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NASA NTRS1983

Properties of echo spectra observed by MST radars

Turbulent scatter and Fresnel reflection are the fundamental echoing mechanisms to interpret the signals observed by Mesosphere-stratosphere-troposphere (MST) radars. Turbulent scattered echoes provide information about the turbulence structure and mean flow of the atmosphere. Observational results with VHF MST radars, however, show the importance of Fresnel reflection due to the infinite gradient of reflectivity at the edges of a scattering layer. This condition is excluded for the weak fluctuation models but it is still possible to include the observed aspect sensitivity by assuming an anisotropic structure of fluctuations. Another explanation of the aspect sensitivity observed by MST radars is advanced. Spectral estimates by the widely used periodogram were related to a four-dimensional spectrum of atmospheric fluctuations with anisotropic structure. Effects of the radar system such as antenna beam width, beam direction and Fast Fourier Transformations (FFT) data length were discussed for the anisotropic turbulent atmosphere. Echo parameters were also estimated.

Wakasugi, K.↗

DOE OSTI2025

Discriminative versus generative approaches to simulation-based inference

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.

high energy physics↗

NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗

NASA NTRS2007

Arctic Sea Ice Parameters from AMSR-E Data using Two Techniques, and Comparisons with Sea Ice from SSM

We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of sea ice concentration, ice extent and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly ice concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated ice region being generally comparable for ABA and NT2 retrievals while data in the marginal ice zones and thin ice regions show higher values when the NT2 algorithm is used. The ice extents and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for ice extents and -6.6 x 10(exp 4) square kilometers for ice area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for ice extent and area: respectively. Likewise, extents and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of extents than area mainly because of differences in channel selection and sensor resolutions. Trends in extent during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).

Comiso, Josefino C.↗

DOE OSTI2019

Design and implementation of a real-time underwater acoustic telemetry system for fish behavior study and environmental sensing

Underwater acoustic telemetry is an important tool for studying the behavior of aquatic animals and assessing the environmental impact of man-made structures such as water power turbines. However, current design of the acoustic telemetry system lacks the ability to transmit collected data continuously in real-time and requires manual retrieval of the recorded measurements, leading to a long delay of data retrieval, high operational cost associated with manpower as well as safety risk for the operators. In addition, there is no efficient way to monitor the status of the acoustic telemetry system, including acoustic transmitters and receivers. For example, if multiple receivers in an array fail, the detection efficiency could be significantly reduced. Herein, we developed a cloud-based, real-time underwater acoustic telemetry system that can be used to estimate behavior or survival of tagged aquatic animals and monitor environmental parameters in near-real-time (< 2 hours). The system incorporates microcontrollers and integrated circuits to create real-time data communication and processing units with a cloud-based service that processes the transmitted data stream to derive behavior and survival information of the tagged animals.

Yang, Yang↗

NASA NTRS2021

The Use of Astronomy VLBA Campaign MOJAVE for Geodesy

We investigated the suitability of the astronomical 15 GHz VLBA observing program MOJAVE-5 for estimation of geodetic parameters, such as station coordinates and Earth orientation parameters. We processed contemporary geodetic dual-band RV and CN experiments observed at 2.3 GHz and 8.6 GHz starting on September 2016 through July 2020 as reference dataset. We showed that the baseline length repeatability from MOJAVE-5 experiments is only a factor of 1.5 greater than from the dedicated geodetic dataset and still below 1 ppb. The wrms of the difference of estimated EOP with respect to the reference IERS C04 time series are a factor of 1.3 to 1.8 worse. We isolated three major differences between the datasets in terms their possible impact on the geodetic results, i.e. the scheduling approach, treatment of the ionospheric delay, and selection of target radio sources. We showed that the major factor causing discrepancies in the estimated geodetic parameters is the different scheduling approach of the datasets. We conclude that systematic errors in MOJAVE-5 dataset are low enough for these data to be used as an excellent testbed for further investigations on the radio source structure effects in geodesy and astrometry.

