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NASA NTRS2017

An Innovative Infrastructure with a Universal Geo-Spatiotemporal Data Representation Supporting Cost-Effective Integration of Diverse Earth Science Data

The SpatioTemporal Adaptive Resolution Encoding (STARE) is a unifying scheme encoding geospatial and temporal information for organizing data on scalable computing/storage resources, minimizing expensive data transfers. STARE provides a compact representation that turns set-logic functions into integer operations, e.g. conditional sub-setting, taking into account representative spatiotemporal resolutions of the data in the datasets. STARE geo-spatiotemporally aligns data placements of diverse data on massive parallel resources to maximize performance. Automating important scientific functions (e.g. regridding) and computational functions (e.g. data placement) allows scientists to focus on domain-specific questions instead of expending their efforts and expertise on data processing. With STARE-enabled automation, SciDB (Scientific Database) plus STARE provides a database interface, reducing costly data preparation, increasing the volume and variety of interoperable data, and easing result sharing. Using SciDB plus STARE as part of an integrated analysis infrastructure dramatically eases combining diametrically different datasets.

Rilee, Michael Lee↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗
NASA NTRS2002

The Science Behind the NASA/NOAA Electronic Theater 2002

Details of the science stories and scientific results behind the Etheater Earth Science Visualizations from the major remote sensing institutions around the country will be explained. The NASA Electronic Theater presents Earth science observations and visualizations in a historical perspective. Fly in from outer space to Temple Square and the University of Utah Campus. Go back to the early weather satellite images from the 1960s see them contrasted with the latest US/Europe/Japan global weather data. See the latest images and image sequences from NASA & NOAA missions like Terra, GOES, NOAA, TRMM, SeaWiFS, Landsat 7 visualized with state-of-the art tools. A similar retrospective of numerical weather models from the 1960s will be compared with the latest "year 2002" high-resolution models. See the inner workings of a powerful hurricane as it is sliced and dissected using the University of Wisconsin Vis-5D interactive visualization system. The largest super computers are now capable of realistic modeling of the global oceans. See ocean vortexes and currents that bring up the nutrients to feed phitoplankton and zooplankton as well as draw the crill fish, whales and fisherman. See the how the ocean blooms in response to these currents and El Nino/La Nina climate regimes. The Internet and networks have appeared while computers and visualizations have vastly improved over the last 40 years. These advances make it possible to present the broad scope and detailed structure of the huge new observed and simulated datasets in a compelling and instructive manner. New visualization tools allow us to interactively roam & zoom through massive global images larger than 40,000 x 20,000 pixels. Powerful movie players allow us to interactively roam, zoom & loop through 4000 x 4000 pixel bigger than HDTV movies of up to 5000 frames. New 3D tools allow highly interactive manipulation of detailed perspective views of many changing model quantities. See the 1m resolution before and after shots of lower Manhattan and the Pentagon after the September 11 disaster as well as shots of Afghanistan from the Space Imaging IKONOS as well as debris plume images from Terra MODIS and SPOT Image. Shown by the SGI-Octane Graphics-Supercomputer are visualizations of hurricanes Michelle 2001, Floyd, Mitch, Fran and Linda. Our visualizations of these storms have been featured on the covers of the National Geographic, Time, Newsweek and Popular Science. Highlights will be shown from the NASA's large collection of High Definition TV (HDTV) visualizations clips New visualizations of a Los Alamos global ocean model, and high-resolution results of a NASA/JPL Atlantic ocean basin model showing currents, and salinity features will be shown. El Nino/La Nina effects on sea surface temperature and sea surface height of the Pacific Ocean will also be shown. The SST simulations will be compared with GOES Gulf Stream animations and ocean productivity observations. Tours will be given of the entire Earth's land surface at 500 m resolution from recently composited Terra MODIS data, Visualizations will be shown from the Earth Science Etheater 2001 recently presented over the last years in New Zealand, Johannesburg, Tokyo, Paris, Munich, Sydney, Melbourne, Honolulu, Washington, New York City, Pasadena, UCAR/Boulder, and Penn State University. The presentation will use a 2-CPU SGI/CRAY Octane Super Graphics workstation with 4 GB RAM and terabyte disk array at 2048 x 768 resolution plus multimedia laptop with three high resolution projectors. Visualizations will also be featured from museum exhibits and presentations including: the Smithsonian Air & Space Museum in Washington, IMAX theater at the Maryland Science Center in Baltimore, the James Lovell Discovery World Science museum in Milwaukee, the American Museum of Natural History (NYC) Hayden Planetarium IMAX theater, etc. The Etheater is sponsored by NASA, NOAA and the American Meteorological Society. This presentation is brought to you by the University of Utah College of Mines and Earth Sciences and, the Utah Museum of Natural History.

