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NASA NTRSDate not supplied

Inferred Sea Level Prediction in the NASA GMAO Seasonal Forecasting System

Reliable predictions of sea level anomalies on seasonal timescales with lead times of 1 to 9 months may have relevance to stakeholders – for example, in the advance deployment of resources for coastal flood mitigation. Routine prediction and analysis may also highlight physical processes associated with sea level change and modeling capabilities on seasonal and other timescales. These forecasts may represent interannual changes in the seasonal slope of the ocean surface, teleconnection effects such as the El Niño/Southern Oscillation phenomenon, and variations in seasonal hydrology including precipitation and coastal runoff. Coupled atmosphere/ocean models are routinely used in the seasonal prediction of temperature anomalies, precipitation anomalies, sea ice cover, and climate indices such as the Niño3.4 predictions under the North American Multi-Model Ensemble (NMME) protocol. Within the limits of their configuration, these complex Earth-system models have a potential for depicting regional changes in oceanic column properties, including the sea surface height. Seasonal prediction models generally have no representation of long-term mass contributions from melting land ice, or changes in vertical land motion; their output may be more specifically characterized as predictions of the ocean dynamic sea level. In practice however, the sea surface height prognostic variable is substantially compromised by the forecast model response to initial conditions. Imbalances between the initial, observed hydrologic cycle and the forecast model state produce abrupt adjustments in the model sea surface height. As a result, most seasonal prediction systems employ a constraint on the globally-averaged sea surface height that is applied at each time step. This essentially renders the prognostic sea surface height variable as unserviceable. Several approaches have previously been used to retrieve sea level information from seasonal forecasts beyond the use of the sea surface height variable. Here, we extend a method of relating other prognostic values, including ocean circulation and climate indices, to observed sea level variations. We use the merged altimetry record of the NASA MEaSUREs Gridded Sea Surface Height Anomalies data set and monthly revised local reference gauge observations from the National Oceanography Centre Permanent Service for Mean Sea Level (PSMSL) to evaluate derived prognostic variables from the NASA Global Modeling and Assimilation Office subseasonal-to-seasonal system version 2.1 (GMAO S2S v2.1). We focus on results for the midlatitudes with particular emphasis on US gauge locations. As shown in previous studies, prognostic ENSO-related indices in boreal winter are well correlated with gauge observations for the US west coast, but also for other locations in the southeastern US. Other forecast climate indices such as the North Atlantic Oscillation have relations to sea level that are limited both seasonally and spatially. As expected, surface atmospheric pressure (e.g., inverse barometer effect) is found to be particularly well correlated with observed sea level. We provide a characterization of forecast skill for seasonal sea level with this method.

Richard I Cullather↗

NASA NTRS1992

Remote Sensing Data from CLARET: A Prototype Cart Data Set

A data set containing radiation, meteorological, and cloud sensor observations is documented. It was prepared for use by the Department of Energy's Atmospheric Radiation Measurement (ARM) program and other interested scientists. These data are a precursor of the types of data that ARM Cloud And Radiation Testbed (CART) sites will provide. The data are from the Cloud Lidar And Radar Exploratory Test (CLARET) conducted by the Wave Propagation Laboratory during autumn 1989 in the Denver-Boulder area of Colorado primarily for the purpose of developing new cloud-sensing techniques on cirrus. After becoming aware of this experiment, ARM scientists requested archival of subsets or the data to assist in the developing ARM program. Five CLARET cases were selected: two with cirrus, one with stratus, one with mixed-phase clouds, and one with clear skies. The cases range from 2 to 9.5 h in length. A pyranometer, pyrgeometer, pyrheliometer, and an infrared radiometer constituted the ensemble of instruments that provided surface radiation data. A lidar, radar, and ceilometer observed the cloud geometrical structure, and visual reports and all-sky camera observations were assimilated to provide cloud cover data. Radiosondes, wind profiler, RASS (profiling virtual temperature), microwave radiometers (observing column integrated liquid water and water vapor), and standard surface measurements provided meteorological data. Satellite data from the stratus case and one cirrus case were analyzed for statistics on cloud cover and top height. The main body of the selected data are available on diskette from the Wave Propagation Laboratory or Los Alamos National Laboratory. In addition to documenting the data set, this report describes CLARET and gives a bibliography of publications associated with the project. Some preliminary results of CLARET' research are also summarized. Simultaneous CO 2 lidar and radar backscatter measurements were shown to provide estimates of the effective radius of ice particles. Simultaneous radar and infrared radiometer data appear useful for estimating column-integrated numbers and average sizes of ice cloud particles. Ice water content obtained with this method compared favorably with values from another empirical technique using radar data alone. Depolarization of the CO 2 lidar signal from ice clouds was surprisingly small, suggesting that calculation of backscatter from nonspherical particles for this lidar is a tractable problem. Examples are also cited of CO 2 lidar measurements of the effective radius of water cloud drop size distributions and of inference of the size of pristine ice crystals that assume a particular orientation in the air. These parameters are all important to radiative transfer through clouds.

