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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.

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NASA NTRS1998

Minimization of Retinal Slip Cannot Explain Human Smooth-Pursuit Eye Movements

Existing models assume that pursuit attempts a direct minimization of retinal image motion or "slip" (e.g. Robinson et al., 1986; Krauzlis & Weisberger, 1989). Using occluded line-figure stimuli, we have previously shown that humans can accurately pursue stimuli for which perfect tracking does not zero retinal slip (Neurologic ARCO). These findings are inconsistent with the standard control strategy of matching eye motion to a target-motion signal reconstructed by adding retinal slip and eye motion, but consistent with a visual front-end which estimates target motion via a global spatio-temporal integration for pursuit and perception. Another possible explanation is that pursuit simply attempts to minimize slip perpendicular to the segments (and neglects parallel "sliding" motion). To resolve this, 4 observers (3 naive) were asked to pursue the center of 2 types of stimuli with identical velocity-space descriptions and matched motion energy. The line-figure "diamond" stimulus was viewed through 2 invisible 3 deg-wide vertical apertures (38 cd/m2 equal to background) such that only the sinusoidal motion of 4 oblique line segments (44 cd/m2 was visible. The "cross" was identical except that the segments exchanged positions. Two trajectories (8's and infinity's) with 4 possible initial directions were randomly interleaved (1.25 cycles, 2.5s period, Ax = Ay = 1.4 deg). In 91% of trials, the diamond appeared rigid. Correspondingly, pursuit was vigorous (mean Again: 0.74) with a V/H aspect ratio approx. 1 (mean: 0.9). Despite a valid rigid solution, the cross however appeared rigid in 8% of trials. Correspondingly, pursuit was weaker (mean Hgain: 0.38) with an incorrect aspect ratio (mean: 1.5). If pursuit were just minimizing perpendicular slip, performance would be the same in both conditions.

Stone, Leland S.↗
DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Compactly‐Supported Nonstationary Kernels for Computing Exact Gaussian Processes on Big Data

The Gaussian process (GP) is a widely used method for analyzing large-scale data sets, including spatio-temporal measurements of nonlinear processes that are now commonplace in the environmental sciences. Traditional implementations of GPs involve stationary kernels (also termed covariance functions) that limit their flexibility, and exact methods for inference that prevent application to data sets with more than about 10,000 points. Modern approaches to address stationarity assumptions generally fail to accommodate large data sets, while all attempts to address scalability focus on approximating the Gaussian likelihood, which can involve subjectivity and lead to inaccuracies. In this work, we explicitly derive an alternative kernel that can discover and encode both sparsity and nonstationarity. We embed the kernel within a fully Bayesian GP model and leverage high-performance computing resources to enable the analysis of massive data sets. We demonstrate the favorable performance of our novel kernel relative to existing exact and approximate GP methods across a variety of synthetic data examples. Furthermore, we conduct space–time prediction based on more than 1 million measurements of daily maximum temperature and verify that our results outperform state-of-the-art methods in the Earth sciences. More broadly, having access to exact GPs that use ultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods to truly compete with a wide variety of machine learning methods.

Gaussian processes↗
DOE OSTI2026

The ABCs of On-Demand Transit (ODT)

On-demand mobility - also referred to as on-demand transit (ODT) - is a form of public mobility that is flexible with respect to where and when service is provided, and ODT deployments have increased significantly in recent years. Transit agencies are becoming increasingly interested in ODT, and due to differing definitions and various service design and business model options, it can be difficult to learn about the emerging industry. This work provides an overview of the definitions of ODT, recent trends internationally and in the U.S., ODT's benefits and challenges (particularly compared to fixed-route transit), three primary service design options, system costs and funding considerations, and a metrics framework for evaluating ODT systems to ensure continued successful performance. Identified benefits include increased service areas, short ride and wait times, increased user flexibility, potential to reduce energy consumption and emissions through shared trips and smaller, right-sized vehicles, increased safety and comfort through door-to-door service, and rich data streams including granular spatio-temporal data that can be analyzed to continuously improve the service. Challenges include scaling ODT service up as small increases in ridership require additional supply to keep service quality high, serving peak times including keeping low wait times, the lack of fixed schedule being challenging for commuters, integrating ODT services with nearby transit systems, and equity for riders without smartphones who cannot track the vehicle in a mobile app. Finally, an overview of seven ODT case studies (in Texas, Missouri, New York, and Ontario, Canada) performed by NREL and related analysis of travel time, energy and emissions, costs, and equity are presented. Initial key findings include: ODT can be cost- and energy-effective compared to fixed-route transit, ODT serves more people than other transit options, and ODT system deployments can be followed by rapid growth.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Cool Flames in Propane-Oxygen Premixtures at Low and Intermediate Temperatures at Reduced-Gravity

