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The Software Engineering Laboratory was monitoring software development at NASA Goddard Space Flight Center since 1976. The data collection activities of the Laboratory and some of the difficulties of obtaining reliable data are described. In addition, the application of this data collection process to a current prototyping experiment is reviewed.
This is the fifteenth in a series of evaluated sets of rate constants and photochemical cross sections compiled by the NASA Panel for Data Evaluation. The data are used primarily to model stratospheric and upper tropospheric processes, with particular emphasis on the ozone layer and its possible perturbation by anthropogenic and natural phenomena. Copies of this evaluation are available in electronic form and may be printed from the following Internet URL: http://jpldataeval.jpl.nasa.gov/.
This project is motivated by the need for temporary or emergency provisioning of LAN service at KSC. The main goal of the project was to evaluate existing wireless bridge equipment in the KSC environment. Wireless bridge equipment can be used to make a wireless connection between two remotely located LAN segments. This report describes the experimental setup used to evaluate the equipment, including antenna connections, workstation connections, bridge software and workstation software The rangefinder program on the bridge was used to gather data about how the RF propagation environment at KSC affects the performance of the wireless bridge. Data was gathered for indoor as well as outdoor propagation. The report concludes with recommendations on how to take into account the particular terrain and building structures at KSC to design future applications of wireless bridges.
Solar radiation resource measurements from radiometers are used to predict and evaluate the performance of photovoltaic and concentrating solar power systems, validate satellite-based models for estimating solar resources, and advance research in solar forecasting and climate change. This study analyzes the performance of various commercially available radiometers used for measuring global horizontal irradiances (GHI) and direct normal irradiances (DNI). These include pyranometers, pyrheliometers, rotating shadowband irradiometers, and a pyranometer with a shading ring deployed at the National Renewable Energy Laboratory's Solar Radiation Research Laboratory (SRRL). The radiometers in this study were deployed for one year (from April 1, 2011, through March 31, 2012) and compared to measurements from radiometers with the lowest values of estimated measurement uncertainties for producing reference GHI and DNI.
Liquid chromatography-mass spectrometry (LC-MS)-based proteomics studies of large sample cohorts can easily require from months to years to complete. Acquiring consistent, high-quality data in such large-scale studies is challenging because of normal variations in instrumentation performance over time, as well as artifacts introduced by the samples themselves, such as those due to collection, storage and processing. Existing quality control methods for proteomics data primarily focus on post-hoc analysis to remove low-quality data that would degrade downstream statistics; they are not designed to evaluate the data in near real-time, which would allow for interventions as soon as deviations in data quality are detected. In addition to flagging analyses that demonstrate outlier behavior, evaluating how the data structure changes over time can aide in understanding typical instrument performance or identify issues such as a degradation in data quality due to the need for instrument cleaning and/or re-calibration. To address this gap for proteomics, we developed Quality Control Analysis in Real-Time (QC-ART), a tool for evaluating data as they are acquired in order to dynamically flag potential issues with instrument performance or sample quality. QC-ART has similar accuracy as standard post-hoc analysis methods with the additional benefit of real-time analysis. We demonstrate the utility and performance of QC-ART in identifying deviations in data quality due to both instrument and sample issues in near real-time for LC-MS-based plasma proteomics analyses of a sample subset of The Environmental Determinants of Diabetes in the Young cohort. We also present a case where QC-ART facilitated the identification of oxidative modifications, which are often underappreciated in proteomic experiments.
This report presents the Applied Meteorology Unit's evaluation of a "Hypersodar" wind profiler located on KSC adjacent to tower 412. The sodar data used for this evaluation were collected during two different periods in March 1999 and November 1998. The evaluation is performed by calculating sodar data availability as a function of height, and bias and Root Mean Square (RMS) differences of wind speed and direction between sodar and tower 313 observations at comparable heights. The RMS differences in wind speed and wind direction from sodar wind solution B at KSC range from 0.65 m s (exp. -1) - 2.04 m s (exp. -1) and 4.5 - 32.3 deg., respectively. Note that these RMS differences are not bias-corrected. The vendor claims that the accuracy of the wind measurements from the sodar is better than 0.5 m s (exp -1) in speed and 10 deg. in direction. The results of the evaluation described here suggest that such accuracy may be attainable though the data available for this comparison made it impossible to confirm the vendor's claims. The sodar was not aligned with true north and was separated by a distance of 3.5 km from tower 313 used for comparisons in this study.