Clouds (Meteorology)↗

NASA NTRS2005

More on Magnetic Spectra from Correlated Crustal Sources on Mars

The spectral method for distinguishing crustal from core-source magnetic fields has been re-examined, modified and applied to both a comprehensive geomagnetic field model and an altitude normalized magnetic map of Mars. These observational spectra are fairly fitted by theoretical forms expected from certain elementary classes of magnetic sources. For Earth we found fields from a core of radius 3512 plus or minus 64 km, in accord with the 3480 km seismologic radius, and a crust represented by a shell of random dipolar sources at radius 6367 plus or minus 14 km, just beneath the 6371.0 km mean radius. For Mars we found only a field from a crust represented in same way, but 46 plus or minus 10 km below the planetary mean radius of 3389.5 km, and with sources about 9.6 plus or minus 3.2 times stronger than Earth's. It is remarkable that the same simple theoretical form should fairly fit crustal magnetic spectra for both worlds and return crustal-source depth estimates of plausible magnitude. Evidently, the idea of an ensemble of compact, quasi-independent, magnetized regions within these planetary crusts has some merit. Yet such estimates, at best a kind of average, depend upon both the observational spectrum fitted and the physical basis of the theoretical spectrum.

Voorhies, C. V.↗

NASA NTRS2004

Crustal Magnetic Spectra from Correlated Sources on Mars

The spectral method for distinguishing crustal from core-source magnetic fields has been re-examined, modified and applied to both a comprehensive geomagnetic field model and an altitude normalized magnetic map of Mars. These observational spectra are fairly fitted by theoretical forms expected from certain elementary classes of magnetic sources. For Earth we found fields from a core radius 3512 +/- 64 km and a crust represented by a shell of random dipolar sources at radius 6367 +/- 14 km. For Mars we found only a field from a crust represented in the same way, but 46 +/- 10 km below the planetary menu radius. More realistic theoretical spectra, allowing for crustal thickness, oblateness and magnetization by a planet centered dipole, were derived and discussed, as were spectral effects of laterally correlated sources. The main effect of laterally correlated sources is to soften the spectrum at high degrees. We tend to over-estimate source shell depth when this is omitted. To include this effect simply size and magnetization distribution functions for extended sources are recast as a characteristic diameter and mean square magnetization amplitude for an ensemble of vertically magnetized spherical caps on a shell. For small caps, and at moderated degrees, the practical derivatives of the log-theoretical spectrum with respect to amplitude, shell radius, and cap diameter are approximately proportional to l, n, and -n **2, respectively. Separation of diameter from amplitude from layer thickness. Results from applications to observational spectra are discussed, noting that there are now several fine field models for Mars; moreover, the terrestrial magnetic spectrum at high degrees, as revised, updated with high precision Oerstad data, and upgrade with high resolution Champ data, appears softer than before.

