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Results for “data quality analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1982

Preliminary analysis of STS-4 entry flight data

A preliminary analysis of the data obtained during entry of the STS-4 Flight was completed. Planned maneuvers were flown during this flight to increase the quality of stability and control analysis, similar to the techniques used during STS-3. The derivatives obtained from STS-4 agreed fairly well with the derivatives obtained on previous flights. The dependence of aileron effectiveness on a elevon position above a Mach number of 10 seen on STS-3 was conclusively verified on STS-4. CSS Mode was engaged to fly the heading alignment circle. After engagement, several cycles of a low amplitude pilot induced oscillation (1 deg/sec) at about 0.3 hertz can be seen. No PIO suppressor activity was seen between preflare and touchdown. This approach demonstrates the advantage of the shallow final glideslope approach. In this type of approach, the pilot is not required to make accurate altitude judgments since an acceptable landing can be made without performing the final flare.

Source record↗

NASA NTRSDate not supplied

Science Workflows using Kamodo

Kamodo is a powerful python software package based on data functionalization. Once a given data set is functionalized, a large variety of capabilities are easily accessible in Kamodo, including unit conversions, custom analysis via function composition, interactive publication quality visualizations, and LaTeX encoding. The entirety of capabilities available in Kamodo are easily applied to both simulated and observed data across the multiple domains of Heliophysics and even in other disciplines. This work includes a variety of science workflows using Kamodo in combination with other resources, including with other python software packages, that expand the utility of Kamodo even further. These workflows include model-data comparisons, ensemble modeling examples, satellite mission planning examples, and other applications, all of which are freely available on CCMC’s Kamodo Github page for the community to adapt to their own uses (https://github.com/nasa/Kamodo). We invite the community to use these workflows and to contribute their own to share.

software↗

NASA NTRSDate not supplied

Science Workflows using Kamodo

python↗

NASA NTRS1972

Data acquisition and processing history for the Explorer 33 (AIMP-D) satellite

The quality control monitoring system, using accounting and quality control data bases, made it possible to perform an in-depth analysis. Results show that the percentage of useable data files for experimenter analysis was 97.7%; only 0.4% of the data sequences supplied to the experimenter exhibited missing data. The 50 percentile probability delay values (referenced to station record data) indicate that the analog tapes arrived within 11 days, the data were digitized within 4.2 weeks, and the experimenter tapes were delivered in 8.95 weeks or less.

Karras, T. J.↗

NASA NTRS1975

Remote sensor digital image data analysis using the General Electric Image 100 analysis system (a study of analysis speed, cost, and performance)

The author has identified the following significant results. It was found that the high speed man machine interaction capability is a distinct advantage of the image 100; however, the small size of the digital computer in the system is a definite limitation. The system can be highly useful in an analysis mode in which it complements a large general purpose computer. The image 100 was found to be extremely valuable in the analysis of aircraft MSS data where the spatial resolution begins to approach photographic quality and the analyst can exercise interpretation judgements and readily interact with the machine.

Mcmurtry, G. J.↗

NASA NTRS1990

The diffuse galactic gamma ray emission

The EGRET (Energetic Gamma-Ray Experiment Telescope) detector will provide a much more detailed view of the diffuse galactic gamma ray intensity in terms of higher resolution, greater statistical significance, and broader energy range than earlier missions. These observations will furnish insight into a number of very important questions related to the dynamics and structure of the Galaxy. A diffuse emission model is being developed that incorporates the latest information on matter distribution and source functions. In addition, it is tailored to the EGRET instrument response functions. The analysis code of the model maintains flexibility to accommodate the quality of the data that is anticipated. The discussion here focuses on the issues of the distributions of matter, cosmic rays, and radiation fields, and on the important source functions that enter into the model calculation of diffuse emission.

Bertsch, David L.↗

NASA NTRS1992

Space Station Freedom Environmental Health Care Program

The paper discusses the environmental planning and monitoring aspects of the Space Station Freedom (SSF) Environmental Health Care Program, which encompasses all phases of the SSF assembly and operation from the first element entry at MB-6 through the Permanent Manned Capability and beyond. Environmental planning involves the definition of acceptability limits and monitoring requirements for the radiation dose barothermal parameters and potential contaminants in the SSF air and water and on internal surfaces. Inflight monitoring will be implemented through the Environmental Health System, which consists of five subsystems: Microbiology, Toxicology, Water Quality, Radiation, and Barothermal Physiology. In addition to the environmental data interpretation and analysis conducted after each mission, the new data will be compared to archived data for statistical and long-term trend analysis and determination of risk exposures. Results of these analyses will be used to modify the acceptability limits and monitoring requirements for the future.

