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All sources NASA NTRS DOE OSTI

DOE OSTI2016

Improving Lidar Turbulence Estimates for Wind Energy

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.

17 WIND ENERGY↗

DOE OSTI2021

Statistical Uncertainty in Paleoclimate Proxy Reconstructions

A quantitative analysis of any environment older than the instrumental record relies on proxies. Uncertainties associated with proxy reconstructions are often underestimated, which can lead to artificial conflict between different proxies, and between data and models. In this paper, using ordinary least squares linear regression as a common example, we describe a simple, robust and generalizable method for quantifying uncertainty in proxy reconstructions. We highlight the primary controls on the magnitude of uncertainty, and compare this simple estimate to equivalent estimates from Bayesian, nonparametric and fiducial statistical frameworks. We discuss when it may be possible to reduce uncertainties, and conclude that the unexplained variance in the calibration must always feature in the uncertainty in the reconstruction. This directs future research toward explaining as much of the variance in the calibration data as possible. We also advocate for a “data-forward” approach, that clearly decouples the presentation of proxy data from plausible environmental inferences.

58 GEOSCIENCES↗

DOE OSTI2013

Survival of Seaward-Migrating PIT and Acoustic-Tagged Juvenile Chinook Salmon in the Snake and Columbia Rivers: An Evaluation of Length-Specific Tagging Effects

Studies examining the survival of juvenile salmon as they emigrate to the ocean provide important information regarding the management of regulated river systems. Acoustic telemetry is a widely used tool for evaluating the behavior and survival of juvenile salmonids in the Columbia River basin. Thus, it is important to understand how the surgical tagging process and the presence of a transmitter affect survival so any biases can be accounted for or eliminated. This study evaluated the effects of fish length and tag type on the survival of yearling and subyearling Chinook salmon during their seaward migrations through the Snake and Columbia rivers during 2006, 2007, and 2008. Fish were collected at Lower Granite Dam on the Snake River (river kilometer 695) and implanted with either only a passive integrated transponder (PIT) tag (PIT fish) or both a PIT tag and an acoustic transmitter (AT fish). Survival was estimated from release at Lower Granite Dam to multiple downstream locations (dams) using the Cormack–Jolly–Seber single release model, and analysis of variance was used to test for differences among length-classes and between tag types. No length-specific tag effect was detected between PIT and AT fish (i.e., length affected the survival of PIT fish in a manner similar to which it affected the survival of AT fish). Survival among the smallest length class (i.e., 80–89 mm) of both PIT and AT subyearling Chinook salmon was markedly low (i.e., 4%). Fish length was positively correlated with the survival of both PIT and AT fish. Significant differences in survival were detected between tag types; the survival of PIT fish was generally greater than that of AT fish. However, confounding variables warrant caution in making strong inferences regarding this factor. Further, results suggest that tag effects may be due to the process of surgically implanting the transmitter rather than the presence of the transmitter.

juvenile Chinook salmon↗

DOE OSTI2013

Consistent quantification of climate impacts due to biogenic carbon storage across a range of bio-product systems

Temporary and permanent carbon storage from biogenic sources is seen as a way to mitigate climate change. The aim of this work is to illustrate the need to harmonize the quantification of such mitigation across all possible storage pools in the bio- and anthroposphere. We investigate nine alternative storage cases and a wide array of bio-resource pools: from annual crops, short rotation woody crops, medium rotation temperate forests, and long rotation boreal forests. For each feedstock type and biogenic carbon storage pool, we quantify the carbon cycle climate impact due to the skewed time distribution between emission and sequestration fluxes in the bio- and anthroposphere. Additional consideration of the climate impact from albedo changes in forests is also illustrated for the boreal forest case. When characterizing climate impact with global warming potentials (GWP), we find a large variance in results which is attributed to different combinations of biomass storage and feedstock systems. The storage of biogenic carbon in any storage pool does not always confer climate benefits: even when biogenic carbon is stored long-term in durable product pools, the climate outcome may still be undesirable when the carbon is sourced from slow-growing biomass feedstock. For example, when biogenic carbon from Norway Spruce from Norway is stored in furniture with a mean life time of 43 years, a climate change impact of 0.08 kg CO{sub 2}eq per kg CO{sub 2} stored (100 year time horizon (TH)) would result. It was also found that when biogenic carbon is stored in a pool with negligible leakage to the atmosphere, the resulting GWP factor is not necessarily − 1 CO{sub 2}eq per kg CO{sub 2} stored. As an example, when biogenic CO{sub 2} from Norway Spruce biomass is stored in geological reservoirs with no leakage, we estimate a GWP of − 0.56 kg CO{sub 2}eq per kg CO{sub 2} stored (100 year TH) when albedo effects are also included. The large variance in GWPs across the range of resource and carbon storage options considered indicates that more accurate accounting will require case-specific factors derived following the methodological guidelines provided in this and recent manuscripts. -- Highlights: • Climate impacts of stored biogenic carbon (bio-C) are consistently quantified. • Temporary storage of bio-C does not always equate to a climate cooling impact. • 1 unit of bio-C stored over a time horizon does not always equate to − 1 unit CO{sub 2}eq. • Discrepancies of climate change impact quantification in literature are clarified.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Estimation of Turbulence Parameters from Scanning Lidars and in-situ Instrumentation in the Perdigao 2017 Campaign