The Cool Flame Experiment aims to address the role of diffusive transport on the structure and the stability of gas-phase, non-isothermal, hydrocarbon oxidation reactions, cool flames and auto-ignition fronts in an unstirred, static reactor. These reactions cannot be studied on Earth where natural convection due to self-heating during the course of slow reaction dominates diffusive transport and produces spatio-temporal variations in the thermal and thus species concentration profiles. On Earth, reactions with associated Rayleigh numbers (Ra) less than the critical Ra for onset of convection (Ra(sub cr) approx. 600) cannot be achieved in laboratory-scale vessels for conditions representative of nearly all low-temperature reactions. In fact, the Ra at 1g ranges from 10(exp 4) - 10(exp 5) (or larger), while at reduced-gravity, these values can be reduced two to six orders of magnitude (below Ra(sub cr)), depending on the reduced-gravity test facility. Currently, laboratory (1g) and NASA s KC-135 reduced-gravity (g) aircraft studies are being conducted in parallel with the development of a detailed chemical kinetic model that includes thermal and species diffusion. Select experiments have also been conducted at partial gravity (Martian, 0.3gearth) aboard the KC-135 aircraft. This paper discusses these preliminary results for propane-oxygen premixtures in the low to intermediate temperature range (310- 350 C) at reduced-gravity.

Pearlman, Howard↗
NASA NTRS2006

Detection of Ice Polar Stratospheric Clouds from Assimilation of Atmospheric Infrared Sounder Data

A novel technique is presented for detection of ice polar stratospheric clouds (PSCs) that form at extremely low temperatures in the lower polar stratosphere during winter. Temperature is a major factor in determining abundance of PSCs, which in turn provide surfaces for heterogeneous chemical reactions leading to ozone loss and radiative cooling. The technique infers the presence of ice PSCs using radiances from the Atmospheric Infrared Sounder (AIRS) in the Goddard Earth Observing System version 5 (GEOS-5) data assimilation system. Brightness temperatures are computed from short-term GEOS-5 forecasts for several hundred AIRS channels, using a radiation transfer module. The differences between collocated AIRS observations and these computed values are the observed-minus-forecast (O-F) residuals in the assimilation system. Because the radiation model assumes clear-sky conditions, we hypothesize that these O-F residuals contain quantitative information about PSCs. This is confirmed using sparse data from the Polar Ozone and Aerosol Measurement (POAM) III occultation instrument. The analysis focuses on 0-F residuals for the 6.79pm AIRS moisture channel. At coincident locations, when POAM III detects ice clouds, the AIRS O-F residuals for this channel are lower than -2K. When no ice PSCs are evident in POAM III data, the AIRS 0-F residuals are larger. Given this relationship, the high spatial density of AIRS data is used to construct maps of regions where 0-F residuals are lower than -2K, as a proxy for ice PSCs. The spatial scales and spatio-temporal variations of these PSCs in the Antarctic and Arctic are discussed on the basis of these maps.