Richard, Elizabeth E.↗

NASA NTRS1993

Implications of ROSAT observations for the local hot bubble

The ROSAT all-sky survey and Guest Observer pointed observations have brought a wealth of high-quality data to the study of the soft X-ray diffuse background. Analysis of the spatial structure of the 1/4 keV flux, including observations of shadows cast by discrete clouds in the interstellar medium, have allowed the separation of the observed flux into foreground and background components supporting a spatially varying local component consistent with previous ideas of the local hot bubble, a highly variable galactic halo component, and an extragalactic background.

Snowden, S. L.↗

NASA NTRS1993

Software Formal Inspections Standard

This Software Formal Inspections Standard (hereinafter referred to as Standard) is applicable to NASA software. This Standard defines the requirements that shall be fulfilled by the software formal inspections process whenever this process is specified for NASA software. The objective of this Standard is to define the requirements for a process that inspects software products to detect and eliminate defects as early as possible in the software life cycle. The process also provides for the collection and analysis of inspection data to improve the inspection process as well as the quality of the software.

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NASA NTRS2002

Monitoring and Assimilation of MLS Measurements in the DAO Ozone Data Assimilation System

Since 1999 the DAO (Data Assimilation Office) at NASA Goddard has operationally assimilated ozone measurements in near real time. Currently, the assimilation system analyzes SBUV profile and total column measurements using an off line CTM with parameterized chemistry within the 3D-PSAS algorithm. During the last year the assimilation system was modified to either monitor or actively assimilate MLS (Microwave Limb Sounder) measurements in conjunction with the active assimilation of TOMS total column and SBUV profiles. It is expected that the active assimilation of MLS profiles will improve analysis results in two ways. First, there should be an improvement in the vertical resolution. Second, there should be an improvement in regions where SBUV measurements do not exist (such as in the polar night). A series of experiments using UARS (Upper Atmospheric Research Satellite) MLS data from December 1991 to March 1992 were run. In these experiments MLS data was either monitored in conjunction with active assimilation of the SBUV profile and TOMS total column measurements, or some combination of MLS, TOMS, and SBUV observations was actively assimilated. Monitoring of MLS data indicated that the MLS observations contain verifiable information that should improve the vertical structure of the analysis results, especially below the ozone peak and above the tropopause. The monitoring also substantiated the potential to improve the assimilation in the polar night. Active assimilation of MLS data does indeed improve the analysis results in these two ways, although the quality of the improvements is not uniform. This suggests that refinement of the specification of the error covariances might be needed to optimize the system. In addition it may be necessary to account for biases between the different sources of ozone information.

Atlas, Robert↗

NASA NTRS2006

Processing TES Level-1B Data

TES L1B Subsystem is a computer program that performs several functions for the Tropospheric Emission Spectrometer (TES). The term "L1B" (an abbreviation of "level 1B"), refers to data, specific to the TES, on radiometric calibrated spectral radiances and their corresponding noise equivalent spectral radiances (NESRs), plus ancillary geolocation, quality, and engineering data. The functions performed by TES L1B Subsystem include shear analysis, monitoring of signal levels, detection of ice build-up, and phase correction and radiometric and spectral calibration of TES target data. Also, the program computes NESRs for target spectra, writes scientific TES level-1B data to hierarchical- data-format (HDF) files for public distribution, computes brightness temperatures, and quantifies interpixel signal variability for the purpose of first-order cloud and heterogeneous land screening by the level-2 software summarized in the immediately following article. This program uses an in-house-developed algorithm, called "NUSRT," to correct instrument line-shape factors.

DeBaca, Richard C.↗

NASA NTRS2011

Airborne Cloud Computing Environment (ACCE)

Airborne Cloud Computing Environment (ACCE) is JPL's internal investment to improve the return on airborne missions. Improve development performance of the data system. Improve return on the captured science data. The investment is to develop a common science data system capability for airborne instruments that encompasses the end-to-end lifecycle covering planning, provisioning of data system capabilities, and support for scientific analysis in order to improve the quality, cost effectiveness, and capabilities to enable new scientific discovery and research in earth observation.

airborne science↗

NASA NTRS2017

Validation of a Thermo-Ablative Model of Elastomeric Internal Insulation Materials