Information and data from radiolysis testing to measure hydrogen (H₂) generated from hydrated oxides on aluminum exposed to ⁶⁰Co radiation were reviewed to evaluate hydrogen generation with radiation dose. Hydrogen generation rate is a primary input to the coupled thermal-chemical system model of the performance of aluminum-clad spent nuclear fuel in a dry storage canister (ASNF-in-canister) over its storage life. Hydrogen generation data and hydrogen generation rate (hydrogen generated per absorbed radiation dose, $\Delta$(H₂)/$\Delta$(dose)) were reported in two reports for radiolysis testing [1, 2]. The testing involved first-time data¹ for radiolytic yield of hydrogen from hydrated-oxides-on-aluminum substrates, and from pristine (non-corroded) aluminum substrates. The laboratory-grown hydrated-oxides-on-aluminum substrates contained a ~ 5 μm film consisting of the trihydroxide bayerite (Al(OH)₃) with amounts of the oxyhydroxide boehmite (AlOOH). These specimens were assumed to also contain physisorbed water (unquantified). The pristine substrates were flat coupons of aluminum that were ground to a 600-grit finish but were not immersed in water to grow a hydrated oxide. The pristine substrates were tested as companion specimens to provide information on hydrogen generation from material without chemisorbed water and were assumed to contain physisorbed water (unquantified).
The research and development of new algorithmic and statistical methods of outbreak detection is an ongoing research priority in the field of biosurveillance. The early detection of emergent disease outbreaks is crucial for effective treatment and mitigation. New detection methods must be compared to established approaches for proper evaluation. This comparison requires biosurveillance test data that accurately reflects the complexity of the real-world data it will be applied to. While the test and evaluation of new detection methods is best performed on real data, it is often impractical to obtain such data as it is either proprietary or limited in scope. Thus, scientists must turn to synthetic data generation to provide enough data to properly eval- uate new detection methodologies. This paper evaluates three such synthetic data sources: The WSARE dataset, the Noufilay equation-based approach, and the Project Mimic data generator.
PM{sub 2.5} air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM{sub 2.5} exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM{sub 2.5} in grid cells with a resolution of 10 km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM{sub 2.5} concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R{sup 2} of 0.95 and 0.94, respectively and PM{sub 2.5} was overestimated by WRF-Chem (R{sup 2}=0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM{sub 2.5}. Current monitoring network in North China was dense enough to provide a reliable PM{sub 2.5} prediction by interpolation technique. - Highlights: • KED and data fusion model predicted daily PM{sub 2.5} with high accuracy. • WRF-Chem performed worse in PM{sub 2.5} prediction compared with KED and data fusion. • The PM{sub 2.5} monitoring network in North China was able to support reliable PM{sub 2.5} interpolation.
Sensors on satellites provide unprecedented understanding of the Earth's climate system by measuring incoming solar radiation, as well as both passive and active observations of the entire Earth with outstanding spatial and temporal coverage. A common challenge with satellite observations is to quantify their ability to provide well-calibrated, long-term, stable records of the parameters they measure. Ground-based intercomparisons offer some insight, while reference observations and internal calibrations give further assistance for understanding long-term stability. A valuable tool for evaluating and developing long-term records from satellites is the examination of data from overlapping satellite missions. This paper addresses how the length of overlap affects the ability to identify an offset or a drift in the overlap of data between two sensors. Ozone and temperature data sets are used as examples showing that overlap data can differ by latitude and can change over time. New results are presented for the general case of sensor overlap by using Solar Radiation and Climate Experiment (SORCE) Spectral Irradiance Monitor (SIM) and Solar Stellar Irradiance Comparison Experiment (SOLSTICE) solar irradiance data as an example. To achieve a 1 % uncertainty in estimating the offset for these two instruments' measurement of the Mg II core (280 nm) requires approximately 5 months of overlap. For relative drift to be identified within 0.1 %/yr uncertainty (0.00008 W/sq m/nm/yr), the overlap for these two satellites would need to be 2.5 years. Additional overlap of satellite measurements is needed if, as is the case for solar monitoring, unexpected jumps occur adding uncertainty to both offsets and drifts; the additional length of time needed to account for a single jump in the overlap data may be as large as 50 % of the original overlap period in order to achieve the same desired confidence in the stability of the merged data set. Results presented here are directly applicable to satellite Earth observations. Approaches for Earth observations offer additional challenges due to the complexity of the observations, but Earth observations may also benefit from ancillary observations taken from ground-based and in situ sources. Difficult choices need to be made when monitoring approaches are considered; we outline some attempts at optimizing networks based on economic principles. The careful evaluation of monitoring overlap is important to the appropriate application of observational resources and to the usefulness of current and future observations.
