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The Llinked Windows Interactive Data System is a prototype visual data exploration system resulting from a NASA/JPL program of research into applying graphical methods for rapidly accessing, displaying and analyzing large multivariate multidisciplinary data sets.
The Linked Windows Interactive Data System is a prototype visual data exploration system resulting from a NASA/JPL program of research into graphical methods for rapidly and interactively accessing, displaying and analyzing large multivariate multidisciplinary data sets.
This paper presents results of uncertainty and sensitivity analyses conducted to assess the relative merits of three duct propagation codes. Results from this study are intended to support identification of a "working envelope" within which to use the various approaches underlying these propagation codes. This investigation considers a segmented liner configuration that models the NASA Langley Grazing Incidence Tube, for which a large set of measured data was available. For the uncertainty analysis, the selected input parameters (source sound pressure level, average Mach number, liner impedance, exit impedance, static pressure and static temperature) are randomly varied over a range of values. Uncertainty limits (95% confidence levels) are computed for the predicted values from each code, and are compared with the corresponding 95% confidence intervals in the measured data. Generally, the mean values of the predicted attenuation are observed to track the mean values of the measured attenuation quite well and predicted confidence intervals tend to be larger in the presence of mean flow. A two-level, six factor sensitivity study is also conducted in which the six inputs are varied one at a time to assess their effect on the predicted attenuation. As expected, the results demonstrate the liner resistance and reactance to be the most important input parameters. They also indicate the exit impedance is a significant contributor to uncertainty in the predicted attenuation.
In a recent study (Desai, 2008) of the actual landing sites for the Mars Pathfinder, Mars Exploration Rovers and the Phoenix Mars Lander, data collected indicates that these missions landed at least 13 km downrange of their targeted landing sites. A direct reason is that density measurements taken during entry, descent and landing were consistently found to be lower than those predicted by Mars atmospheric models. The basis of this study is to further investigate this problem to see if certain aspects of current Mars atmospheric models need to be reevaluated. This study compared four different atmospheric profiles at nine pairs of comparable sites around Mars. The comparable sites are similar in location and Local True Solar Time; however they are from different Mars years. One year had normal conditions, while the other year experienced a global dust storm. Three of the atmospheric profiles were obtained by the Mars Global Surveyor (MGS); these data sets include a Radio Science profile and Thermal Emission Spectrometer data in limb and nadir projections. The three MGS data sets were selected from large observational databases to allow the sites to have data observed at nearcoincident times. The last atmospheric profile is from the Mars Global Reference Atmospheric Model (Mars-GRAM), an engineering level atmospheric model widely used for mission applications. Through careful data evaluation, several conclusions were drawn. Ratios among the data show that the three observed MGS profiles were consistent with each other, although they were recorded by three different methods. Mars-GRAM data had larger differences from the observed data. Mars-GRAM particularly had issues with consistently overestimating upper atmosphere atmospheric density, as noted in Desai s study. Differences between the observed atmospheric density profiles from the Mars-GRAM atmospheric density profile could be attributed to a temperature bias in the model, especially in the upper atmosphere.
Cloud Computing has been implemented in several commercial arenas. The NASA Nebula Cloud Computing platform is an Infrastructure as a Service (IaaS) built in 2008 at NASA Ames Research Center and 2010 at GSFC. Nebula is an open source Cloud platform intended to: a) Make NASA realize significant cost savings through efficient resource utilization, reduced energy consumption, and reduced labor costs. b) Provide an easier way for NASA scientists and researchers to efficiently explore and share large and complex data sets. c) Allow customers to provision, manage, and decommission computing capabilities on an as-needed bases
The Kepler mission is described in overview and the Kepler technique for discovering exoplanets is discussed. The design and implementation of the Kepler spacecraft, tracing the data path from photons entering the telescope aperture through raw observation data transmitted to the ground operations team is described. The technical challenges of operating a large aperture photometer with an unprecedented 95 million pixel detector are addressed as well as the onboard technique for processing and reducing the large volume of data produced by the Kepler photometer. The technique and challenge of day-to-day mission operations that result in a very high percentage of time on target is discussed. This includes the day to day process for monitoring and managing the health of the spacecraft, the annual process for maintaining sun on the solar arrays while still keeping the telescope pointed at the fixed science target, the process for safely but rapidly returning to science operations after a spacecraft initiated safing event and the long term anomaly resolution process.The ground data processing pipeline, from the point that science data is received on the ground to the presentation of preliminary planetary candidates and supporting data to the science team for further evaluation is discussed. Ground management, control, exchange and storage of Kepler's large and growing data set is discussed as well as the process and techniques for removing noise sources and applying calibrations to intermediate data products.