In thermo-ablative material modeling, as in many fields of analysis, the quality of the existing models significantly exceeds that of the experimental data required for their validation. In an effort to narrow this gap, a laboratory-scale internal insulation test bed was developed that exposes insulation samples to realistic solid rocket motor (SRM) internal environments while being instrumented to record real-time rates of both model inputs (i.e., chamber pressure, total surface heat flux, and radiative heat flux) as well as model outputs (i.e., material decomposition depths (MDDs) and in-depth material temperatures). In this work, the measured SRM internal environment parameters were used in conjunction with equilibrium thermochemistry codes as inputs to one-dimensional thermo-ablative models of the PBINBR and CFEPDM insulation samples used in the lab-scale test firings. The computed MDD histories were then compared with those deduced from real-time X-ray radiography of the insulation samples, and the calculated in-depth temperatures were compared with those measured by embedded thermocouples. The results of this exercise emphasize the challenges of modeling and testing elastomeric materials in SRM environments while illuminating the path forward to improved fidelity.

Martin, Heath T.↗

NASA NTRS2018

Software Formal Inspections Standard

The purpose of this Standard is to define the requirements for a software inspection process aimed at detecting and eliminating defects as early as possible in the software life cycle. This process can be used for any documented product; however, this Standard focuses on its use for software products - i.e., software code, plans, manuals, etc. The process provides for the collection and analysis of inspection data to improve the inspection process as well as the quality of the software.

Wetherholt, Martha S.↗

NASA NTRS2019

Workforce for the Future Development of Space Access Vehicles

The development of advanced vehicles that will travel between planetary surfaces with atmospheres and space requires the availability of experimental and computational capabilities that accomplish the research leading to new technologies and the development, test, and evaluation (RDT&E) of new flight systems. The United States is at risk of not having the required RDT&E workforce – qualified and in sufficient numbers – in place and ready to meet the future market’s commercial and defense needs. Systemic challenges include uncertain Federal budgets that limit and interrupt government research and development, the projectized nature of new space access systems that drive boom/bust cycles, an aging aerospace workforce (compounded by a mid-age demographic gap), limited to declining investment in sustaining and advancing experimental and computational tools infrastructure, lack of standards for sharing (and leveraging) data, dramatically changing technologies, changing social norms, and potentially large increases in commercial market needs. Flight systems have mission-based trajectories to and from space and designers must ensure risk is properly assessed across the entire trajectory. Thus, physics questions must be answered at each stage of flight, which requires a suite of experimental and computational tools. The RDT&E workforce utilizing these tools include subject matter experts from the producers, interested in acquiring data and information on the product, and from the capabilities being utilized, interested in addressing the product customer’s needs (providing robust data collection techniques, data quality, timeliness – available when needed, efficient with cost management, and teaming on data analysis). This requires a range of skills – in addition to aerospace, other engineers, and software developers, a highly trained and certified craft and technician workforce is critical to future success. This paper will present a human resources construct that addresses the system of needs for people – including, but more than just technical skills and an application of that construct in the hypersonic Test and Evaluation (T&E) community. This is part of a larger AIAA effort to document challenges and associated best practices for the aerospace RDT&E workforce.

Steven C Dunn↗

NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗

NASA NTRSDate not supplied

ASDC Distribution and Services of TEMPO Data

The Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument represents a groundbreaking advancement in remote sensing technology, providing real-time and high-resolution measurements of atmospheric pollutants and air quality monitoring. This presentation will highlight the significance of TEMPO and its data distribution by NASA’s Atmospheric Science Data Center (ASDC) to facilitate the analyses by the research and end user communities. TEMPO's geostationary orbit allows for continuous and high-resolution measurements of key atmospheric pollutants, including nitrogen dioxide (NO 2 ), ozone (O 3 ), and formaldehyde (HCHO). As a result, researchers can investigate the distribution patterns of these pollutants on an hourly basis, providing valuable information for understanding regional and temporal variations in air quality. Furthermore, this presentation will highlight the usability of TEMPO data in complementing ground-based air quality monitoring networks. ASDC distribution services and tools facilitate efficient data handling and analysis to support research and application uses of TEMPO data. As part of NASA’s Earthdata ecosystem, TEMPO will be available through Earthdata Search and Worldview, as well as have variable and spatial subsetting capabilities. This presentation will provide an overview of data access and services available for TEMPO data.

Hazem Mahmoud↗

NASA NTRSDate not supplied

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Rolf Reichle↗