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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Improved Satellite Retrievals of NO2 and SO2 over the Canadian Oil Sands and Comparisons with Surface Measurements

Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km × 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005-2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of greater than 0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals.

surface concentration↗
NASA NTRS1993

Extraction of auxiliary data from AVIRIS distribution tape for spectral, radiometric, and geometric quality assessment

Remotely sensed data are affected by system (sensor and platform), and scene related effects. For quantitative investigations the spectral, radiometric characteristics of the system and scene have to be known. The relevant effects and their possible influence on an image have to be specifically determined for every remote sensing system and adequate description parameters need to be updated and reported on a regular basis as they are carried out, e.g., for the AVIRIS system. It is evident that the strength of the influence of similar effects in very dependent on the accessibility of auxiliary information about such sensor systems. Degradation in a spaceborne system can normally be just reported and cannot be corrected. In contrast, an airborne sensor can be evaluated, maintained and improved periodically. Such maintenance efforts are particularly important because airborne systems are exposed to extreme and changing environments. These include tens of takeoffs and landing each year as well as extreme changes in temperature and humidity on the tarmac and in flight. For the AVIRIS system there are environmental stresses such as changes in temperature, air pressure, humidity, vibration of the platform or scene-related reasons like atmospheric conditions, and topography. The information contained in the auxiliary files included with the AVIRIS data can be used to assess these effects and compensate for them. In addition the spectral, radiometer and geometric calibration data contained in the auxiliary file are required for quantitative analysis of the data. The paper describes tools to access the auxiliary information that characterizes the AVIRIS system. These tools allow the examination of parameters that may impact the quality of the measured AVIRIS image. An example of the use of this auxiliary data was carried out with regard to a parametric geocoding approach. Emphasis is placed on the reported auxiliary information that describes the geometric character of the AVIRIS data in 1991. Results are presented using data from the AVIRIS flight #910705, run 6 and 7 of the NASA MAC Europe 1991 campaign in a test site in Central Switzerland.

Meyer, Peter↗
NASA NTRS2018

Quantitative Daily Maps of PM 2.5 Episodes for California and Other Regions: Satellite Column Water and Optical Depth as Allied Tracers of Dilution

The Western US and many regions globally present daunting difficulties in understanding PM 2.5 episodes. We evaluate extensions of a method independent of modeled source-description and transport/transformation and using several satellite remote sensing products from imaging spectrometers. The San Joaquin Valley (SJV) especially suffers few-day episodes due to shallow mixing; PM 2.5 retrieval suffers low satellite AOT (Aerosol Optical Thickness) and bright surfaces.Nevertheless, we find residual errors in our maps of of typically 5-8 micrograms per cubic meter. Episodes in the Valley reaching 60-100 micrograms per cubic meter. These maps detail pollution from Interstate 5 at the scale of a few kilometers. The maps are based on NASA's MODerate resolution Imaging Spectrometer (MODIS) data at circa 1 kilometer as processed with the Multi-Angle Implementation of Atmospheric Correction. The Bay Area Air Quality Management District has requested that we test our methods in their challenging environment characterized by multiple sub-basins defined by complex topography. Our tests suggest that nearly similar precision may be expected for wintertime conditions with high PM 2.5 . We note difficulties when measured PM 2.5 is less than 8-10 micrograms per cubic meter, but good relative precision when PM 2.5 rises above 20; i.e. in episodes of concern for morbidity and mortality. Our method stresses physically meaningful functions of MODIS-MAIAC (Multi-Angle Implementation of Atmospheric Correction)-derived AOD (Aerosol Optical Depth) and total water vapor column. A mixed-effects statistical model exploiting existing station data works powerfully to allow us daily AOT-to-PM 2.5 relationships that allow a calibration of the map. In those cases where water vapor and particles have generally similar surface sources, using the ratio of AOT / Column_water can improve the daily calibrations so as to reach our quoted precision. We briefly present some cartoon idealizations that explain this success and also the likely reasons that our mixed effects model (or "daily calibration") works; also when it should not work. The combined satellite/mixed-effects model works best for wintertime San Joaquin Valley episodes, where the meteorology of particle and H2O(v) dilution is quite appropriate. We extended and tested the methodology (a) for the Bay Area wintertime situations and (b) for smoke plume events (e.g. the October 2017 fire events of the Sonoma area). Our SJV work was evaluated using NASA's DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) airborne measurements, and by season- long measurements in Fresno. If the composition and size distribution of the aerosols can be assessed for the regions we describe, retrievals should have improved accuracy.