The potential of Landsat data processing to provide systematic continental scale products has been demonstrated by several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent free availability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicable to large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correction methods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmospheric characterization approaches. The MODIS-based method uses the MODIS Terra derived dynamic aerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions in each coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from each Landsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validation results are presented comparing ETM+ atmospherically corrected data generated using these two methods with AERONET corrected ETM+ data for 95 10 km×10 km 30 m subsets, a total of nearly 8 million 30 m pixels, located across the conterminous United States. The results indicate that the MODIS-based method has better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands.
The potential of Landsat data processing to provide systematic continental scale products has been demonstratedby several projects including the NASA Web-enabled Landsat Data (WELD) project. The recent freeavailability of Landsat data increases the need for robust and efficient atmospheric correction algorithms applicableto large volume Landsat data sets. This paper compares the accuracy of two Landsat atmospheric correctionmethods: a MODIS-based method and the Landsat Ecosystem Disturbance Adaptive ProcessingSystem (LEDAPS) method. Both methods are based on the 6SV radiative transfer code but have different atmosphericcharacterization approaches. The MODIS-based method uses the MODIS Terra derived dynamicaerosol type, aerosol optical thickness, and water vapor to atmospherically correct ETM+ acquisitions ineach coincident orbit. The LEDAPS method uses aerosol characterizations derived independently from eachLandsat acquisition and assumes a fixed continental aerosol type and uses ancillary water vapor. Validationresults are presented comparing ETM+ atmospherically corrected data generated using these two methodswith AERONET corrected ETM+ data for 95 10 km10 km 30 m subsets, a total of nearly 8 million 30 mpixels, located across the conterminous United States. The results indicate that the MODIS-based methodhas better accuracy than the LEDAPS method for the ETM+ red and longer wavelength bands.
To investigate the ability of column (or partial column) information to represent surface air quality, results of linear regression analyses between surface mixing ratio data and column abundances for O3 and NO2 are presented for the July 2011 Maryland deployment of the DISCOVER-AQ mission. Data collected by the P-3B aircraft, ground-based Pandora spectrometers, Aura/OMI satellite instrument, and simulations for July 2011 from the CMAQ air quality model during this deployment provide a large and varied data set, allowing this problem to be approached from multiple perspectives. O3 columns typically exhibited a statistically significant and high degree of correlation with surface data (R(sup 2) > 0.64) in the P- 3B data set, a moderate degree of correlation (0.16 < R(sup 2) < 0.64) in the CMAQ data set, and a low degree of correlation (R(sup 2) < 0.16) in the Pandora and OMI data sets. NO2 columns typically exhibited a low to moderate degree of correlation with surface data in each data set. The results of linear regression analyses for O3 exhibited smaller errors relative to the observations than NO2 regressions. These results suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere can be meaningful for surface air quality analysis.
The landscape freeze/thaw (FT) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and Soil Moisture and Ocean Salinity (SMOS) L-band passive microwave measurements using a polarization ratio-based algorithm for landscape FT monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: tundra, boreal forest, and prairies. The reference data include in situ measurements of soil temperature (Tsoil) and air temperature (Tair), and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between Lband FT detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using Tair as the reference. Aquarius radiometer 2 (incidence angle of 39.6) data gives the best accuracies (90.8), while for SMOS the best results (87.8 of accuracy) are obtained at higher incidence angles (55- 60). The FT algorithm identifies both freeze onset and end with a delay of about one week in tundra and two weeks in forest and prairies, when compared to Tair. The analysis shows a stronger FT signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the polarization ratio) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar FT patterns over Canada. This study shows the potential of using L-band spaceborne observations for FT monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth.
