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This retrieval system builds, updates, retrieves, and performs basic statistical analyses on blood, urine, and diet parameters for the M071 and M073 Skylab and Apollo experiments. This system permits data entry from cards to build an indexed sequential file. Programs are easily modified for specialized analyses.
Earth is under a constant barrage of information from space. Whether from satellites orbiting our planet, spacecraft circling Mars, or probes streaking toward the far reaches of the Solar System, NASA collects massive amounts of data from its spacefaring missions each day. NASA s Earth Observing System (EOS) satellites, for example, provide daily imagery and measurements of Earth s atmosphere, oceans, vegetation, and more. The Earth Observing System Data and Information System (EOSDIS) collects all of that science data and processes, archives, and distributes it to researchers around the globe; EOSDIS recently reached a total archive volume of 4.5 petabytes. Try to store that amount of information in your standard, four-drawer file cabinet, and you would need 90 million to get the job done. To manage the flood of information, NASA has explored technologies to efficiently collect, archive, and provide access to EOS data for scientists today and for years to come. One such technology is now providing similar capabilities to businesses and organizations worldwide.
Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'tudes spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Donnes) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40 deg incidence angle, over the 2013 - 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https:ismn.geo.tuwien.ac.at) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE),Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is 48 similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R 0.80) over the Oznet sites and worst (R 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R 0.51 and R 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.
The progress and direction of the computer industry have resulted in widespread use of dissimilar and incompatible mainframe data systems. Data collection from these multiple systems is a labor intensive task. In the past, data collection had been restricted to the efforts of personnel specially trained on each system. Information is one of the most important resources an organizations has. Any improvement in an organization's ability to access and manage that information provides a competitive advantage. This problem of data collection is compounded at NASA sites by multi-center and contractor operations. The Centralized Automated Data Retrieval System (CADRS) is designed to provide a common interface that would permit data access, query, and retrieval from multiple contractor and NASA systems. The methods developed for CADRS have a strong commercial potential in that they would be applicable for any industry that needs inter-department, inter-company, or inter-agency data communications. The widespread use of multi-system data networks, that combine older legacy systems with newer decentralized networks, has made data retrieval a critical problem for information dependent industries. Implementing the technology discussed in this paper would reduce operational expense and improve data collection on these composite data systems.
This volume provides several levels of documentation for the program module of the NASA medical directorate mini-computer storage and retrieval system. A biomedical information system overview describes some of the reasons for the development of the mini-computer storage and retrieval system. It briefly outlines all of the program modules which constitute the system.
A method is presented for using satellite measurements to interpolate vertical temperature soundings between radiosonde stations. The method finds a set of coefficients, which when multiplied by corresponding measured radiance quantities, yield zero temperature error at a radiosonde station. This derived set of coefficients is then applied to satellite radiance measurements at places between radiosonde stations. The computations show, for example, that the average absolute error in the layer 1000-800 mbs is only 0.3K when the corresponding 'minimum-information' method error was 2.9K. The method may be most applicable to measurements from geostationary satellites, but should also be applicable to measurements from polar orbiting satellites under certain conditions.
Three Nike Tomahawk rockets were launched in Red Lake, Ontario, one previous to, and two during, the total eclipse of the Sun, for the purpose of studying the atmosphere and its interaction with solar radiation. The method and preliminary results of three experiments that were used to measure solar radiation in the X-ray, Lyman-alpha, and visible parts of the spectrum are described. The instrumentation designed for this investigation is discussed as well as post-flight data processing techniques. The retrieved data were processed to some extent to verify that a valid representation of the solar radiation was obtained. The Lyman-alpha experiment yielded very good results, and preliminary data are included. The visible radiation experiment served as support for the other experiments and also functioned well. Due to a high level of energetic particles during the eclipse, the X-ray data were contaminated and are not presented. However, satellite observations indicate no appreciable level of X-rays from the Sun during the time of the eclipse.