Hana Krasna↗

DOE OSTI2022

Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon “reanalysis” product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower net ecosystem exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit–Wishart ensemble filter (TWEnF) state data assimilation (SDA) framework, a generalization of the common ensemble Kalman filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ∼ 500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and leaf area index (LAI) (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced root mean square error (RMSE) compared to the calibrated forecast. Ultimately, PEcAn's new open-source regional data assimilation framework provides a scalable workflow for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) as well as accelerating terrestrial carbon cycle research.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Quantifying Model Form Uncertainty of Epidemic Forecasting Models from Incidence Data

Objective We present a mathematical framework for non-parametric estimation of the force of infection, together with statistical upper and lower confidence bands. The resulting estimates allow to assess how well simpler models, such as SEIR, fit the observed time series of incidence data.IntroductionUncertainty Quantification (UQ), the ability to quantify the impact of sample-to-sample variations and model misspecification on predictions and forecasts, is a critical aspect of disease surveillance. While quantifying the impact of stochastic uncertainty in the data is well understood, quantifying the impact of model misspecification is significantly harder. For the latter, one needs a "universal model" to which more restrictive parametric models are compared too.MethodsThis talk presents a useful modeling framework for time series of incidence data from contagious diseases that enables one to identify and quantify the impact of model form uncertainty. Specifically, we propose to focus on estimating the timedependent force of infection. The latter is a universal parameters for all contagious disease model. Using a machine learning technique for estimating monotone functions, i.e., isotonic regression and its variants, one can estimate the force ofinfection without addtional assumptions. We note that most contagious disease model do satisfy this monotonicity assumption, due to a combination of factors: depletion of susceptibles, implementation of mitigation strategies, behavior change, etc. Comparing the resulting "non-parametric" estimate with parametric estimates, obtained by fitting an SEIR for example, can reveal model deficiencies and help quantify model form uncertainties.Finally, we discuss how ideas from "strict bound theory" can be used to develop upper and lower uncertainty bands for force of infection that acknowledge the intrinsic stochasticity in the data.ResultsWe demonstrate the application of the methodology to weekly Influenza Like Illness (ILI) incidence data from France andcompare the results to fitted SIR and SEIR models. This comparison can be seen as a nonparametric goodness of fit test, providing one with tools to do simple model selection.ConclusionsWe present a novel and flexible model to statistically describe the force of infection as a function of time. Comparing the fit to incidence data of that model with the fit of simpler parametric models enables the quantification of model form uncertainty and associated model selection.

Hengartner, Nicholas↗

NASA NTRS1994

A geopotential model from satellite tracking, altimeter, and surface gravity data: GEM-T3

An improved model of Earth's gravitational field, Goddard Earth Model T-3 (GEM-T3), has been developed from a combination of satellite tracking, satellite altimeter, and surface gravimetric data. GEM-T3 provides a significant improvement in the modeling of the gravity field at half wavelengths of 400 km and longer. This model, complete to degree and order 50, yields more accurate satellite orbits and an improved geoid representation than previous Goddard Earth Models. GEM-T3 uses altimeter data from GEOS 3 (1975-1976), Seasat (1978) and Geosat (1986-1987). Tracking information used in the solution includes more than 1300 arcs of data encompassing 31 different satellites. The recovery of the long-wavelength components of the solution relies mostly on highly precise satellite laser ranging (SLR) data, but also includes Tracking Network (TRANET) Doppler, optical, and satellite-to-satellite tracking acquired between the ATS 6 and GEOS 3 satellites. The main advances over GEM-T2 (beyond the inclusion of altimeter and surface gravity information which is essential for the resolution of the shorter wavelength geoid) are some improved tracking data analysis approaches and additional SLR data. Although the use of altimeter data has greatly enhanced the modeling of the ocean geoid between 65 deg N and 60 deg S latitudes in GEM-T3, the lack of accurate detailed surface gravimetry leaves poor geoid resolution over many continental regions of great tectonic interest (e.g., Himalayas, Andes). Estimates of polar motion, tracking station coordinates, and long-wavelength ocean tidal terms were also made (accounting for 6330 parameters). GEM-T3 has undergone error calibration using a technique based on subset solutions to produce reliable error estimates. The calibration is based on the condition that the expected mean square deviation of a subset gravity solution from the full set values is predicted by the solutions' error covariances. Data weights are iteratively adjusted until this condition for the error calibration is satisfied. In addition, gravity field tests were performed on strong satellite data sets withheld from the solution (thereby ensuring their independence). In these tests, the performance of the subset models on the withheld observations is compared to error projections based on their calibrated error covariances. These results demonstrate that orbit accuracy projections are reliable for new satellites which were not included in GEM-T3.