Chatfield, Robert B.↗
NASA NTRSDate not supplied

Space-Based Lidar Observations of the 3D Structure of the Earth System

Lidar provides precise measurements of the three-dimensional structure of the clouds, aerosols, ocean/land/snow/ice surfaces, as well as ocean subsurface. Lidar also provides unique information about physical propertiesof particulates in the atmosphere for both radiative transfer and air quality applications.In this talk, I will present an overview of our recent studies of aerosols, clouds, ocean and snow using space-based lidar measurements (e.g., LITE, CALIPSO and ICESat-2), such as classifications of aerosols and thermodynamics phase of clouds, cloud microphysical properties, snow depths and phytoplankton biomass. I will also introduce a new concept of 3D Earth system observations with data fusion though combined active/passive remote sensing and machine learning. The new concept aims to reveal vertical structure of the aerosols/clouds/surfaces/subsurface from passive sensors by taking advantage of lidar measurements to effectively resolve the vertical structureby unscrambling the highly convoluted multi-angle, spectral and polarization information from passive sensors and apply the knowledge to a large swath where lidar measurements are not available.

Ali Omar↗
NASA NTRS2011

Using Satellite Aerosol Retrievals to Monitor Surface Particulate Air Quality

The MODIS and MISR aerosol products were designed nearly two decades ago for the purpose of climate applications. Since launch of Terra in 1999, these two sensors have provided global, quantitative information about column-integrated aerosol properties, including aerosol optical depth (AOD) and relative aerosol type parameters (such as Angstrom exponent). Although primarily designed for climate, the air quality (AQ) community quickly recognized that passive satellite products could be used for particulate air quality monitoring and forecasting. However, AOD and particulate matter (PM) concentrations have different units, and represent aerosol conditions in different layers of the atmosphere. Also, due to low visible contrast over brighter surface conditions, satellite-derived aerosol retrievals tend to have larger uncertainty in urban or populated regions. Nonetheless, the AQ community has made significant progress in relating column-integrated AOD at ambient relative humidity (RH) to surface PM concentrations at dried RH. Knowledge of aerosol optical and microphysical properties, ambient meteorological conditions, and especially vertical profile, are critical for physically relating AOD and PM. To make urban-scale maps of PM, we also must account for spatial variability. Since surface PM may vary on a finer spatial scale than the resolution of standard MODIS (10 km) and MISR (17km) products, we test higher-resolution versions of MODIS (3km) and MISR (1km research mode) retrievals. The recent (July 2011) DISCOVER-AQ campaign in the mid-Atlantic offers a comprehensive network of sun photometers (DRAGON) and other data that we use for validating the higher resolution satellite data. In the future, we expect that the wealth of aircraft and ground-based measurements, collected during DISCOVER-AQ, will help us quantitatively link remote sensed and ground-based measurements in the urban region.

Levy, Robert C.↗
NASA NTRS1981

The use of LANDSAT by the states for water quality assessment

The use of LANDSAT for water resources applications in U.S. state demonstration projects is reviewed. The most common single application undertaken between 1976 and 1981 was found to be water condition assessment. The majority of projects, however, fell into the general category of watershed hydrology. Some of the states are attempting to use LANDSAT data in an operational mode for water quality assessment. Two of these state projects from Vermont and Wisconsin are described in brief. The basic information requirements of Section 314 of the U.S. Pollution Control Act are given with the type of input LANDSAT data could potentially provide toward those requirements. Surveys were performed to determine: (1) how its states were responding in 1980 to 1981 to the federal and state water quality laws; and (2) the status of LANDSAT analysis capabilities of each state.