Hasler, A. Fritz↗
NASA NTRS2015

A Coordinated X-Ray and Optical Campaign of the Nearby Massive Binary Sigma Orionis Aa: X-Ray Variability - II

We present time-resolved and phase-resolved variability studies of an extensive X-ray high-resolution gratings spectral dataset of the Sigma Ori Aa binary system. The four observations, obtained with Chandra ACIS HETGS, have a total exposure time of approximately 479 kiloseconds and provide nearly complete binary phase coverage. Variability of the total X-ray flux in the range 5-25 angstroms is confirmed, with maximum amplitude of about plus or minus 15 percent within a single approximately 125 kiloseconds observation. Periods of 4.76 days and 2.04 days are found in the total X-ray flux, as well as an apparent overall increase in flux level throughout the 9-day observational campaign. Using 40 kiloseconds contiguous spectra derived from the original observations, we investigate variability of emission line parameters and ratios. Several emission lines are shown to be variable, including S XV, Si XIII, and Ne IX. For the first time, variations of the X-ray emission line widths as a function of the binary phase are found in a binary system, with the smallest widths at phi equals 0.0 when the secondary Aa2 is at inferior conjunction. We use the results of an SPH radiative transfer code model, customized for this project, to relate the presence of a low density cavity in the primary stellar wind embedded shock that is associated with the secondary star to the emission line width variability.

binaries: closeaEuro"binaries: eclipsingaEuro"star↗
NASA NTRS2017

Evidence for Reduced Species Star Formation Rates in the Centers of Massive Galaxies at zeta = 4

We perform the first spatially-resolved stellar population study of galaxies in the early universe z equals 3.5 -6.5, utilizing the Hubble Space Telescope Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) imaging dataset over the GOODS-S field. We select a sample of 418 bright and extended galaxies at z less than or approximately equal to 3.5-6.5 from a parent sample of approximately 8000 photometric-redshift selected galaxies from Finkelstein et al. We first examine galaxies at 3.5 less than or equal to z less than or approximately equal to 4.0 using additional deep K-band survey data from the HAWK-I UDS and GOODS Survey (HUGS) which covers the 4000 Angstrom break at these redshifts. We measure the stellar mass, star formation rate, and dust extinction for galaxy inner and outer regions via spatially-resolved spectral energy distribution fitting based on a Markov Chain Monte Carlo algorithm. By comparing specific star formation rates (sSFRs) between inner and outer parts of the galaxies we find that the majority of galaxies with the high central mass densities show evidence for a preferentially lower sSFR in their centers than in their outer regions, indicative of reduced sSFRs in their central regions. We also study galaxies at z approximately equal to 5 and 6 (here limited to high spatial resolution in the rest-frame ultraviolet only), finding that they show sSFRs which are generally independent of radial distance from the center of the galaxies. This indicates that stars are formed uniformly at all radii in massive galaxies at z approximately equal to 5-6, contrary tomassive galaxies at z. less than approximately equal to 4.

early universe – galaxies: bulges – galaxies: ↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗
NASA NTRS2013

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

Temporal Queries↗
NASA NTRSDate not supplied

Mercury Lander: A Planetary Mission Concept Study for the 2023–2032 Decadal Survey