Voorhies, Coerte V.↗

NASA NTRS2013

NASA Tech Briefs, June 2013

Topics include: Cloud Absorption Radiometer Autonomous Navigation System - CANS, Software Method for Computed Tomography Cylinder Data Unwrapping, Re-slicing, and Analysis, Discrete Data Qualification System and Method Comprising Noise Series Fault Detection, Simple Laser Communications Terminal for Downlink from Earth Orbit at Rates Exceeding 10 Gb/s, Application Program Interface for the Orion Aerodynamics Database, Hyperspectral Imager-Tracker, Web Application Software for Ground Operations Planning Database (GOPDb) Management, Software Defined Radio with Parallelized Software Architecture, Compact Radar Transceiver with Included Calibration, Software Defined Radio with Parallelized Software Architecture, Phase Change Material Thermal Power Generator, The Thermal Hogan - A Means of Surviving the Lunar Night, Micromachined Active Magnetic Regenerator for Low-Temperature Magnetic Coolers, Nano-Ceramic Coated Plastics, Preparation of a Bimetal Using Mechanical Alloying for Environmental or Industrial Use, Phase Change Material for Temperature Control of Imager or Sounder on GOES Type Satellites in GEO, Dual-Compartment Inflatable Suitlock, Modular Connector Keying Concept, Genesis Ultrapure Water Megasonic Wafer Spin Cleaner, Piezoelectrically Initiated Pyrotechnic Igniter, Folding Elastic Thermal Surface - FETS, Multi-Pass Quadrupole Mass Analyzer, Lunar Sulfur Capture System, Environmental Qualification of a Single-Crystal Silicon Mirror for Spaceflight Use, Planar Superconducting Millimeter-Wave/Terahertz Channelizing Filter, Qualification of UHF Antenna for Extreme Martian Thermal Environments, Ensemble Eclipse: A Process for Prefab Development Environment for the Ensemble Project, ISS Live!, Space Operations Learning Center (SOLC) iPhone/iPad Application, Software to Compare NPP HDF5 Data Files, Planetary Data Systems (PDS) Imaging Node Atlas II, Automatic Calibration of an Airborne Imaging System to an Inertial Navigation Unit, Translating MAPGEN to ASPEN for MER, Support Routines for In Situ Image Processing, and Semi-Supervised Eigenbasis Novelty Detection.

Source record↗

NASA NTRS2011

A Generalised Porosity Formalism for Isotropic and Anisotropic Effective Opacity and its Effects on X-ray Line Attenuation in Clumped O Star Winds

We present a generalised formalism for treating the porosity-associated reduction in continuum opacity that occurs when individual clumps in a stochastic medium become optically thick. As in previous work, we concentrate on developing bridging laws between the limits of optically thin and thick clumps. We consider geometries resulting in either isotropic or anisotropic effective opacity, and, in addition to an idealised model in which all clumps have the same local overdensity and scale, we also treat an ensemble of clumps with optical depths set by Markovian statistics. This formalism is then applied to the specific case of bound-free absorption of X- rays in hot star winds, a process not directly affected by clumping in the optically thin limit. We find that the Markov model gives surprisingly similar results to those found previously for the single clump model, suggesting that porous opacity is not very sensitive to details of the assumed clump distribution function. Further, an anisotropic effective opacity favours escape of X-rays emitted in the tangential direction (the venetian blind effect), resulting in a bump of higher flux close to line centre as compared to profiles computed from isotropic porosity models. We demonstrate how this characteristic line shape may be used to diagnose the clump geometry, and we confirm previous results that for optically thick clumping to significantly influence X-ray line profiles, very large porosity lengths, defined as the mean free path between clumps, are required. Moreover, we present the first X-ray line profiles computed directly from line-driven instability simulations using a 3-D patch method, and find that porosity effects from such models also are very small. This further supports the view that porosity has, at most, a marginal effect on X-ray line diagnostics in O stars, and therefore that these diagnostics do indeed provide a good clumping insensitive method for deriving O star mass-loss rates.

Sundqvist, Jon O.↗

NASA NTRS2002

A Generalised Porosity Formalism for Isotropic and Anisotropic Effective Opacity and Its Effects on X-ray Line Attenuation in Clumped O Star Winds

generalised↗

NASA NTRS1997

Evaluation of Tropospheric Water Vapor Simulations from the Atmospheric Model Intercomparison Project

Simulations of humidity from 28 general circulation models for the period 1979-88 from the Atmospheric Model Intercomparison Project are compared with observations from radiosondes over North America and the globe and with satellite microwave observations over the Pacific basin. The simulations of decadal mean values of precipitable water (W) integrated over each of these regions tend to be less moist than the real atmosphere in all three cases; the median model values are approximately 5% less than the observed values. The spread among the simulations is larger over regions of high terrain, which suggests that differences in methods of resolving topographic features are important. The mean elevation of the North American continent is substantially higher in the models than is observed, which may contribute to the overall dry bias of the models over that area. The authors do not find a clear association between the mean topography of a model and its mean W simulation, however, which suggests that the bias over land is not purely a matter of orography. The seasonal cycle of W is reasonably well simulated by the models, although over North America they have a tendency to become moister more quickly in the spring than is observed. The interannual component of the variability of W is not well captured by the models over North America. Globally, the simulated W values show a signal correlated with the Southern Oscillation index but the observations do not. This discrepancy may be related to deficiencies in the radiosonde network, which does not sample the tropical ocean regions well. Overall, the interannual variability of W, as well as its climatology and mean seasonal cycle, are better described by the median of the 28 simulations than by individual members of the ensemble. Tests to learn whether simulated precipitable water, evaporation, and precipitation values may be related to aspects of model formulation yield few clear signals, although the authors find, for example, a tendency for the few models that predict boundary layer depth to have large values of evaporation and precipitation. Controlled experiments, in which aspects of model architecture are systematically varied within individual models, may be necessary to elucidate whether and how model characteristics influence simulations.

