access is not adequate. Specific data structures must be designed that accommodate the needs of the numerical algorithm yet eliminate unnecessary paging. An implementation of the subspace iteration method using hypermatrix data structures is presented. Use of hypermatrices is shown to provide unbiased and localized data access. The various modifications to the conventional formulation are described and an example problem illustrates the potential benefits of the hypermatrix formulation. Possibilities for adapting hypermatrix data structures to new supercomputer architectures are discussed.

Schmidt, Richard J.↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
DOE OSTI2025

On the Recoverability of Reactor Dynamics from Point Kinetics Data using SINDYc

Data driven models for reactor dynamics tend to face a few notable challenges. First, the broad range of timescales present across the various feedback mechanisms. Next, high standards for safety and importance of model performance in all operational domains. Finally, the correlations between state variables observed in most transients leads to difficulties when methods attempt to attribute certain dynamic phenomena to a particular cause. The current paper seeks to support efforts towards incorporating physics knowledge into one particular data-driven method for finding reactor dynamics called ``Sparse Identification of Nonlinear Dynamics with Control (SINDYc). The incorporation of physical knowledge into SINDYc will help address the challenges listed above by giving the model an initial understanding of the system being modeled. The demonstration of an exact representation of a set of point reactor dynamics equations in SINDYc is provided. Then, this allows for further discussion with mathematical justification as to why SINDYc, and other data-driven methods, may be difficult to apply to nuclear reactor dynamics due to correlations in the state variables. A particularly useful result of this work is the set of candidate functions required in SINDYc to exactly represent the reactor dynamics under a point approximation. In the future, SINDYc can be integrated with point models for reactor dynamics before being applied to the physical system to yield higher accuracy.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2001

The Impact of QuikScat on Weather Analysis and Forecasting

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse, mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific The SeaWinds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). More details on the SeaWinds instrument can be found in Atlas et al. (2001) and Bloom et al. (1999). This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS).

Atlas, Robert↗
NASA NTRS2003

Impact of SeaWinds Scatterometer Data on Numerical Weather Prediction

Scatterometer observations of the ocean surface wind speed and direction improve the depiction and prediction of storms at sea. These data are especially valuable where observations are otherwise sparse - mostly in the Southern Hemisphere and tropics, but also on occasion in the North Atlantic and North Pacific. The SeaWinds scatterometer on the QuikScat satellite was launched in June 1999 and it represents a dramatic departure in design from the other scatterometer instruments launched during the past decade (ERS-1,2 and NSCAT). More details on the SeaWinds instrument can be found in Atlas et a1 (2001) and Bloom et al. (1999). At the time of this writing, SeaWinds scatterometer data from the ADEOS 2 satellite are not yet available . Therefore this paper will be limited to results from the SeaWinds scatterometer on Quikscat. This presentation shows the influence of QuikScat data in data assimilation systems both from the NASA Data Assimilation Office (GEOS-3) and from NCEP (GDAS).

Atlas, Robert↗
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.↗
DOE OSTI2025

Interior soft x-ray tomography with sparse global sampling

To investigate the feasibility of interior imaging reconstruction in soft X-ray tomography for higher-resolution cellular imaging, including whole-cell imaging, we develop an alignment and reconstruction algorithm that combines a small number of sparse whole-cell images with a high-resolution local interior scan. Based on numerical simulations, we demonstrate that combined reconstructions mitigate the depth-of-field limitation in high-resolution scans, enable radiation dose optimization, and yield quantitative X-ray absorption values with sparse sampling. We further validate our numerical approach using experimental data from two different cell types and show that the combined reconstruction reliably provides high spatial resolution within an interior region of interest of a whole cell. The resulting sparse reconstruction framework offers robust, faithful visualization of cellular organelles in soft X-ray tomography. This mesoscale imaging strategy allows one to ‘scout’ and zoom into selected subcellular volumes of interest, enabling increased spatial resolution without sacrificing larger-volume imaging and providing information on the relative positions of all organelles within a cell.

3D imaging↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗
NASA NTRS1983

Satellite-derived sea surface temperature: Introduction

Satellites now play an increasing role in systematic monitoring of the global oceans. Measurements of sea surface temperature (SST) are of primary importance in understanding heat storage and transport within the ocean and cross the ocean-atmosphere boundary. In some regions, local changes in SST of only 1 to 2 C have major effects on global climate and weather patterns. The satellite measurements provide a data base complementary to the (sometimes) accurate but sparsely-distributed point measurements available from ships and buoys. The demands placed on satellite sensors are stringent. Accuracies of better than 1 C are required and are often desired to a few tenths of a degree. Furthermore, measurement accuracies must be stable spatially and temporally in order for satellite data to be used with confidence in models of air-sea interaction and climate. There now exists a need to evaluate objectively the performance of the latest generation of sensors under a sufficient variety of environmental conditions to indicate present accuracies, deficiencies, and potential for improvement.