The understanding of the sources, spatial distribution and temporal variability of turbulence in the atmospheric boundary layer (ABL) and improved simulation of its forcing processes require observations in a broad range of terrain types and atmospheric conditions. In this study, we estimate turbulence kinetic energy (TKE) dissipation rate using multiple techniques, including traditional in-situ measurements of sonic anemometers on meteorological towers, a hot-wire anemometer on a tethered lifting system (TLS), as well as remote-sensing retrievals from a vertically staring lidar and two lidars performing range-height indicator (RHI) scans. For the retrieval of e from the lidar RHI scans, we introduce a modification of the Doppler Spectral Width (DSW) method. This method uses spatio-temporal averages of the variance of the line-of-sight (LOS) velocity and the turbulent broadening of the Doppler backscatter spectrum. We validate this method against the observations from the other instruments, also including uncertainty estimations for each method. The synthesis of the results from all instruments enables a detailed analysis of the spatial and temporal variability of e across a valley between two parallel ridges at the Perdigao 2017 campaign. We find that the shear zones above and below nighttime low-level jets (LLJ) experience turbulence enhancements, as does the wake of a wind turbine (WT). We analyze in detail how e varies in the early morning of 14 June 2017, when the turbulence in the valley, approximately eleven rotor diameters downstream of the WT, is still significantly enhanced by the WT wake.

17 WIND ENERGY↗

DOE OSTI2025

Numerical simulation of asteroid geometry variance on airburst threat

For an atmospheric airburst the primary source of concern when assessing uncertainty is the size and velocity. Determining these properties provides the basis for threat assessment, as the total energy of the asteroid may then be estimated, and the threat investigated thoroughly. Even with clarity as to how much energy an asteroid may deposit, a great deal of uncertainty still exists for the actual energy deposition process. One such source of uncertainty is the geometry of the incoming asteroid. The geometry of an asteroid will alter the stress distribution during entry, which adds uncertainty to when fracture will occur. Here, in this study, we use Smoothed Particle Hydrodynamics to model the atmospheric airburst of Tunguska-scale asteroids with varying geometric profiles, including a sphere, ellipsoid, binary and superellipsoid. Each asteroid is modeled as a homogenous structure with strength. We assess uncertainty through a series of planar 2D simulation cases for each geometry, comparing the source of stochasticity across geometries. A single 3D airburst simulation for each geometry is also analyzed. Additionally, the 3D cases are compared to the highly uncertain Tunguska event, predicting variance in burst height across geometries, but all bounded by theoretical burst heights proposed for Tunguska.

Airburst↗

DOE OSTI2014

On the estimation of statistical uncertainties on powder diffraction and small-angle scattering data from two-dimensional X-ray detectors

Optimal methods are explored for obtaining one-dimensional powder pattern intensities from two-dimensional planar detectors with good estimates of their standard deviations. Methods are described to estimate uncertainties when the same image is measured in multiple frames as well as from a single frame. The importance of considering the correlation of diffraction points during the integration and the resampling process of data analysis is shown. It is found that correlations between adjacent pixels in the image can lead to seriously overestimated uncertainties if such correlations are neglected in the integration process. Off-diagonal entries in the variance–covariance (VC) matrix are problematic as virtually all data processing and modeling programs cannot handle the full VC matrix. It is shown that the off-diagonal terms come mainly from the pixel-splitting algorithm used as the default integration algorithm in many popular two-dimensional integration programs, as well as from rebinning and resampling steps later in the processing. When the full VC matrix can be propagated during the data reduction, it is possible to get accurate refined parameters and their uncertainties at the cost of increasing computational complexity. However, as this is not normally possible, the best approximate methods for data processing in order to estimate uncertainties on refined parameters with the greatest accuracy from just the diagonal variance terms in the VC matrix is explored.