Mercury holds unique clues to the distribution of elements at the birth of the solar system and how planets form and evolve in close proximity to their host stars. The Mercury Lander mission concept returns in situ measurements to: understand Mercury’s unique mineralogy and geochemistry; characterize the massive core’s structure; measure the planet’s active and ancient magnetic fields at the surface; investigate the processes that alter the surface and produce the exosphere; and provide groundtruth for remote datasets. The mission concept achieves one Mercury year (~88 Earth days) of surface operations with an 11-instrument, high-heritage payload delivered to a landing site within Mercury’s widely distributed low-reflectance material, and addresses science goals encompassing geochemistry, geophysics, the Mercury space environment, and geology. The spacecraft launches on an expendable SpaceX Falcon Heavy in 2035. The four-stage flight system uses a solar-electric propulsion cruise stage to reach Mercury in2045. The orbital stage brings the spacecraft into a thermally safe orbit, then performs orbital maneuvers to prepare for descent. During the orbital phase, a narrow-angle camera acquires images for selecting a low-hazard landing zone within our region of interest. The descent stage begins the braking burn ~120 s before landing. The lander continues to touchdown, using continuous LIDAR operations to support hazard detection and safely deliver the payload to the surface. Landing is at dusk to meet thermal requirements, permitting ~30 hours of sunlight for initial observations. The radioisotope-powered lander continues operations through the Mercury night. Direct-to-Earth communication is possible for the initial three weeks of landed operations, drops out for six weeks, and resumes for the final month. Thermal conditions exceed lander operating temperatures shortly after sunrise, ending operations. A total of ~11 GB of data are returned to Earth. The Phase A–D mission cost estimate (50% unencumbered reserves, excluding launch vehicle) is $1.2 B(FY25$), comparing favorably with past New Frontiers missions and to the cost cap in the New Frontiers4 call(~$1.1B FY25$). This cost estimate shows that a Mercury Lander mission is feasible and compelling as a New Frontiers-class mission in the coming decade.

C M Ernst↗
NASA NTRS2022

Understanding Structure in Line-Driven Stellar Winds Using Ultraviolet Spectropolarimetry in the Time Domain

The most massive stars are thought to lose a significant fraction of their mass in a steady wind during the main-sequence and blue supergiant phases. This in turn sets the stage for their further evolution and eventual supernova, and preconditions the surrounding medium for all following events, with consequences for ISM energization, chemical enrichment, and dust formation. Understanding these processes requires accurate observational constraints on the mass-loss rates of the most luminous stars, which can also be used to test theories of stellar wind driving. In the past, mass-loss rates have been characterized via collisional emission processes such as optical Hα and free-free radio emission, but these so-called “density squared” diagnostics require correction in the presence of widespread clumping. Recent observational and theoretical evidence points to the likelihood of a ubiquitously high level of such clumping in hot-star winds, but quantifying its effects requires a deeper understanding of the complex dynamics of radiatively driven winds and their stochastic instabilities. Furthermore, large-scale structures initiating in surface anisotropies and propagating throughout the wind can also affect wind driving and alter mass-loss diagnostics. Time series spectroscopy of high resonance-line opacity in the UV, capable of high resolution and high signal-to-noise, are required to better understand these complex dynamics, and more accurately determine mass-loss rates. The proposed Polstar mission (Scowen et al. 2022, this volume) provides the necessary resolution at the Sobolev (∼10 km s −1 ) or sound-speed (∼20 km s −1 ) scale, for over three dozen bright galactic massive stars with signal-to noise an order of magnitude above that of the celebrated MEGA campaign (Massa et al. 1995) of the International Ultraviolet Explorer (IUE), via continuous observations that track propagating structures through the winds in real time. Supporting geometric constraints are provided by the polarimetric capabilities present in all the datasets of such a mission.

Polstar – NASA MIDEX↗
NASA NTRS2021

The Mercury Lander Mission Concept Study: Enabling Transformative Science from the Surface of the Inner-Most Planet

As an end-member of rockyplanet formation, Mercury holds unique clues about the original distribution of elements in the earliest stages of Solar System development,as well as how planets form and evolve in close proximity to their host starsgenerally. This Mercury Lander mission concept enables in situ surface measurements that address several fundamental science questions raised by MESSENGER’s pioneering exploration of Mercury. Such measurements are needed to understand Mercury’s unique mineralogy and geo-chemistry; to characterize the structure of the planet’s proportionally massive core’s structure; to measure the planet’s active and ancient magnetic fields at the surface; to investigate the processes that alter the surface and produce the exosphere; and to provide groundtruth for current and future remote datasets

C M Ernst↗
NASA NTRS2020

Mercury Lander: Transformative Science from the Surface of the Innermost Planet

As an end-member of terrestrial planet formation, Mercury holds unique clues about the original distribution of elements in the earliest stages of solar system development and how planets and exoplanets form and evolve in close proximity to their host stars. This Mercury Lander mission concept enables in situ surface measurements that address several fundamental science questions raised by MESSENGER’s pioneering exploration of Mercury. Such measurements are needed to understand Mercury’s unique mineralogy and geochemistry; to characterize the proportionally massive core’s structure; to measure the planet’s active and ancient magnetic fields at the surface; to investigate the processes that alter the surface and produce the exosphere; and to provide ground truth for current and future remote datasets. NASA’s Planetary Mission Concept Studies (PMCS) program awarded this study to a multidisciplinary team led by Dr. Carolyn Ernst of the Johns Hopkins Applied Physics Laboratory (APL), to evaluate the feasibility of accomplishing transformative science through a New-Frontiers-class, landed mission to Mercury in the next decade. The resulting mission concept achieves one full Mercury year (~88 Earth days) of surface operations with an ambitious, high-heritage, landed science payload, corresponding well with the New Frontiers mission framework.