Lerch, F. J.↗

DOE OSTI2020

Fatigue behavior and modeling for additive manufactured 304L stainless steel: The effect of surface roughness

The fatigue strength of additively manufactured metallic parts in their as-built surface condition is mainly dominated by the surface roughness. Post-processing is often inevitable to reduce surface roughness effects even though post-processing diminishes the main advantage of additive manufacturing, which is net-shaped direct-to-service production. This work investigates the underlying mechanisms responsible for fatigue failure of additively manufactured 304L stainless steel parts in as-built and machined/polished surface conditions. Both strain- and force-controlled, fully reversed fatigue tests were conducted to gain a comprehensive understanding of surface roughness effects on fatigue behavior. The sensitivity to surface roughness is shown to be dependent on the control mode, with stress-based fatigue tests showing greater sensitivity than strain-based fatigue tests. Moreover, the fatigue life estimation for as-built specimens was performed based on surface roughness parameters as well as the fatigue properties of the specimens in machined/polished surface condition of the material without using any fatigue data of specimens in as-built surface condition. Accordingly, the effect of surface roughness on the fatigue behavior could be estimated reasonably well in both strain-life and stress-life approaches.

36 MATERIALS SCIENCE↗

NASA NTRS1993

Wind field model-based estimation of Seasat scatterometer winds

A model-based approach to estimating near-surface wind fields over the ocean from Seasat scatterometer (SASS) measurements is presented. The approach is a direct assimilation technique in which wind field model parameters are estimated directly from the scatterometer measurements of the radar backscatter of the ocean's surface using maximum likelihood principles. The wind field estimate is then computed from the estimated model parameters. The wind field model used in this approach is based on geostrophic approximation and on simplistic assumptions about the wind field vorticity and divergence but includes ageostrophic winds. Nine days of SASS data were processed to obtain unique wind estimates. Comparisons in performance to the traditional two-step (point-wise wind retrieval followed by ambiguity removal) wind estimate method and the model-based method are provided using both simulated radar backscatter measurements and actual SASS measurements. In the latter case the results are compared to wind fields determined using subjective ambiguity removal. While the traditional approach results in missing measurements and reduced effective swath width due to fore/aft beam cell coregistration problems, the model-based approach uses all available measurements to increase the effective swath width and to reduce data gaps. The results reveal that the model-based wind estimates have accuracy comparable to traditionally estimated winds with less 'noise' in the directional estimates, particularly at low wind speeds.

Long, David G.↗

NASA NTRS1995

The second-moment climatology of the GATE rain rate data

The first part of this paper presents the description of the GARP (Global Atmospheric Research Program) Atlantic Tropical Experiment (GATE) 1 rain-rate data and its two-dimensional spectral and correlation characteristics, which has made it possible to accomplish the following: to show the concentration of a significant power along the frequency axis in the spatiotemporal spectra; to detect a diurnal cycle (which has a range of variation of about 3.4-5.4 mm/n) as one of the sources of bias in the rain statistics of satellite data; to study the distinction between the north-south and east-west transport of spatial rain-rate field and character of its anisotropy; to evaluate the scales of the distinction between second-moment estimates associated with ground and satellite samples; and to determine the appropriate spatial and temporal scales of simple linear stochastic models fitted to averaged rain-rate fields. The second part of this paper is devoted to an analysis of the diffusion of the rain rate by establishing a relationship between the parameters of the multivariate autoregressive model and the coefficients of a diffusion equation. This analysis led to the use of rain data to estimate the rain advection velocity as well as other coefficients of the diffusion equation of the corresponding field. The results obtained can be used for comparison with corresponding estimates of other sources of data (satellite, Tropical Oceans Global Atmosphere Coupled Ocean - Atmosphere Response Experiment (TOGA, COARE) or simulated by physical models), for generating multiple samples of any size, for solving the inverse problems of some of the hydrodynamic equations, and in some other areas of rain data analysis and modeling.

Polyak, Ilya↗

NASA NTRSDate not supplied

CONUS and Global Carbon Consumption Database for Wildland Fire

Fire plays a significant role on both national and global scales, profoundly impacting landscapes shaped by human activity as well as wildlands. Even though fire can be devastating, wildland fire is a natural and integral force on our landscapes. Fires can also serve to reduce fuels to mitigate wildfire risk and maintain healthy ecosystem functions. However, the smoke produced by fires, regardless of their size or purpose, can pose adverse effects on human health when inhaled downwind. Understanding the influence of smoke on air quality and human well-being necessitates the quantification of emissions that fires release into the atmosphere. In response to this need, we have developed a carbon consumption database for the Continental United States (CONUS) and are developing a global carbon consumption database, both of which are directly link to distinct fuels within various fire danger categories. Fuel Characteristic Classification System (FCCS) fuels are used to parameterize biomass, and consumption is broken down into five Fire Danger categories (Low, Moderate, High, Very High, Extreme), for both ‘new’ and ‘residual’ burning scenarios. Residual burned area is defined as burning in areas that have recently burned. We implemented the FCCS 30-meter CONUS fuelbed dataset during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign to estimate daily carbon emissions. Our emissions estimates were rigorously compared against in-situ measurements of CO2, CO, and black carbon aerosols, revealing a robust agreement between the two datasets. The 300-meter global database builds upon the foundations of the Pettinari, M. Lucrecia (2015) Global Fuelbed database, a global fuel map with standardized FCCS biomass parameters. These products can serve as valuable tools when used in conjunction with burned area data to rapidly and accurately estimate carbon consumed and released into the atmosphere.