Gaffen, Dian J.↗

NASA NTRS1995

A new method for determining the CO to H2 conversion factor for translucent clouds

In this paper we describe a new technique for obtaining the conversion factor between the molecular hydrogen column density and the CO(J = 1-0) integrated antenna temperature. This factor, typically known as X(sub CO) is often to be of order a few times 10(exp 20)/sq cm/K km/s) for the molecular clouds in the Galaxy and is one of the primary means of determining the molecular cloud mass from CO observations. However, for the low-extinction interstellar clouds known as the translucent molecular clouds, estimates of X(sub CO) vary by up to a factor of 60 depending on the object and techniques employed to calibrate X(sub CO). Since the cloud mass is directly proportional to X(sub CO) uncertainties in mass estimates of translucent clouds can be more than an order of magnitude. We calibrate the H2 content in translucent clouds by using the linear relationship between the CH and H2 column densities. The CH column density is readily determined from observations of the CH ground-state hyperfine main-line transition at 3335 MHz. Using CH as a surrogate tracer for H2 and CO(J = 1-0) observations of a sample of translucent and dark molecular clouds, we find a wide variation in values for X(sub CO). For translucent clouds, X(sub CO) ranges from 0.3 to 6.8 x 10(exp 20) and for dark clouds the values range from 0.8 to 8.6. Although the average values for both types of cloud are similar to the canonical value determined for the Galactic molecular cloud ensemble (2-4 x 10(exp 20)), the scatter in individual X(sub CO) values may indicate that X(sub CO) for a given translucent cloud cannot be determined a priori and must be obtained for each cloud so that a reliable mass determination may be made.

Magnani, L.↗

NASA NTRS2007

Automated Knowledge Discovery From Simulators

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Burl, Michael↗

NASA NTRS2006

Leveraging Existing Mission Tools in a Re-Usable, Component-Based Software Environment

Emerging methods in component-based software development offer significant advantages but may seem incompatible with existing mission operations applications. In this paper we relate our positive experiences integrating existing mission applications into component-based tools we are delivering to three missions. In most operations environments, a number of software applications have been integrated together to form the mission operations software. In contrast, with component-based software development chunks of related functionality and data structures, referred to as components, can be individually delivered, integrated and re-used. With the advent of powerful tools for managing component-based development, complex software systems can potentially see significant benefits in ease of integration, testability and reusability from these techniques. These benefits motivate us to ask how component-based development techniques can be relevant in a mission operations environment, where there is significant investment in software tools that are not component-based and may not be written in languages for which component-based tools even exist. Trusted and complex software tools for sequencing, validation, navigation, and other vital functions cannot simply be re-written or abandoned in order to gain the advantages offered by emerging component-based software techniques. Thus some middle ground must be found. We have faced exactly this issue, and have found several solutions. Ensemble is an open platform for development, integration, and deployment of mission operations software that we are developing. Ensemble itself is an extension of an open source, component-based software development platform called Eclipse. Due to the advantages of component-based development, we have been able to vary rapidly develop mission operations tools for three surface missions by mixing and matching from a common set of mission operation components. We have also had to determine how to integrate existing mission applications for sequence development, sequence validation, and high level activity planning, and other functions into a component-based environment. For each of these, we used a somewhat different technique based upon the structure and usage of the existing application.