Mercury lander↗
NASA NTRS1998

Inferences About the Early Moon From Gravity and Topography

Recent spacecraft missions to the Moon have significantly improved our knowledge of the lunar gravity and topography fields, and have raised some new and old questions about the early lunar history. It has frequently been assumed that the shape of the Moon today reflects an earlier equilibrium state and that the Moon has retained some internal strength. Recent analysis indicating a superisostatic state of some lunar basins lends support to this hypothesis. On its simplest level, the present shape of the Moon is slightly flattened by 2.2 +/- 0.2 km while its gravity field, represented by an equipotential surface, is flattened only about 0.5 km. The hydrostatic component to the flattening arising from the Moon's present day rotation contributes only 7 m. This difference between the topographic shape of the MOon and the shape of its gravitational equipotential has frequently been explained as the "memory" of an earlier moon that was rotating faster and had a correspondingly larger hydrostatic flattening. To obtain this amount of hydrostatic flattening from rotation alone, and accounting for the contribution of the present-day gravity field, the Moon's rotation rate would need to be about 15x greater than at present, leading ot a period of < 2 days. Maintaining its synchronous rotation with Earth would require a radius for the Moon's orbit of approximately 9 Earth Radii. Unfortunately, our confidence in the observed lunar flattening is not as great as we would like. The uncertainty of .02 km may not properly reflect the limitations of the Clementine dataset, which did not sample poleward of latitudes 81 N and 79 S. Also, the large variation of topography +/- 8 km seen on the MOon dwarfs our estimate fo the flattening. Further the lunar south pole is on the edge of, or possibly inside the massive deep, South Pole-Aitken Basin. Thus, polar radii could be underestimated. This would yield a smaller flattening, which would imply a greater lunar rotation period and orbital radius. However, Basin compensation states and analyses of support and relaxation of topography at long wavelengths point to a lunar shape that has retained a flattening from an earlier faster rotation period.

Smith, D. E.↗
NASA NTRS1992

UIT support observations archive

We are in the process of archiving the approximately 1.2Tbytes of imaging data acquired in support of UIT observations. The UIT is one of three telescopes comprising the ASTRO spacecraft and is a 38-cm f/9 Ritchey-Chretien telescope with wavelength coverage 1200 A to 3000 A and a 40 arcminute diameter field-of-view at 4 min FWHM resolution. During the ASTRO shuttle mission, there were 65 different pointings (some with multiple targets) of the UIT and hence 65 fields. Our support data of these fields were obtained with the KPNO and CTIO 0.9m telescopes and several of the 2048x2048 CCD's. Our images are typically about 20 acrminutes on a side and contain the entire UV target except for nearby galaxies, for which mosaiced images were created. All good quality images in the broadband and narrow band filters for every target are being archived. Though all of the UIT targets were well studied astronomical objects, these frames are many of the first large field format images of them and, when combined with the soon to be released UV frames, provide a unique dataset. These data were already used to address a wide variety of astronomical questions. Vacuum ultraviolet (VUV) observations were used to study star formation in a sample of nearby galaxies, since integrated VUV - optical colors provide the most sensitive available measure of the formation rate of massive stars. The blue stages later in stellar evolution are also being studied. These data allow more accurate determination of the helium and metallicities of hothorzontal branch stars. These data are being prepared to release to the public in CD-ROM format through the National Space Science Data Center.

Smith, Eric P.↗
NASA NTRS1993

Value added data archiving

Researchers in the Molecular Sciences Research Center (MSRC) of Pacific Northwest Laboratory (PNL) currently generate massive amounts of scientific data. The amount of data that will need to be managed by the turn of the century is expected to increase significantly. Automated tools that support the management, maintenance, and sharing of this data are minimal. Researchers typically manage their own data by physically moving datasets to and from long term storage devices and recording a dataset's historical information in a laboratory notebook. Even though it is not the most efficient use of resources, researchers have tolerated the process. The solution to this problem will evolve over the next three years in three phases. PNL plans to add sophistication to existing multilevel file system (MLFS) software by integrating it with an object database management system (ODBMS). The first phase in the evolution is currently underway. A prototype system of limited scale is being used to gather information that will feed into the next two phases. This paper describes the prototype system, identifies the successes and problems/complications experienced to date, and outlines PNL's long term goals and objectives in providing a permanent solution.