Middleton, E. M.↗
NASA NTRS2007

Cabin Air Quality On Board Mir and the International Space Station: A Comparison

The maintenance of the cabin atmosphere aboard spacecraft is critical not only to its habitability but also to its function. Ideally, air quality can be maintained by striking a proper balance between the generation and removal of contaminants. Both very dynamic processes, the balance between generation and removal can be difficult to maintain and control because the state of the cabin atmosphere is in constant evolution responding to different perturbations. Typically, maintaining a clean cabin environment on board crewed spacecraft and space habitats is the central function of the environmental control and life support (ECLS) system. While active air quality control equipment is deployed on board every vehicle to remove carbon dioxide, water vapor, and trace chemical components from the cabin atmosphere, perturbations associated with logistics, vehicle construction and maintenance, and ECLS system configuration influence the resulting cabin atmospheric quality. The air-quality data obtained from the International Space Station (ISS) and NASA-Mir programs provides a wealth of information regarding the maintenance of the cabin atmosphere aboard long-lived space habitats. A comparison of the composition of the trace chemical contaminant load is presented. Correlations between ground-based and in-flight operations that influence cabin atmospheric quality are identified and discussed, and observations on cabin atmospheric quality during the NASA-Mir expeditions and the International Space Station are explored.

Macatangay, Ariel↗
NASA NTRS1993

NASA metrology and calibration, 1993

Th sixteenth annual workshop of NASA's Metrology and Calibration Working Group was held April 20-22, 1993. The goals of the Working Group are to provide Agencywide standardization of individual metrology programs, where appropriate; to promote cooperation and exchange of information within NASA, with other Government agencies, and with industry; to serve as the primary Agency interface with the National Institute of Standards and Technology; and to encourage formal quality control techniques such as Measurement Assurance Programs. These proceedings contain unedited reports and presentations from the workshop and are provided for information only.

Source record↗
NASA NTRS1978

A frequency domain approach to handling qualities design

A method for designing linear multivariable feedback control systems based on desired closed loop transfer matrix information is introduced. The technique which was employed to achieve the final design was based on a theoretical result, known as the structure theorem. The structure theorem was a frequency domain relationship which simplified the expression for the transfer matrix (matrix of transfer functions) of a linear time-invariant multivariable system. The effect of linear state variable feedback on the closed loop transfer matrix of the system was also clarified.

Wolovich, W. A.↗
NASA NTRS2004

Simulation Based Acquisition for NASA's Office of Exploration Systems

In January 2004, President George W. Bush unveiled his vision for NASA to advance U.S. scientific, security, and economic interests through a robust space exploration program. This vision includes the goal to extend human presence across the solar system, starting with a human return to the Moon no later than 2020, in preparation for human exploration of Mars and other destinations. In response to this vision, NASA has created the Office of Exploration Systems (OExS) to develop the innovative technologies, knowledge, and infrastructures to explore and support decisions about human exploration destinations, including the development of a new Crew Exploration Vehicle (CEV). Within the OExS organization, NASA is implementing Simulation Based Acquisition (SBA), a robust Modeling & Simulation (M&S) environment integrated across all acquisition phases and programs/teams, to make the realization of the President s vision more certain. Executed properly, SBA will foster better informed, timelier, and more defensible decisions throughout the acquisition life cycle. By doing so, SBA will improve the quality of NASA systems and speed their development, at less cost and risk than would otherwise be the case. SBA is a comprehensive, Enterprise-wide endeavor that necessitates an evolved culture, a revised spiral acquisition process, and an infrastructure of advanced Information Technology (IT) capabilities. SBA encompasses all project phases (from requirements analysis and concept formulation through design, manufacture, training, and operations), professional disciplines, and activities that can benefit from employing SBA capabilities. SBA capabilities include: developing and assessing system concepts and designs; planning manufacturing, assembly, transport, and launch; training crews, maintainers, launch personnel, and controllers; planning and monitoring missions; responding to emergencies by evaluating effects and exploring solutions; and communicating across the OExS enterprise, within the Government, and with the general public. The SBA process features empowered collaborative teams (including industry partners) to integrate requirements, acquisition, training, operations, and sustainment. The SBA process also utilizes an increased reliance on and investment in M&S to reduce design risk. SBA originated as a joint Industry and Department of Defense (DoD) initiative to define and integrate an acquisition process that employs robust, collaborative use of M&S technology across acquisition phases and programs. The SBA process was successfully implemented in the Air Force s Joint Strike Fighter (JSF) Program.