The spatial characteristics of the data were evaluated. A program was developed to reduce the spatial distortions resulting from variable viewing distance, and geometrically adjusted data sets were generated. The potential need for some level of radiometric adjustment was evidenced by an along track band of high reflectance across different cover types in the Varian imagery. A multiple regression analysis was employed to explore the viewing angle effect on measured reflectance. Areas in the data set which appeared to have no across track stratification of cover type were identified. A program was developed which computed the average reflectance by column for each channel, over all of the scan lines in the designated areas. A regression analysis was then run using the first, second, and third degree polynomials, for each channel. An atmospheric effect as a component of the viewing angle source of variance is discussed. Cover type maps were completed and training and test field selection was initiated.
This paper describes the performance of a parallel state estimation (PSE) tool implemented using advanced computing techniques. The developed code is able to solve state estimation of large-size, practical power systems within one second. Benchmark tests against a commercial tool shows that the computational speed is 10 times faster. Benefits brought by the sub-second state estimation are also discussed.
The clumping index (CI) is a canopy structure parameter that describes the dispersion or grouping of leaves. Previously, it has been estimated based on the normalized difference between hotspot and darkspot (NDHD), which is derived from multi-angle remote sensing data. However, currently it is impossible to derive CI from NDHD for a large area at a spatial resolution finer than 275 m since such fine multi-angle data are unavailable. In this study, an algorithm of the mixed-pixel clumping index (MPCI) was implemented, and an MPCI map of China's landmass at 1 km resolution was derived from the HJ-1A/1B data at 30 m resolution. The MPCI map was compared with the previous NDHD CI derived from the moderate resolution imaging spectroradiometer (MODIS). The correlation of these two datasets was greater than 0.9, and the mean bias was approximately 0.1. Indirectly, the MPCI map was applied to an effective leaf area index (LAI) product to derive true LAI. Using the MODIS LAI product as a reference, we found that the coefficient of determination was improved from 0.72 to 0.80, and the root mean squared error was reduced from 0.53 to 0.35 m 2 /m 2 after the effective LAI is corrected by this MPCI map, suggesting that this MPCI map is comparable to the NDHD CI. Although our algorithm is currently tested at 1 km resolution, potentially, it can be applied to higher spatial resolution than 275 m for mapping LAI and carbon cycle modeling before these multi-angle data at higher resolution are available.
It is estimated that over half (~66%) of active oil and gas wells are marginally productive, producing less than 15 barrels of oil equivalent per day. This presentation showcases an evaluation of New York State compliance inspection reports and discusses the current capabilities of machine learning to identify high-risk MCWs that could be targeted during methane emission reduction efforts and future model deployments. Findings are valuable to the New York Department of Environmental Conservation and other state oil and gas regulatory agencies seeking to prioritize the spending of Methane Emission Reduction Program funds to plug marginal conventional wells with emissions.
This documentation and dataset can be used to test the performance of automated fault detection and diagnostics algorithms for buildings. The dataset was created by LBNL, PNNL, NREL, ORNL and ASHRAE RP-1312 (Drexel University). It includes data for air-handling units and rooftop units simulated with PNNL's large office building model.
The author has identified the following significant results. The S190A, S190B, and S192 photographs and imagery were studied, using standard air-photo interpretation techniques supplemented by color additive viewing and density slicing. The EREP data were found to have potential usefulness for natural resource inventory work, water quality monitoring, and land use mapping for specific problems at scales up to 1:30,000. Distinctions between forest types in North Carolina are limited to conifers, mixed conifer-hardwoods, and hardwoods. Geologic interpretation was limited to detection of lineaments; lithologic differentiation and soil group mapping have proved infeasible in North Carolina except for differentiation of wetland soils in the coastal plain. Imagery from the S192 multispectral scanner has proved to be capable of useful discriminations for vegetation and crop analysis.
This is the supplement to the fifteenth in a series of evaluated sets of rate constants and photochemical cross sections compiled by the NASA Panel for Data Evaluation. The data are used primarily to model stratospheric and upper tropospheric processes, with particular emphasis on the ozone layer and its possible perturbation by anthropogenic and natural phenomena. Copies of this evaluation are available in electronic form and may be printed from the following Internet URL: http://jpldataeval.jpl.nasa.gov/.
This is the fifteenth in a series of evaluated sets of rate constants and photochemical cross sections compiled by the NASA Panel for Data Evaluation. The data are used primarily to model stratospheric and upper tropospheric processes, with particular emphasis on the ozone layer and its possible perturbation by anthropogenic and natural phenomena. Copies of this evaluation are available in electronic form and may be printed from the following Internet URL: http://jpldataeval.jpl.nasa.gov/