Stajner, Ivanka↗
NASA NTRS2001

The Cool Flames Experiment

A space-based experiment is currently under development to study diffusion-controlled, gas-phase, low temperature oxidation reactions, cool flames and auto-ignition in an unstirred, static reactor. At Earth's gravity (1g), natural convection due to self-heating during the course of slow reaction dominates diffusive transport and produces spatio-temporal variations in the thermal and thus species concentration profiles via the Arrhenius temperature dependence of the reaction rates. Natural convection is important in all terrestrial cool flame and auto-ignition studies, except for select low pressure, highly dilute (small temperature excess) studies in small vessels (i.e., small Rayleigh number). On Earth, natural convection occurs when the Rayleigh number (Ra) exceeds a critical value of approximately 600. Typical values of the Ra, associated with cool flames and auto-ignitions, range from 104-105 (or larger), a regime where both natural convection and conduction heat transport are important. When natural convection occurs, it alters the temperature, hydrodynamic, and species concentration fields, thus generating a multi-dimensional field that is extremely difficult, if not impossible, to be modeled analytically. This point has been emphasized recently by Kagan and co-workers who have shown that explosion limits can shift depending on the characteristic length scale associated with the natural convection. Moreover, natural convection in unstirred reactors is never "sufficiently strong to generate a spatially uniform temperature distribution throughout the reacting gas." Thus, an unstirred, nonisothermal reaction on Earth does not reduce to that generated in a mechanically, well-stirred system. Interestingly, however, thermal ignition theories and thermokinetic models neglect natural convection and assume a heat transfer correlation of the form: q=h(S/V)(T(bar) - Tw) where q is the heat loss per unit volume, h is the heat transfer coefficient, S/V is the surface to volume ratio, and (T(bar) - Tw ) is the spatially averaged temperature excess. This Newtonian form has been validated in spatially-uniform, well-stirred reactors, provided the effective heat transfer coefficient associated with the unsteady process is properly evaluated. Unfortunately, it is not a valid assumption for spatially-nonuniform temperature distributions induced by natural convection in unstirred reactors. "This is why the analysis of such a system is so difficult." Historically, the complexities associated with natural convection were perhaps recognized as early as 1938 when thermal ignition theory was first developed. In the 1955 text "Diffusion and Heat Exchange in Chemical Kinetics", Frank-Kamenetskii recognized that "the purely conductive theory can be applied at sufficiently low pressure and small dimensions of the vessel when the influence of natural convection can be disregarded." This was reiterated by Tyler in 1966 and further emphasized by Barnard and Harwood in 1974. Specifically, they state: "It is generally assumed that heat losses are purely conductive. While this may be valid for certain low pressure slow combustion regimes, it is unlikely to be true for the cool flame and ignition regimes." While this statement is true for terrestrial experiments, the purely conductive heat transport assumption is valid at microgravity (mu-g). Specifically, buoyant complexities are suppressed at mu-g and the reaction-diffusion structure associated with low temperature oxidation reactions, cool flames and auto-ignitions can be studied. Without natural convection, the system is simpler, does not require determination of the effective heat transfer coefficient, and is a testbed for analytic and numerical models that assume pure diffusive transport. In addition, mu-g experiments will provide baseline data that will improve our understanding of the effects of natural convection on Earth.

Pearlman, Howard↗
NASA NTRS2010

NASA Tech Briefs, February 2010

Topics covered include: Insulation-Testing Cryostat With Lifting Mechanism; Optical Testing of Retroreflectors for Cryogenic Applications; Measuring Cyclic Error in Laser Heterodyne Interferometers; Self-Referencing Hartmann Test for Large-Aperture Telescopes; Measuring a Fiber-Optic Delay Line Using a Mode-Locked Laser; Reconfigurable Hardware for Compressing Hyperspectral Image Data; Spatio-Temporal Equalizer for a Receiving-Antenna Feed Array; High-Speed Ring Bus; Nanoionics-Based Switches for Radio-Frequency Applications; Lunar Dust-Tolerant Electrical Connector; Compact, Reliable EEPROM Controller; Quad-Chip Double-Balanced Frequency Tripler; Ka-Band Waveguide Two-Way Hybrid Combiner for MMIC Amplifiers; Radiation-Hardened Solid-State Drive; Use of Nanofibers to Strengthen Hydrogels of Silica, Other Oxides, and Aerogels; Two Concepts for Deployable Trusses; Concentric Nested Toroidal Inflatable Structures; Investigating Dynamics of Eccentricity in Turbomachines; Improved Low-Temperature Performance of Li-Ion Cells Using New Electrolytes; Integrity Monitoring of Mercury Discharge Lamps; White-Light Phase-Conjugate Mirrors as Distortion Correctors; Biasable, Balanced, Fundamental Submillimeter Monolithic Membrane Mixer; ICER-3D Hyperspectral Image Compression Software; and Context Modeler for Wavelet Compression of Spectral Hyperspectral Images.