Greene, Kevin↗

NASA NTRSDate not supplied

Spaceborne Lidar Retrievals of PM2.5 for Air Quality Studies and Applications

Fine particulate matter (PM2.5) substantially contributes to air pollution and negatively affects human health. While many studies have investigated the use of passive column-integrated aerosol optical depth to infer surface PM2.5, the use of lidar observations for air quality characterization is not nearly as extensive. Lidar measurements are critical, however, due to the vertical aerosol information they provide, including near the surface. In this presentation, we first provide an overview of various lidar-based approaches for estimating PM2.5 concentrations and then discuss how lidar measurements can assist other air quality applications. For example, estimates of PM2.5 have been obtained in a physics-based approach through CALIOP near-surface aerosol extinction retrievals, assumptions on the mass extinction efficiency, and incorporating other parameters (an aerosol hygroscopic growth factor and PM2.5/PM10 ratio). Application of this algorithm over the contiguous United States (CONUS) from 2006 to 2018 yielded larger PM2.5 values over the eastern and western CONUS (~10-15 μg/m³) and lower PM2.5 levels in the central CONUS (~5 μg/m³). These spatial patterns were similar to those from gridded PM2.5 concentrations obtained through in situ measurements at ground stations operated by the US Environmental Protection Agency. In another approach, the Cloud Aerosol Transport System (CATS) lidar was used with the Goddard Earth Observing System (GEOS) model in a 1D ensemble-based variational technique to obtain PM2.5 over the US and Europe, and the spatial patterns of the CATS/GEOS based PM2.5 concentrations generally captured those from surface stations (with corresponding hourly EPA PM2.5 vs CATS PM2.5 statistics of R=0.4 and bias=1.5 μg/m³). In our recent work, as part of the Models, In situ, and Remote sensing of Aerosols (MIRA) Working Group, we have applied both the CALIOP and CATS/GEOS based approaches over the highly polluted country of India during the post-monsoon season (September-October 2016). We derived elevated levels of two-month mean PM2.5 (~100 μg/m³) in northern India, especially near New Delhi. These high PM2.5 concentrations in the Indo-Gangetic plain are driven in large part from the seasonal burning of crop residue and meteorological conditions typical at this time of the year, such as low wind speeds and a shallow boundary layer. While the satellite-derived PM2.5 moderately replicates (R = ~0.7-0.9) the spatial variability in the two-month mean of surface in situ PM2.5 from monitoring sites operated by the Central and State Pollution Control Boards, we show results from specific scenes for which there are large deviations between the satellite-derived PM2.5 and in situ measurements. Other current work on this topic focuses on developing PM2.5 estimates using airborne high spectral resolution lidar measurements through machine learning regression algorithms and involves several parameters (e.g., aerosol extinction, color ratio, lidar ratio). Application of this method over major metropolitan areas in the US and Asia have resulted in high correlations (R = 0.93) with surface measurements. This airborne lidar approach can be adapted to spaceborne lidar measurements, and all three of these approaches can be applied to ESA’s EarthCARE Atmospheric Lidar instrument, setting the stage for the future Cloud Aerosol Lidar for Global Scale Observations of the Ocean-Land Atmosphere System (CALIGOLA) mission. Ultimately, beyond estimates of PM2.5, the aerosol vertical distribution from lidars can benefit studies involving passive sensor approaches for PM2.5 proxies (including from geostationary satellites), wildfire smoke plume injection heights, volcanic emissions (e.g., ash height retrievals), and aerosol/air quality model assimilation, evaluation, and forecasts.

Travis D Toth↗

NASA NTRS2019

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

Hylke E. Beck↗

NASA NTRS1998

Control of the NASA Langley 16-Foot Transonic Tunnel with the Self-Organizing Feature Map

A predictive, multiple model control strategy is developed based on an ensemble of local linear models of the nonlinear system dynamics for a transonic wind tunnel. The local linear models are estimated directly from the weights of a Self Organizing Feature Map (SOFM). Local linear modeling of nonlinear autonomous systems with the SOFM is extended to a control framework where the modeled system is nonautonomous, driven by an exogenous input. This extension to a control framework is based on the consideration of a finite number of subregions in the control space. Multiple self organizing feature maps collectively model the global response of the wind tunnel to a finite set of representative prototype controls. These prototype controls partition the control space and incorporate experimental knowledge gained from decades of operation. Each SOFM models the combination of the tunnel with one of the representative controls, over the entire range of operation. The SOFM based linear models are used to predict the tunnel response to a larger family of control sequences which are clustered on the representative prototypes. The control sequence which corresponds to the prediction that best satisfies the requirements on the system output is applied as the external driving signal. Each SOFM provides a codebook representation of the tunnel dynamics corresponding to a prototype control. Different dynamic regimes are organized into topological neighborhoods where the adjacent entries in the codebook represent the minimization of a similarity metric which is the essence of the self organizing feature of the map. Thus, the SOFM is additionally employed to identify the local dynamical regime, and consequently implements a switching scheme than selects the best available model for the applied control. Experimental results of controlling the wind tunnel, with the proposed method, during operational runs where strict research requirements on the control of the Mach number were met, are presented. Comparison to similar runs under the same conditions with the tunnel controlled by either the existing controller or an expert operator indicate the superiority of the method.