FCCS↗

NASA NTRS1979

Multistage variable probability forest volume inventory

An inventory scheme based on the use of computer processed LANDSAT MSS data was developed. Output from the inventory scheme provides an estimate of the standing net saw timber volume of a major timber species on a selected forested area of the Navajo Nation. Such estimates are based on the values of parameters currently used for scaled sawlog conversion to mill output. The multistage variable probability sampling appears capable of producing estimates which compare favorably with those produced using conventional techniques. In addition, the reduction in time, manpower, and overall costs lend it to numerous applications.

Anderson, J. E.↗

DOE OSTI2016

DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks

Gene expression is controlled by the combinatorial effects of regulatory factors from different biological subsystems such as general transcription factors (TFs), cellular growth factors and microRNAs. A subsystem’s gene expression may be controlled by its internal regulatory factors, exclusively, or by external subsystems, or by both. It is thus useful to distinguish the degree to which a subsystem is regulated internally or externally–e.g., how non-conserved, species-specific TFs affect the expression of conserved, cross-species genes during evolution. We developed a computational method (DREISS, dreiss.gerteinlab.org) for analyzing the Dynamics of gene expression driven by Regulatory networks, both External and Internal based on State Space models. Given a subsystem, the “state” and “control” in the model refer to its own (internal) and another subsystem’s (external) gene expression levels. The state at a given time is determined by the state and control at a previous time. Because typical time-series data do not have enough samples to fully estimate the model’s parameters, DREISS uses dimensionality reduction, and identifies canonical temporal expression trajectories (e.g., degradation, growth and oscillation) representing the regulatory effects emanating from various subsystems. To demonstrate capabilities of DREISS, we study the regulatory effects of evolutionarily conserved vs. divergent TFs across distant species. In particular, we applied DREISS to the time-series gene expression datasets of C. elegans and D. melanogaster during their embryonic development. We analyzed the expression dynamics of the conserved, orthologous genes (orthologs), seeing the degree to which these can be accounted for by orthologous (internal) versus species-specific (external) TFs. We found that between two species, the orthologs have matched, internally driven expression patterns but very different externally driven ones. This is particularly true for genes with evolutionarily ancient functions (e.g. the ribosomal proteins), in contrast to those with more recently evolved functions (e.g., cell-cell communication). This suggests that despite striking morphological differences, some fundamental embryonic-developmental processes are still controlled by ancient regulatory systems.

Biochemistry & Molecular Biology↗

NASA NTRS1983

Design criteria and eigensequence plots for satellite computed tomography

The use of the degrees of freedom for signal is proposed as a design criteria for comparing different designs for satellite and other measuring systems. It is also proposed that certain eigensequence plots be examined at the design stage along with appropriate estimates of the parameter lambda playing the role of noise to signal ratio. The degrees of freedom for signal and the eigensequence plots may be determined using prior information in the spectral domain which is presently available along with a description of the system, and simulated data for estimating lambda. This work extends the 1972 work of Weinreb and Crosby.

Wahba, G.↗

NASA NTRS1985

Design criteria and eigensequence plots for satellite-computed tomography

Wahba, G.↗

NASA NTRS1997

Analysis of Particle Image Velocimetry (PIV) Data for Acoustic Velocity Measurements

Acoustic velocity measurements were taken using Particle Image Velocimetry (PIV) in a Normal Incidence Tube configuration at various frequency, phase, and amplitude levels. This report presents the results of the PIV analysis and data reduction portions of the test and details the processing that was done. Estimates of lower measurement sensitivity levels were determined based on PIV image quality, correlation, and noise level parameters used in the test. Comparison of measurements with linear acoustic theory are presented. The onset of nonlinear, harmonic frequency acoustic levels were also studied for various decibel and frequency levels ranging from 90 to 132 dB and 500 to 3000 Hz, respectively.

Blackshire, James L.↗