Source record↗
NASA NTRS2008

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors: Implementation, Analysis and Validation - Part 2

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the intercomparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.

Ganguly, Sangram↗
NASA NTRS2004

Evaluation and Application of Satellite-Based Latent Heating Profile Estimation Methods

In recent years, methods for estimating atmospheric latent heating vertical structure from both passive and active microwave remote sensing have matured to the point where quantitative evaluation of these methods is the next logical step. Two approaches for heating algorithm evaluation are proposed: First, application of heating algorithms to synthetic data, based upon cloud-resolving model simulations, can be used to test the internal consistency of heating estimates in the absence of systematic errors in physical assumptions. Second, comparisons of satellite-retrieved vertical heating structures to independent ground-based estimates, such as rawinsonde-derived analyses of heating, provide an additional test. The two approaches are complementary, since systematic errors in heating indicated by the second approach may be confirmed by the first. A passive microwave and combined passive/active microwave heating retrieval algorithm are evaluated using the described approaches. In general, the passive microwave algorithm heating profile estimates are subject to biases due to the limited vertical heating structure information contained in the passive microwave observations. These biases may be partly overcome by including more environment-specific a priori information into the algorithm s database of candidate solution profiles. The combined passive/active microwave algorithm utilizes the much higher-resolution vertical structure information provided by spaceborne radar data to produce less biased estimates; however, the global spatio-temporal sampling by spaceborne radar is limited. In the present study, the passive/active microwave algorithm is used to construct a more physically-consistent and environment-specific set of candidate solution profiles for the passive microwave algorithm and to help evaluate errors in the passive algorithm s heating estimates. Although satellite estimates of latent heating are based upon instantaneous, footprint- scale data, suppression of random errors requires averaging to at least half-degree resolution. Analysis of mesoscale and larger space-time scale phenomena based upon passive and passive/active microwave heating estimates from TRMM, SSMI, and AMSR data will be presented at the conference.

Olson, William S.↗
NASA NTRS1994

Directions of Moving Plaids is Biased by Asymmetric Viewing Windows

Directionally selective V1 neurons are tuned to particular spatio-temporal frequencies and respond to local 1-D edge motion. At least some MT neurons however appear to respond to the actual velocity of moving 2D patterns. To better understand how the 1D local motion information available from VI is integrated to derive a 2D velocity signal we investigated human perception of moving plaids, 2-D patterns composed of the sum of two 1-D sinusoidal gratings of different orientations. We measured the effect of the shape of the viewing window on the perceived direction of plaid motion. The plaids were spatially windowed by 2-D Gaussians with unequal standard deviations (sigma 1, sigma 2). Four observers indicated perceived direction by adjusting a pointer. Direction errors were measured as a function of the difference between window orientation and true plaid direction (DELTA THETA) for several grating spatial frequencies (SF = 0.3, 0.6, 1.2 c/d) and window aspect ratios (AR = sigma 1/sigma 2 = 1, 1.4, 2, 4). Observers showed systematic errors in perceived direction (approx. 15 for AR = 4 and SF = 0.6 c/d) that peaked at DELTA THETA approx. 40. The errors increased for increasing aspect ratio and decreased for increasing spatial frequencies (or number of cycles). These results show that despite the unambiguous motion of the plaids, under these conditions human misperceive the motion. These data constrain models of motion integration from V1 to MT and, in particular, are inconsistent with algorithms that use either the Intersection of Constraints rule or cross correlation to compute the perceived direction of motion.

Beutter, B. R.↗
DOE OSTI2025

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗
DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
NASA NTRS1992

Classification with spatio-temporal interpixel class dependency contexts

A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.