Berard, Peter R.↗
NASA NTRS2003

Carbonate Platform Development and Stromatolite Morphogenesis: Constraints on Environmental and Biological Evolution

Work has been completed on the digital mapping of a terminal Proterozoic reef complex in Namibia. This complex formed an isolated carbonate platform developed downdip on a carbonate ramp of the Nama Group. The stratigraphic evolution of the platform was digitally reconstructed from an extensive dataset that was compiled by using digital surveying technologies. The platform comprises three accommodation cycles in which each subsequent cycle experienced progressively greater influence of a long-term accommodation increase. Aggradation and progradation during the first cycle resulted in a flat, uniform, sheet-like platform. The coarsening and shallowing-upward sequence representing the first cycle is dominated by columnar stromatolitic thrombolites and massive dolostones with interbedded mudstone-grainstone at the base of the sequence grading into cross-bedded dolostones. The second cycle features aggradation, formation of a distinct margin containing thrombolite mounds and domes, and the development of a bucket geometry. Columnar stromatolitic thrombolites dominate the platform interior. The final stage of platform development shows a deepening trend with initial aggradation and formation of well-bedded, thin deposits in the interior and mound development at the margins. While the interior drowned, the platform margin kept up with rising sea level and a complex pinnacle reef formed containing fused and coalesced thrombolite mounds flanked by bioclastic grainstones (containing Cloudina and Namacalathus fossils) and collapse breccias. A set of isolated large thrombolite mounds flanked by shales indicate the final stage of the carbonate platform. During a progressive increase in accommodation, a flat-topped isolated carbonate platform becomes aerially less extensive by either backstepping or formation of smaller pinnacles or a combination of both. The overall geometric evolution of the studied platform from flat-topped to bucket with elevated margins is recorded in many Proterozoic and Phanerozoic isolated carbonate platforms with similar dimensions. The terminal Proterozoic, microbial-dominated, isolated carbonate platform of this study clearly illustrates that the answer to accommodation changes was already familiar among carbonate platforms before the dawn of metazoan-dominated platforms.

Grotzinger, John P.↗
NASA NTRS2010

Fast Multivariate Search on Large Aviation Datasets

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

Bhaduri, Kanishka↗
NASA NTRS2019

How Well Does NASA GEOS Model Perform in Simulating Dust Deposition into the Tropical Atlantic Ocean?

Massive dust emitted from North Africa can transport long distances across the tropical Atlantic Ocean, reaching the Americas. Dust deposition along the transit adds microorganisms and essential nutrients to marine ecosystem, which has important implications for biogeochemical cycle and climate. However, assessing the dust-ecosystemclimate interactions has been hindered in part by the paucity of dust deposition measurements and large uncertainties associated with oversimplified representations of dust processes in current models. We have recently produced a unique dataset of seasonal dust deposition flux and dust loss frequency into the tropical Atlantic Ocean at a nominal resolution of 200 km x 500 km by using the decade-long (2007-2016) record of aerosol three-dimensional distribution from four satellite sensors, namely CALIOP, MODIS, MISR, and IASI. On the basis of the ten-year average, the yearly dust deposition into the tropical Atlantic Ocean is estimated at 98-153 Tg. The dust deposition shows large spatial and temporal (on seasonal and interannual scale) variability. The satellite observations also yield an estimate of annual mean dust loss frequency of 0.052 ~ 0.078 d-1, a useful diagnostic that makes it possible to disentangle the dust transport and removal processes from the dust emissions when identifying the major factors contributing to the uncertainties and biases in the model simulated dust deposition. In this study, we use the dataset along with in situ and remote sensing observations to assess how well NASA GEOS model performs in simulating trans-Atlantic dust transport and deposition. We found that the GEOS modeling of dust deposition falls within the range of satellite-based estimates. However, this reasonable agreement in dust deposition is a compensation of the model's underestimate of dust emissions and overestimate of dust removal efficiency. Further, the overestimate of dust removal efficiency results largely from the model's overestimate of rainfall rate. Our results provide insights into the model's deficiencies at process level, which could better guide model improvements.

Yu, Hongbin↗