Yang, X.↗

DOE OSTI2012

Inter- and Intrafraction Uncertainty in Prostate Bed Image-Guided Radiotherapy

Purpose: The goals of this study were to measure inter- and intrafraction setup error and prostate bed motion (PBM) in patients undergoing post-prostatectomy image-guided radiotherapy (IGRT) and to propose appropriate population-based three-dimensional clinical target volume to planning target volume (CTV-PTV) margins in both non-IGRT and IGRT scenarios. Methods and Materials: In this prospective study, 14 patients underwent adjuvant or salvage radiotherapy to the prostate bed under image guidance using linac-based kilovoltage cone-beam CT (kV-CBCT). Inter- and intrafraction uncertainty/motion was assessed by offline analysis of three consecutive daily kV-CBCT images of each patient: (1) after initial setup to skin marks, (2) after correction for positional error/immediately before radiation treatment, and (3) immediately after treatment. Results: The magnitude of interfraction PBM was 2.1 mm, and intrafraction PBM was 0.4 mm. The maximum inter- and intrafraction prostate bed motion was primarily in the anterior-posterior direction. Margins of at least 3-5 mm with IGRT and 4-7 mm without IGRT (aligning to skin marks) will ensure 95% of the prescribed dose to the clinical target volume in 90% of patients. Conclusions: PBM is a predominant source of intrafraction error compared with setup error and has implications for appropriate PTV margins. Based on inter- and estimated intrafraction motion of the prostate bed using pre- and post-kV-CBCT images, CBCT IGRT to correct for day-to-day variances can potentially reduce CTV-PTV margins by 1-2 mm. CTV-PTV margins for prostate bed treatment in the IGRT and non-IGRT scenarios are proposed; however, in cases with more uncertainty of target delineation and image guidance accuracy, larger margins are recommended.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Multitaper Magnitude‐Squared Coherence for Time Series With Missing Data: Understanding Oscillatory Processes Traced by Multiple Observables

To explore the hypothesis of a common source of variability in two time series, observers may estimate the magnitude-squared coherence (MSC), which is a frequency-domain view of the cross correlation. For time series that do not have uniform observing cadence, MSC can be estimated using Welch's overlapping segment averaging. However, multitaper has superior statistical properties to Welch's method in terms of the tradeoff between bias, variance, and bandwidth. The classical multitaper technique has recently been extended to accommodate time series with underlying uniform observing cadence from which some observations are missing. This situation is common for solar and geomagnetic data sets, which may have gaps due to breaks in satellite coverage, instrument downtime, or poor observing conditions. We demonstrate the scientific use of missing-data multitaper magnitude-squared coherence by detecting known solar mid-term oscillations in simultaneous, missing-data time series of solar Lyman α flux and geomagnetic Disturbance Storm Time index. Due to their superior statistical properties, we recommend that multitaper methods be used for all heliospheric time series with underlying uniform observing cadence.

Astro-statistics techniques (1886)↗

DOE OSTI2017

Prediction of Individual Social-Demographic Role Based on Travel Behavior Variability Using Long-Term GPS Data

With the development of and advances in smartphones and global positioning system (GPS) devices, travelers’ long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-to-Home and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers’ activity behavior features for an extended period. After that, the variability pattern of an individual’s travel behavior is used for estimating the individual’s social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment’s result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. Further, the results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Analysis of land-based CSEM data for CO2 monitoring at Bell Creek, MT