Jeon, Byeungwoo↗
NASA NTRS1994

Optimizing tertiary storage organization and access for spatio-temporal datasets

We address in this paper data management techniques for efficiently retrieving requested subsets of large datasets stored on mass storage devices. This problem represents a major bottleneck that can negate the benefits of fast networks, because the time to access a subset from a large dataset stored on a mass storage system is much greater that the time to transmit that subset over a network. This paper focuses on very large spatial and temporal datasets generated by simulation programs in the area of climate modeling, but the techniques developed can be applied to other applications that deal with large multidimensional datasets. The main requirement we have addressed is the efficient access of subsets of information contained within much larger datasets, for the purpose of analysis and interactive visualization. We have developed data partitioning techniques that partition datasets into 'clusters' based on analysis of data access patterns and storage device characteristics. The goal is to minimize the number of clusters read from mass storage systems when subsets are requested. We emphasize in this paper proposed enhancements to current storage server protocols to permit control over physical placement of data on storage devices. We also discuss in some detail the aspects of the interface between the application programs and the mass storage system, as well as a workbench to help scientists to design the best reorganization of a dataset for anticipated access patterns.

Chen, Ling Tony↗
NASA NTRS2006

Imaging Spectroscopy of Sunspots using IBIS

We use the Interferometric BIdimensional Spectrometer (IBIS) of the INAF/Arcetri Astrophysical Observatory and installed at the National Solar Observatory (NSO) Dunn Solar Telescope, to understand the structure of sunspots. These high resolution observations were acquired on 2004 July 30-31, of active region NOAA 10654, using the high order NSO adaptive optics system. We map the spatio-temporal variation of the penumbral Doppler signatures in three spectral lines, FeI 6301.5\AA, FeII 7224.4\AA, and CaII 8542.6\AA, from the photosphere to the chromosphere. From a 70-minute temporal average of individual 32-second cadence Doppler observations we find that the averaged velocities decrease with height, about 3.5 times larger in the deeper photosphere (FeII 7224.4\AA; height-of-formation $\approx$\50 km) than in the upper photosphere FeI 6301.5\AA; height-of-formation $\approx$\350 km), There is a remarkable coherence of Doppler signals over the height difference of 300 km. From a highspeed animation of the Doppler sequence we find evidence for what appears to be ejection of high speed gas concentrations from edges of penumbral filaments into the surrounding granular photosphere. The Evershed flow persists a few arcseconds beyond the traditionally demarcated penumbra-granulation boundary. We present these and other results and discuss the implications of these measurements for sunspot models

Balasubramaniam, K. S.↗
NASA NTRS2006

3-D Structure of Sunspots using Imaging Spectroscopy

We use the Interferometric BIdimensional Spectrometer (IBIS) of the INAF/Arcetri Astrophysical Observatory and installed at the National Solar Observatory (NSO) Dunn Solar Telescope, to understand the structure of sunspots. Using the spectral lines FeI 6301.5 A, FeII 7224.4 A, and CaII 8542.6 A, we examine the spectroscopic variation of sunspot penumbral and umbral structures at the heights of formation of these lines. These high resolution observations were acquired on 2004 July 30-31, of active region NOAA 10654, using the high order NSO adaptive optics system. We map the spatio-temporal variation of Doppler signatures in these spectral lines, from the photosphere to the chromosphere. From a 70-minute temporal average of individual 32-second cadence Doppler observations we find that the averaged velocities decrease with height, about 3.5 times larger in the deeper photosphere (FeII 7224.4 A; height-of-formation approx. 50 km) than in the upper photosphere FeI 6301.5 A; height-of-formation approx. 350 km), There is a remarkable coherence of Doppler signals over the height difference of 300 km. From a high-speed animation of the Doppler sequence we find evidence for what appears to be ejection of high speed gas concentrations from edges of penumbral filaments into the surrounding granular photosphere. The Evershed flow persists a few arcseconds beyond the traditionally demarcated penumbra-granulation boundary. We present these and other results and discuss the implications of these measurements for sunspot models.

Balasubramaniam, K. S.↗