Motter, Mark A.↗

NASA NTRS2024

Dust Model Sensitivity to Dust Source Mask, Sandblasting Efficiency, Air Density, and Land Use: Implications for Model Improvement

This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared to a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 10 times more dust than a physics-based expression. Simulations using model-predicted versus a fixed constant for air density differ by only 8%. However, this difference could range between 12 and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The differences in the pairs of these simulations are generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.

Dust-emission source↗

NASA NTRS2001

Radiation dosimetry using three-dimensional optical random access memories

Three-dimensional optical random access memories (3D ORAMs) are a new generation of high-density data storage devices. Binary information is stored and retrieved via a light induced reversible transformation of an ensemble of bistable photochromic molecules embedded in a polymer matrix. This paper describes the application of 3D ORAM materials to radiation dosimetry. It is shown both theoretically and experimentally, that ionizing radiation in the form of heavy charged particles is capable of changing the information originally stored on the ORAM material. The magnitude and spatial distribution of these changes are used as a measure of the absorbed dose, particle type and energy. The effects of exposure on 3D ORAM materials have been investigated for a variety of particle types and energies, including protons, alpha particles and 12C ions. The exposed materials are observed to fluoresce when exposed to laser light. The intensity and the depth of the fluorescence is dependent on the type and energy of the particle to which the materials were exposed. It is shown that these effects can be modeled using Monte Carlo calculations. The model provides a better understanding of the properties of these materials. which should prove useful for developing systems for charged particle and neutron dosimetry/detector applications. c2001 Published by Elsevier Science B.V.

Non-NASA Center↗

NASA NTRS2004

Starting Vortex Identified as Key to Unsteady Ejector Performance

Unsteady ejectors are currently under investigation for use in some pulse-detonation-engine-based propulsion systems. Experimental measurements made in the past, and recently at the NASA Glenn Research Center, have demonstrated that thrust augmentation can be enhanced considerably when the driver is unsteady. In ejector systems, thrust augmentation is defined as = T(sup Total)/T(sup j), where T(sup Total) is the total thrust of the combined ejector and driving jet and T(sup j) is the thrust due to the driving jet alone. There are three images in this figure, one for each of the named thrust sources. The images are color contours of measured instantaneous vorticity. Each image is an ensemble average of at least 150 phase-locked measurements. The flow is from right to left, and the shape and location of each driver is shown on the far right of each image. The emitted vortex is a clearly defined "doughnut" of highly vortical (spinning) flow. In these planar images, the vortex appears as two distorted circles, one above, and one below the axis of symmetry. Because they are spinning in the opposite direction, the two circles have vorticity of opposite sign and thus are different colors. There is also a rectangle shown in each image. Its width represents the ejector diameter that was found experimentally to yield the highest thrust augmentation. It is apparent that the optimal ejector diameter is that which just "captures" the vortex: that is, the diameter bounding the outermost edge of the vortex structure. The exact mechanism behind the enhanced performance is unclear; however, it is believed to be related to the powerful vortex emitted with each pulse of the unsteady driver. As such, particle imaging velocimetry (PIV) measurements were obtained for three unsteady drivers: a pulsejet, a resonance tube, and a speaker-driven jet. All the drivers were tested with ejectors, and all exhibited performance enhancement over similarly sized steady drivers. The characteristic starting vortices of each driver are shown in these images. The images are color contours of measured instantaneous vorticity. Each image is an ensemble average of at least 150 phase-locked measurements. The flow is from right to left. The shape and location of each driver is shown on the far right of each image. The rectangle shown in each image represents the ejector diameter that was found experimentally to yield the highest thrust augmentation. It is apparent that the optimal ejector diameter is that which just "captures" the vortex: that is, the diameter bounding the outermost edge of the vortex structure. Although not shown, it was observed that the emitted vortex spread as it traveled downstream. The spreading rate for the pulsejet is shown as the dashed lines in the top image. A tapered ejector was fabricated that matched this shape. When tested, the ejector demonstrated superior performance to all those previously tested at Glenn (which were essentially of straight, cylindrical form), achieving a remarkable thrust augmentation of 2. The measured thrust augmentation is shown as a function of ejector length. Also shown are the thrust augmentation values achieved with the straight, cylindrical ejectors of varying diameters. Here, thrust augmentation is plotted as a function of ejector length for several families of ejector diameters. It can be seen that large thrust augmentation values are indeed obtained and that they are sensitive to both ejector length and diameter, particularly the latter. Five curves are shown. Four correspond to straight ejector diameters of 2.2, 3.0, 4.0, and 6.0 in. The fifth curve corresponds to the tapered ejector contoured to bound the emitted vortex. For each curve, there are several data points corresponding to different lengths. The largest value of thrust augmentation is 2.0 for the tapered ejector and 1.81 for the straight ejectors. Regardless of their diameters, all the ejectors trend toward peak performance at a particular leng. That the cross-sectional dimensions of optimal ejectors scaled precisely with the vortex dimensions on three separate pulsed thrust sources demonstrates that the action of the vortex is responsible for the enhanced ejector performance. The result also suggests that, in the absence of a complete understanding of the entrainment and augmentation mechanisms, methods of characterizing starting vortices may be useful for correlating and predicting unsteady ejector performance.