Njoku, E. G.↗
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↗
DOE OSTI2025

FY24 Task 4: Studies of Phosphate and Fluoride Solubility for Dissolution of High Phosphate Tank Waste

Knowledge gaps have been identified in phosphate solubility in almost every single- and multi-component system, and particularly for aluminate in phosphate/hydroxide, where uncertainties in predicted values of aluminum solubility are greater than 50%. Solubility data of multicomponent, aqueous electrolytes containing sodium hydroxide, sodium fluoride, sodium phosphate, sodium nitrite, sodium nitrate, and dissolved gibbsite are also sparse. For example, for solutions of (i) sodium nitrate and sodium phosphate, data are only available at 30 and 50 °C, and for (ii) dissolved gibbsite in sodium hydroxide and sodium phosphate, only two data points are available at 20 and 40 °C. There is no data available on mixtures of sodium hydroxide, sodium phosphate, sodium fluoride, and dissolved gibbsite. These knowledge gaps were identified in a technical review of waste solubility data and the impact of dilution on solution stabilities and will be addressed in this work to predict aluminum hydroxide and sodium phosphate solubility in multicomponent electrolytes upon dilution, and upon variation of temperature. Results will provide the technical basis to develop accurate models for (i) gibbsite solubility and mass transfer of aluminum between solid and liquid forms following the sluicing of sludge and saltcake with water; and (ii) further blending of these suspensions with bismuth from bismuth phosphate waste, and zirconium and uranium left from the fuel decladding. This work will be essential to developing a disposition path for retrieval solutions from bismuth phosphate wastes. This effort would also support sludge washing to further reduce phosphate concentration if that process were to be added back to the flowsheet in the future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Compressive Sensing Application for Transient Photometric Measurements

Compressive Sensing (CS) is a mathematical theory for simultaneous data acquisition and compression. Natural phenomena may be sparse in some physical or temporal domain. If we exploit this sparsity by applying the technique of CS to obtain information, how do our measurements change as a function of domain and measurement systematics? What are the specific implications for the science and for the sensing infrastructure? In this talk, we will discuss the generalized systematic effects consequent to the application of CS to time-series photometric measurements. We assess implications for observability, sparsification, and information loss in the detection, retrieval and reconstruction process. To study time-series photometry, we explore the field of gravitational microlensing. A source star, typically in the galactic bulge, gets microlensed when there is a precise alignment of a lensing star and its planetary system, with the source star. The microlensed source star changes in flux magnification as the lensing system crosses the precise path of alignment, resulting in a microlensing curve in time domain. A high-cadence, high-resolution system, which uses low power and bandwidth is essential to obtain valuable science measurements. Hence, we discuss application of CS to gravitational microlensing data sets, which in turn can be generalized to any time-ordered photometric measurements.

Asmita Abhay Korde↗
DOE OSTI2021

The Geography of Employment Outcomes for Radiation Oncology Graduates in 2019

The geographic distribution of practicing radiation oncologists is of concern for multiple stakeholders within the field. Employment outcomes of graduating residents can affect that distribution, and they are of major concern to current residents. Data investigating employment outcomes of recent graduates are sparse. We aimed to analyze the employment outcomes of the radiation oncology residency class of 2019.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Convergent Concordant Mode Approach for Molecular Vibrations: CMA-2

The concordant mode approach (CMA) is a promising new scheme for dramatically increasing the system size and level of theory achievable in quantum chemical computations of molecular vibrational frequencies. Here, we achieve advances in the CMA hierarchy by computations targeting CCSD(T)/cc-pVTZ (coupled cluster singles and doubles with perturbative triples using a correlation-consistent polarized-valence triple-ζ basis set) benchmarks within the G2 molecular test set, executing a statistical analysis for 1501 frequencies from 111 compounds and then separately solving the refractory case of pyridine. First, MP2/cc-pVTZ (second-order Møller–Plesset perturbation theory with the same basis set) proves to be an excellent and preferred choice for generating the underlying (Level B) normal modes of the CMA scheme. Utilizing this Level B within the CMA-0A method reproduces the 1501 benchmark frequencies with a mean absolute error (MAE) of only 0.11 cm –1 and an attendant standard deviation of 0.49 cm –1 . Second, a convergent CMA-2 method is constituted that allows efficient computation of higher level (Level A) frequencies to any reasonable accuracy threshold by using only Hartree–Fock (HF) and MP2 or density functional theory (DFT) data to generate ξ parameters, which select the sparse off-diagonal force field elements for explicit evaluation at Level A. When Level B = MP2/cc-pVTZ, a cutoff of ξ = 0.02 provides an average maximum absolute error per molecule of only 0.17 cm –1 by incurring merely a 33% increase in average cost over CMA-0A. This CMA-2 method also eradicates the 4 problematic CMA-0A outliers of pyridine with even less effort (ξ = 0.04, 22% increase). Finally, the newly developed CMA procedures are shown to be highly successful when applied to 1-(1H-pyrrol-3-yl)ethanol, a new test molecule with diverse types of vibration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The erosion of biodiversity and biomass in the Atlantic Forest biodiversity hotspot

Tropical forests are being deforested worldwide, and the remaining fragments are suffering from biomass and biodiversity erosion. Quantifying this erosion is challenging because ground data on tropical biodiversity and biomass are often sparse. Here, we use an unprecedented dataset of 1819 field surveys covering the entire Atlantic Forest biodiversity hotspot. We show that 83-85% of the surveys presented losses in forest biomass and tree species richness, functional traits, and conservation value. On average, forest fragments have 25-32% less biomass, 23-31% fewer species, and 33, 36, and 42% fewer individuals of late-successional, large-seeded, and endemic species, respectively. Biodiversity and biomass erosion are lower inside strictly protected conservation units, particularly in large ones. We estimate that biomass erosion across the Atlantic Forest remnants is equivalent to the loss of 55-70 thousand km 2 of forests or US$2.3-2.6 billion in carbon credits. These figures have direct implications on mechanisms of climate change mitigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Discovering the Unknowns: A First Step

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.

42 ENGINEERING↗