This paper discusses the Ontology-driven Interactive Search Environment for Earth Sciences (ODISEES) project currently being developed to aid researchers attempting to find usable data among an overabundance of closely related data. ODISEES' ontological structure relies on a modular, adaptable concept modeling approach, which allows the domain to be modeled more or less as it is without worrying about terminology or external requirements. In the model, variables are individually assigned semantic content based on the characteristics of the measurements they represent, allowing intuitive discovery and comparison of data without requiring the user to sift through large numbers of data sets and variables to find the desired information.
With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.
Emissions of trace gases and particles from fires have a major impact on climate, visibility, air quality, and public health. Biomass burning emissions include reactive nitrogen gases, among which ammonia (NH3) plays a prominent role. NH3 is a short-lived gas that acts as precursor for secondary inorganic aerosols. This process is still poorly constrained in downwind fire plumes. In summer 2019, NASA and NOAA carried out the joint airborne FIREX-AQ (Fire Influence on Regional to global Environments and Air Quality) mission over the continental US to sample plumes from wildfires and agricultural fires. On board the NASA DC-8, we used a modified PTR-ToF-MS instrument for measuring NH3 in situ and at high time resolution. Over the course of the mission, we collected a large set of NH3 data in plumes emitted from fires differing in fuel type and burning stage. Herein, we will present the measured NH3 emissions ratios and factors, compare them with the literature, and investigate the dependence of NH3 emissions on fuel type and burning conditions.
Predicting the onset of boundary-layer transition is often more accurate using physics-based models that directly compute disturbance growth rather than phenomenological models often implemented into industrial CFD codes. The aim of this ongoing study is to calibrate linear, physics-based computations of transition in subsonic flows over swept wings against a large set of experimental data. Advancing the calibration of linear models of transition contributes to the CFD-Vision-2030 goal of automated boundary-layer transition prediction. This progress report uses the dual N-factor method to model transition over the swept NACA 64-2-015A wing. The flow conditions match selected test conditions from an extensive experimental dataset acquired from the NASA Ames 12-ft Pressure Tunnel. The OVERFLOW 2.4b flow solver is used to obtain laminar basic states based on an infinite-span assumption. Stability analyses are performed on 365 distinct configurations with linear stability theory (LST) and parabolized stability equations (PSE) from the Langley Stability and Transition Analysis Codes (LASTRAC), modeling the growth of Tollmien-Schlichting (TS) and stationary crossflow (SCF) disturbances. From a total of 67 data points for unswept, i.e., TS-dominant configurations, the critical N-factor based on PSE is found to be N_TS = 9. The SCF critical N-factor is found to be near 8 for the highly swept, SCF-dominant configurations. Dual N-factor curves for both LST and PSE computations demonstrate a high level of interaction between TS and SCF. It may be worthwhile to investigate an alternate metric to visualize maximal SCF amplification upstream of the transition location to account for the growth of SCF modes near the leading edge, which is not considered in the conventional applications of the dual N-factor criterion.
Predicting the onset of boundary-layer transition is often more accurate using physics-based models that directly compute disturbance growth rather than phenomenological models often implemented into industrial CFD codes. The aim of this ongoing study is to calibrate linear, physics-based computations of transition in subsonic flows over swept wings against a large set of experimental data. Advancing the calibration of linear models of transition contributes to the CFD-Vision-2030 goal of automated boundary-layer transition prediction. This progress report uses the dual N-factor method to model transition over the swept NACA 64-2-015A wing. The flow conditions match selected test conditions from an extensive experimental dataset acquired from the NASA Ames 12-ft Pressure Tunnel. The OVERFLOW 2.4b flow solver is used to obtain laminar basic states based on an infinite-span assumption. Stability analyses are performed on 365 distinct configurations with linear stability theory (LST) and parabolized stability equations (PSE) from the Langley Stability and Transition Analysis Codes (LASTRAC), modeling the growth of Tollmien-Schlichting (TS) and stationary crossflow (SCF) disturbances. From a total of 67 data points for unswept, i.e., TS-dominant configurations, the critical N-factor based on PSE is found to be N_TS = 9. The SCF critical N-factor is found to be near 8 for the highly swept, SCF-dominant configurations. Dual N-factor curves for both LST and PSE computations demonstrate a high level of interaction between TS and SCF. It may be worthwhile to investigate an alternate metric to visualize maximal SCF amplification upstream of the transition location to account for the growth of SCF modes near the leading edge, which is not considered in the conventional applications of the dual N-factor criterion.
