Quality assessment of a satellite altimetry data product in the Nordic, Barents, and Kara seas
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Denis L. Volkov↗
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Guidelines for ensuring data quality for photovoltaic system performance assessment and monitoring.
Abstract not provided.
Livera, Andreas↗
GUIDELINES FOR ENSURING DATA QUALITY FOR PHOTOVOLTAIC SYSTEM PERFORMANCE ASSESSMENT AND MONITORING.
Abstract not provided.
Livera, Andreas↗
GUIDELINES FOR ENSURING DATA QUALITY FOR PHOTOVOLTAIC SYSTEM PERFORMANCE ASSESSMENT AND MONITORING.
Abstract not provided.
Livera, Andreas↗
Improving and Assessing the Quality of Uncertainty Quantification in Deep Learning
Deep learning (DL) models have enjoyed increased attention in recent years because of their powerful predictive capabilities. While many successes have been achieved, standard deep learning methods suffer from a lack of uncertainty quantification (UQ). While the development of methods for producing UQ from DL models is an active area of current research, little attention has been given to the quality of the UQ produced by such methods. In order to deploy DL models to high-consequence applications, high-quality UQ is necessary. This report details the research and development conducted as part of a Laboratory Directed Research and Development (LDRD) project at Sandia National Laboratories. The focus of this project is to develop a framework of methods and metrics for the principled assessment of UQ quality in DL models. This report presents an overview of UQ quality assessment in traditional statistical modeling and describes why this approach is difficult to apply in DL contexts. An assessment on relatively simple simulated data is presented to demonstrate that UQ quality can differ greatly between DL models trained on the same data. A method for simulating image data that can then be used for UQ quality assessment is described. A general method for simulating realistic data for the purpose of assessing a model’s UQ quality is also presented. A Bayesian uncertainty framework for understanding uncertainty and existing metrics is described. Research that came out of collaborations with two university partners are discussed along with a software toolkit that is currently being developed to implement the UQ quality assessment framework as well as serve as a general guide to incorporating UQ into DL applications.
97 MATHEMATICS AND COMPUTING↗
Perspectives on Citizen Science Data Quality
Information about data quality helps potential data users to determine whether and how data can be used and enables the analysis and interpretation of such data. Providing data quality information improves opportunities for data reuse by increasing the trustworthiness of the data. Recognizing the need for improving the quality of citizen science data, we describe quality assessment and quality control (QA/QC) issues for these data and offer perspectives on aspects of improving or ensuring citizen science data quality and for conducting research on related issues.
54 ENVIRONMENTAL SCIENCES↗
Cloud Screening and Quality Control Algorithm for Star Photometer Data: Assessment with Lidar Measurements and with All-sky Images
This paper presents the development and set up of a cloud screening and data quality control algorithm for a star photometer based on CCD camera as detector. These algorithms are necessary for passive remote sensing techniques to retrieve the columnar aerosol optical depth, delta Ae(lambda), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of delta Ae() and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable Ae(lambda) and W fluctuations caused by the atmospheric turbulence, the data are averaged on 30 min. The algorithm is applied to the star photometer deployed in the city of Granada (37.16 N, 3.60 W, 680 ma.s.l.; South-East of Spain) for the measurements acquired between March 2007 and September 2009. The algorithm is evaluated with correlative measurements registered by a lidar system and also with all-sky images obtained at the sunset and sunrise of the previous and following days. Promising results are obtained detecting cloud-affected data. Additionally, the cloud screening algorithm has been evaluated under different aerosol conditions including Saharan dust intrusion, biomass burning and pollution events.
data quality control algorithm↗
Deliverable 6.7-Final Technical Report: Development Summary and Evaluation of the Solar Uncertainty Integrator (SUNI) Software
The Data Quality and Uncertainty Integration Project was a three-year effort to address stakeholder needs for assessing solar radiation resource data quality based on existing tools for estimating radiometer measurement uncertainties and assessing post-measurement data quality. The annual research objectives for the project addressed a logical progression of effort needed to achieve the ultimate project goal of developing the Solar Uncertainty Integrator (SUNI) software. This final technical report summarizes the development process for achieving these key research objectives and addresses the outreach and code development efforts in the final year of the project to develop a new solar irradiance data uncertainty integration software package.