Hale, Joe↗
NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗
NASA NTRS1990

The FIFE information system

A description is given of the FIFE (First ISLSCP Field Experiment) information system, which was developed to serve FIFE investigators as a tool for designing the experiment and for organizing and manipulating the complex data set. Fulfilling these functions on an experiment-driven timeline led to abandoning the classical sequential development paradigm of software engineering in favor of a more responsive and broadly based approach. The design, development, and operation of the information system supporting the experiment had to be flexible and under direct day-to-day control of scientist/users. Because of the organization around scientific requirements, the system was able to incorporate diverse data types in a systematic way as they became available, to add scientific rigor by identifying data gaps at an early stage, and to provide real-time quality assurance. These factors are important for designing and building future databases and long-term information systems to support interdisciplinary scientific research.

Strebel, Donald E.↗
NASA NTRS2017

Development of Gridded Innovations and Observations Supplement to MERRA-2

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths can be known, drift in certain instruments and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are in observation-space data formats and have not been made easily available to reanalysis users.A test data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). We refer to this proof-of-concept data as the MERRA-2 Gridded Innovations and Observations (GIO). In this paper, we present the data format and its strengths and limitations with some initial testing and validation of the methodology.

SSMI↗
NASA NTRS2018

Development of Gridded Innovations and Observations Supplement to MERRA-2

Atmospheric reanalysis have become an important source of data for weather and climate research, owing to the continuity of the data, but especially because of the multitude of observational data included (radiosondes, commercial aircraft, retrieved data products and radiances). However, the presence of assimilated observations can vary based on numerous factors, and so it is difficult or impossible for a researcher to say with any degree of certainty how many and what type of observations contributed to the reanalysis data they are using at any give point in time or space, or their contribution to the eventual analyzed fields. For example, quality control, transmission interruptions, and station outages can occasionally affect data availability. While orbital paths are known, drift in certain instruments, cloud clearing, and the large number of available instruments makes it challenging to know which satellite is observing any region at any point in the diurnal cycle. Furthermore, there is information from the statistics generated by the data assimilation that can help understand the model and the quality of the reanalysis. Typically, the assimilated observations and their innovations are saved in observation-space data formats and are not easily made available to reanalysis users.Here, we present an early version of a data set has been developed to make the MERRA-2 assimilated observations available for rapid and general use, by simplifying the data format. The observations are binned to a grid similar as MERRA-2 and saved as netCDF. This data collection includes the mean and number of observations in the bin as well as its variance. The data will also include the innovations from the data assimilation, the forecast departure and the analysis increment, as well as bias correction (for satellite radiances). In this paper, we present the data format (called MERRA-2 Gridded Innovations and Observations or GIO) and its strengths and limitations with some initial testing and validation of the methodology.

Bosilovich, Michael G.↗
NASA NTRS1971

Saturn S-2 problem resolution history report

A summary of S-2 Program problems and the solutions that were implemented is presented. The problems occurred during a period starting with the initial design concepts and continuing through the launch of the tenth S-2 flight stage information is from nine separate disciplines: design, facilities, logistics, manufacturing, material, program management, quality assurance, safety, and tests.

Virgil, F. W.↗
NASA NTRS1972

Land use management in Minnesota

The author has identified the following significant results. Preliminary analysis of bulk imagery suggests that the forty-acre data cell used in the Minnesota Land Management Information Systems (MLMIS) can be utilized in interpretation of ERTS-1 data. High quality bulk images of the Twin Cities metropolitan area suggest that detail in urban land use patterns is much greater than originally anticipated. This implies a greater work effort in this area than was planned. Furthermore, the forest classes of land use can also be usefully divided into subcategories. Preliminary analysis of one rather low quality image also indicates that subclasses of wetlands can be identified. Prospects are bright for improving the potential detail that ERTS-1 can contribute to MLMIS.

Sizer, J. E.↗