The wide variety of data available to the diligent researcher and the myriad paths to obtaining it are emphasized. This inventory is an attempt to make accessible much of the four-dimensional data available in the world. In this quick-look catalog are listed separate data sets (Data Sets sections), sources of data including centers and large data bases (Sources section), and some data expected to be available in the future (Future Data section). In the Data Sets section, individual data sets are arranged alphabetically by institution, with those archived in the U.S.A. listed first, followed by those found elsewhere. The Sources section includes large data bases, centers, and directories. Sources are arranged alphabetically by country. This section is followed by a Future Data section which is a collection of data sets, experiments, and other future developments of which we are cognizant. A collection of further information and order blanks provided by some of the archiving institutions are presented in the appendix.
Net mass flux from the stratosphere to the troposphere can be computed from the heating rate along the 380K isentropic surface and the time rate of change of the mass of the lowermost stratosphere (the region between the tropopause and the 380K isentrope). Given this net mass flux and the cross tropopause diabatic mass flux, the residual adiabatic mass flux across the tropopause can also be estimated. These fluxes have been computed using meteorological fields from a free-running general circulation model (FVGCM) and two assimilation data sets, FVDAS, and UKMO. The data sets tend to agree that the annual average net mass flux for the Northern Hemisphere is about 1P10 kg/s. There is less agreement on the southern Hemisphere flux that might be half as large. For all three data sets, the adiabatic mass flux is computed to be from the upper troposphere into the lowermost stratosphere. This flux will dilute air entering from higher stratospheric altitudes. The mass fluxes are convolved with ozone mixing ratios from the Goddard 3D CTM (which uses the FVGCM) to estimate the cross-tropopause transport of ozone. A relatively large adiabatic flux of tropospheric ozone from the tropical upper troposphere into the extratropical lowermost stratosphere dilutes the stratospheric air in the lowermost stratosphere. Thus, a significant fraction of any measured ozone STE may not be ozone produced in the higher Stratosphere. The results also illustrate that the annual cycle of ozone concentration in the lowermost stratosphere has as much of a role as the transport in the seasonal ozone flux cycle. This implies that a simplified calculation of ozone STE mass from air mass and a mean ozone mixing ratio may have a large uncertainty.
The Magellan data set constitutes an example of the large volumes of data that today's instruments can collect, providing more detail of Venus than was previously available from Pioneer Venus, Venera 15/16, or ground-based radar observations put together. However, data analysis technology has not kept pace with data collection and storage technology. Due to the sheer size of the data, complete and comprehensive scientific analysis of such large volumes of image data is no longer feasible without the use of computational aids. Our progress towards developing a pattern recognition system for aiding in the detection and cataloging of small-scale natural features in large collections of images is reported. Combining classical image processing, machine learning, and a graphical user interface, the detection of the 'small-shield' volcanoes (less than 15km in diameter) that constitute the most abundant visible geologic feature in the more that 30,000 synthetic aperture radar (SAR) images of the surface of Venus are initially targeted. Our eventual goal is to provide a general, trainable tool for locating small-scale features where scientists specify what to look for simply by providing examples and attributes of interest to measure. This contrasts with the traditional approach of developing problem specific programs for detecting Specific patterns. The approach and initial results in the specific context of locating small volcanoes is reported. It is estimated, based on extrapolating from previous studies and knowledge of the underlying geologic processes, that there should be on the order of 10(exp 5) to 10(exp 6) of these volcanoes visible in the Magellan data. Identifying and studying these volcanoes is fundamental to a proper understanding of the geologic evolution of Venus. However, locating and parameterizing them in a manual manner is forbiddingly time-consuming. Hence, the development of techniques to partially automate this task were undertaken. The primary constraints for this particular problem are that the method must be reasonably robust and fast. Unlike most geological features, the small volcanoes of Venus can be ascribed to a basic process that produces features with a short list of readily defined characteristics differing significantly from other surface features on Venus. For pattern recognition purposes the relevant criteria include (1) a circular planimetric outline, (2) known diameter frequency distribution from preliminary studies, (3) a limited number of basic morphological shapes, and (4) the common occurrence of a single, circular summit pit at the center of the edifice.