14 SOLAR ENERGY↗
An Approach for Setting Priorities for Experimental Studies in Support of Safety Assessments - 19393
Many experiments have been conducted, in both academia and industry, to support the understanding of the disposal of radioactive waste. For the disposal of low-level radioactive waste (LLW) into radioactive waste repositories, it is important to develop an approach to tackle which datasets are able to directly underpin safety assessments, and what future research experiments could address the gaps in this understanding. In this paper, we present an approach to identify which future experiments should be considered to underpin safety assessments for disposals of LLW in a near-surface repository. The approach involves 6-steps: define the disposal problem (e.g. the disposal of LLW in engineered vaults); determine what key processes may affect the disposal of the waste; establish the assumptions underpinning the key processes; determine how sensitive the key process parameters are to variation; determine whether key data are available and assess the quality of that data with respect to the disposal problem; and determine attributes for experiments needed to supply missing key data. We show, in this paper, how this approach can be used to develop a better understanding of C-14 gas generation, under varying degrees of groundwater saturation, for the disposal of LLW into engineered vaults, in the near-surface. Furthermore, we have developed an approach that identifies the types of experiments and their attributes to aid researchers to help select appropriate types of experiments to underpin safety assessments. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles Using Python
Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.
Jessica K Headley↗
Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles
Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.
Jessica K Headley↗
Evolution of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) flight and ground data processing system
The modifications made to both the AVIRIS instrument and the data processing facility since the instrument made its initial flight in the summer of 1987 are described. Historical development of the data system is discussed and attention is given to enhancements to instrument stability and noise performance. AVIRIS data facility objectives include rapid and automated decommutation and archiving of data, the ability to assess the quality of the data and health of the instrument, and the provision of an automated procedure for applying radiometric corrections to the data and providing responsive processing of data requests from investigators. The modifications described have resulted in an overall radiometric stability of better than 10 percent, with stability of only a few percent during a single flight.
Porter, Wallace M.↗
Science Quality Assessment in Support of EOS MISR Data
The Multi-angle Imaging SpectroRadiometer (MISR) instrument, scheduled to fly on board the first Earth Observing System (EOS) spacecraft, will have a data stream that produces over 100 GB of data per day, once in full operation, of information about the Earth's atmosphere, clouds, and surface.
remote sensing Multi-angle Imaging SpectroRadiomet↗
Road Extraction from AVIRIS Using Spectral Mixture and Q-Tree Filter Techniques
Accurate road location and condition information are of primary importance in road infrastructure management. Additionally, spatially accurate and up-to-date road networks are essential in ambulance and rescue dispatch in emergency situations. However, accurate road infrastructure databases do not exist for vast areas, particularly in areas with rapid expansion. Currently, the US Department of Transportation (USDOT) extends great effort in field Global Positioning System (GPS) mapping and condition assessment to meet these informational needs. This methodology, though effective, is both time-consuming and costly, because every road within a DOT's jurisdiction must be field-visited to obtain accurate information. Therefore, the USDOT is interested in identifying new technologies that could help meet road infrastructure informational needs more effectively. Remote sensing provides one means by which large areas may be mapped with a high standard of accuracy and is a technology with great potential in infrastructure mapping. The goal of our research is to develop accurate road extraction techniques using high spatial resolution, fine spectral resolution imagery. Additionally, our research will explore the use of hyperspectral data in assessing road quality. Finally, this research aims to define the spatial and spectral requirements for remote sensing data to be used successfully for road feature extraction and road quality mapping. Our findings will facilitate the USDOT in assessing remote sensing as a new resource in infrastructure studies.
Gardner, Margaret E.↗
Needs Assessment for the Use of NASA Remote Sensing Data for Regulatory Water Quality
This slide presentation reviews the assessment of the needs that NASA can use for the remote sensing of water quality. The goal of this project is to provide information for decision-making activities (water quality standards) using remotely sensed/satellite based water quality data from MODIS and Landsat data.
Spiering, Bruce↗
Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment
The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.
Quattrochi, Dale A.↗
Quantitative Microstructure Characterization of a NMC Electrode
Performance of lithium-ion batteries (LIBs) is strongly influenced by the porous microstructure of their electrodes. In this work, 3D microstructures of calendared and un-calendared positive electrode LiNi1/3Mn1/3Co1/3O2 (NMC) have been investigated in order to extract relevant properties useful for battery modeling. Transport (volume fraction, connectivity, particle size and tortuosity) and electrochemical (specific surface area) properties have been calculated for the pore and the active material. Special attention has been paid to determine the size of the so-called representative volume element (RVE) required to be statistically representative of the heterogeneous medium. Several parameters have been calculated using a panel of different numerical methods in order to compare their results. Besides, the image level of detail has been evaluated (using original criteria based upon edge detection) to assess the overall data quality available for the study.
30 DIRECT ENERGY CONVERSION↗
An assessment of remote sensor imagery in the determination of housing quality data
Selected census tracts in the metropolitan Washington area were examined using varying scales of aerial photography. Observable characteristics of housing and neighborhoods were assessed to determine feasibility of providing data on housing stock and quality and neighborhood condition from the imagery. Small scale imagery is shown to be of relatively marginal value in providing much of the data in the detail required, but can be useful for general survey purposes.
Mallon, H. J.↗