Paxson, Daniel E.↗

NASA NTRSDate not supplied

Adding GPU Support to the Markov Chain Monte Carlo Code Catmip

In geophysics, we are confronted with many under-determined inverse problems. For example, all of our observations of earthquakes are made at the Earth’s surface. So, when we try to infer how slip during an earthquake evolves in space and time, we find that there are many potential slip histories that are consistent with our limited observations and our understanding of earthquake physics. One way to approach these problems is with Bayesian analysis which allows us to infer the ensemble of all potential slip models that satisfy the observations and our prior knowledge of earthquake physics. In Bayesian analysis, our prior knowledge is known as the prior probability density function or prior PDF, the fit to the data is known as the data likelihood, and the target PDF that satisfies both the prior PDF and data likelihood is known as the posterior PDF. However, simulating the posterior PDF typically requires using Markov Chain Monte Carlo (MCMC) to draw tens of billions of random realizations of earthquake slip models, which may not be computationally feasible. To make this and similar geophysical inversions computationally tractable, we developed the Cascading Adaptive Transitional Metropolis In Parallel (CATMIP) algorithm. CATMIP is an efficient parallel Markov Chain Monte Carlo (MCMC) sampler that is used for model fitting and uncertainty quantification in geophysics. Example use cases are earthquake rupture modeling, determining mineral composition on Mars, reconstructing the history of ocean salinity, and historical earthquake relocation. CATMIP employs many parallel instances of the Metropolis algorithm for sampling in a transitioning framework. Transitioning is a process in which a set of random samples at equilibrium with a known probability density function (PDF) are used as seeds for the Markov chains to sample successive target PDFs that incrementally move the distribution from the starting seeds to the final desired PDF that describes the relative plausibility of potential values for the model parameters. The algorithm is implemented as a Master-Worker model employing MPI for communication. The worker processes are loosely coupled with global parameters periodically optimized by the master process. This provides a very high amount of parallelism with little communication between updates. During the presentation we will discuss the history of the algorithm and elaborate the earthquake rupture modeling use case for the CATMIP package. Our first step toward GPU optimization was to optimize the code for the CPU. CPU profiling revealed that most of the compute time is spent in calls to level 2 BLAS routines and calls to GSL random number generators. We revised the algorithm to employ level 3 BLAS routines instead. In our presentation we will describe how this was accomplished. Adding GPU support to CATMIP consisted mostly of replacing the calls to GSL with calls to GPU vendor-provided library routines. A small number of loops were directly implemented in CUDA. In the presentation will provide implementation details. Finally, we will discuss methods for profiling and opportunities for further optimizing GPU execution. By creating a code with the flexibility to run on either a CPU or GPU architecture, CATMIP can be used on systems ranging from large CPU-based HPC environments to single servers with GPU acceleration and everything in between.

HECC↗