Monitoring subsurface fluid migration is critical to any CO2 injection operation like enhanced oil recovery or carbon storage. The controlled-source electromagnetic (CSEM) method provides a promising low-cost, non-invasive alternative to conventional monitoring methods. To investigate the use of CSEM for monitoring CO2 injection, we are conducting a time-lapse charged well casing CSEM survey at the Bell Creek oil field. There, CO2 is injected into the reservoir for enhanced oil recovery, which also results in incidental carbon storage. From October 2017 to October 2018 we completed three field campaigns, and we plan to complete one more in July 2019. We have analyzed the signal strength and variance of the time-lapse CSEM data. Difference data signal strength well exceeds the standard deviation of the data, and it agrees with estimates of expected signal strength derived from numerical modeling. This analysis indicates that our survey can detect the change in conductivity within the reservoir due to fluid movement.

time-lapse, land CSEM, monitoring, processing↗

DOE OSTI2018

Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses

Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1985

Statistical methods for efficient design of community surveys of response to noise: Random coefficients regression models

Research studies of residents' responses to noise consist of interviews with samples of individuals who are drawn from a number of different compact study areas. The statistical techniques developed provide a basis for those sample design decisions. These techniques are suitable for a wide range of sample survey applications. A sample may consist of a random sample of residents selected from a sample of compact study areas, or in a more complex design, of a sample of residents selected from a sample of larger areas (e.g., cities). The techniques may be applied to estimates of the effects on annoyance of noise level, numbers of noise events, the time-of-day of the events, ambient noise levels, or other factors. Methods are provided for determining, in advance, how accurately these effects can be estimated for different sample sizes and study designs. Using a simple cost function, they also provide for optimum allocation of the sample across the stages of the design for estimating these effects. These techniques are developed via a regression model in which the regression coefficients are assumed to be random, with components of variance associated with the various stages of a multi-stage sample design.

Tomberlin, T. J.↗

NASA NTRS1986

A comparison of methods for DPLL loop filter design

Four design methodologies for loop filters for a class of digital phase-locked loops (DPLLs) are presented. The first design maps an optimum analog filter into the digital domain; the second approach designs a filter that minimizes in discrete time weighted combination of the variance of the phase error due to noise and the sum square of the deterministic phase error component; the third method uses Kalman filter estimation theory to design a filter composed of a least squares fading memory estimator and a predictor. The last design relies on classical theory, including rules for the design of compensators. Linear analysis is used throughout the article to compare different designs, and includes stability, steady state performance and transient behavior of the loops. Design methodology is not critical when the loop update rate can be made high relative to loop bandwidth, as the performance approaches that of continuous time. For low update rates, however, the miminization method is significantly superior to the other methods.

Aguirre, S.↗

NASA NTRS1994

Estimation of the electromagnetic bias from retracked TOPEX data

We examine the electromagnetic (EM) bias by using retracked TOPEX altimeter data. In contrast to previous studies, we use a parameterization of the EM bias which does not make stringent assumptions about the form of the correction or its global behavior. We find that the most effective single parameter correction uses the altimeter-estimated wind speed but that other parameterizations, using a wave age related parameter of significant wave height, may also significantly reduce the repeat pass variance. The different corrections are compared, and their improvement of the TOPEX height variance is quantified.

Rodriguez, Ernesto↗

NASA NTRS2005

Adaptive Filtering Using Recurrent Neural Networks

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Parlos, Alexander G.↗

DOE OSTI2016

A Dirichlet process mixture model for automatic {sup 18}F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

Purpose: The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on {sup 18}F-fluorodeoxyglucose positron emission tomography ({sup 18}F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness. Methods: The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10–37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available. Results: Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve was determined to automatically generate the optimal parameter from the variance of the ROI. This “calibration curve” was then applied to contour the whole data set. The accuracy (mean discrepancy between DPM model-based contours and reference contours) of volume estimation was below (1 ± 7)% on the whole data set (1 SD). The overlap between true and automatically segmented contours, measured by the Dice similarity coefficient, was 0.93 with a SD of 0.03. Conclusions: The proposed DPM model was able to accurately reproduce known volumes of FDG concentration, with high overlap between segmented and true volumes. For all the analyzed inserts of the IEC phantom, the algorithm proved to be robust to variations in radius and in TBR. The main advantage of this algorithm was that no setting of DPM parameters was required in advance, since the proper setting of the only parameter that could significantly influence the segmentation results was automatically related to the uptake variance of the chosen ROI. Furthermore, the algorithm did not need any preliminary choice of the optimum number of classes to describe the ROIs within PET images and no assumption about the shape of the lesion and the uptake heterogeneity of the tracer was required.

60 APPLIED LIFE SCIENCES↗