The succession of US and international Earth observing satellites that follow each other, seconds to minutes apart, across the local afternoon equator crossing is called the ATrain. The A-Train consists of the following satellites, in order of equator crossing: OCO, EOS Aqua, CloudSat, CALIPSO, PARASOL, and EOS Aura. Flying in such formation increases the number of observations, validates observations, and enables coordination between science observations, resulting in a more complete virtual science platform (Kelly, 2000). The goal of this project is to create the first ever A-Train virtual data portal/center, the A-Train Data Depot (ATDD), to process, archive, access, visualize, analyze and correlate distributed atmosphere measurements from various A-Train instruments along A-Train tracks. The ATDD will enable the free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. Once the infrastructure of the ATDD is in place, it will be easily evolved to serve data from all A-Train data measurements: one stop shopping. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) will be accommodated by reusing the GSFC Atmospheric Composition Data and Information Services Center (ACDISC) visualization and analysis tool, GIOVANNI, existing data reduction tools, on-line archiving for fast data access, access to remote data without unnecessary data transfers, and data retrieval by users finding data desirable for further study. Initial measurements utilized include CALIPSO lidar backscatter, CloudSat radar reflectivity, clear air relative humidity, water vapor and temperature from AIRS, and cloud properties and aerosols from both MODIS. This will be foilowed by associated measurements from TVILS, =MI, HIRDLS, sad TES. Given the independent nature of instrumentlplatform development, the ATDD project has been met with many interesting challenges that, once resolved, will provide a much greater understanding of the relative flight dynamics and data co-registration of the suite of A-Train instruments, thus greatly increasing the accuracy of A-Train data analysis. Some of these challenges will be illustrated and discussed. The project's early visualizations and analysis efforts illustrate the importance of managing data so that measurements from various missions can be combined to enhance the understanding of the atmosphere. A-Train data management coordination, as performed here, is extremely significant in facilitating the A-Train science of clouds, precipitation, aerosol and chemistry.
The present invention provides a network device interface and method for digitally connecting a plurality of data channels to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. In one embodiment, the bus controller transmits messages to the network device interface containing a plurality of bits having a value defined by a transition between first and second states in the bits. The network device interface determines timing of the data sequence of the message and uses the determined timing to communicate with the bus controller.
A maximum a posteriori Bayesian Classifier is used to perform a supervised classification of multifrequency, polariemtric, airborne, SAR obserations of boreal forests from the Bonanza Creek Experimental Forest, near Fairbanks, AK, into six Categories.
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The effects of the inclusion of satellite temperature retrieval data, and of different satellite retrieval methods, on forecasts made with the NASA Goddard Laboratory for Atmospheres (GLA) fourth-order model were investigated using, as the parameter, the available potential energy (APE) in its isentropic form. Calculation of the APE were used to study the differences in the forecast sets both globally and in the Northern Hemisphere during 72-h forecast period. The analysis data sets used for the forecasts included one containing the NESDIS TIROS-N retrievals, the GLA retrievals using the physical inversion method, and a third, which did not contain satellite data, used as a control; two data sets, with and without satellite data, were used for verification. For all three data sets, the Northern Hemisphere values for the total APE showed an increase throughout the forecast period, mostly due to an increase in the zonal component, in contrast to the verification sets, which showed a steady level of total APE.
Different methods for retrieving atmospheric profiles in the presence of clouds from hyperspectral satellite remote sensing data will be described. We will present results from the JPSS cloud-clearing algorithm and NASA Langley cloud retrieval algorithm.
The literature on high pressure oxygen compatibility has been surveyed in order to present the existing state of knowledge. Searches have been conducted of NASA and NBS data retrieval systems. In addition, many individuals, active in the field, were contacted in order to retrieve useful unpublished information. Compatibility data, such as mechanical impact, pneumatic impact, ignition temperature, and flash-and-fire point, were compiled for pressures above 200 bar. Lower pressure data were included if they were useful for extrapolation to pressures above 200 bar. These data, too numerous to be given here, are available from the authors. Brief descriptions of the trends of these data are given. Recommendations for additional high pressure studies are included.
The author has identified the following significant results. The integration of the available methods provided the analyst with the unified scanner analysis package (USAP), the flexibility and versatility of which was superior to many previous integrated techniques. The USAP consisted of three main subsystems; (1) a spatial path, (2) a spectral path, and (3) a set of analytic classification accuracy estimators which evaluated the system performance. The spatial path consisted of satellite and/or aircraft data, data correlation analyzer, scanner IFOV, and random noise model. The output of the spatial path was fed into the analytic classification and accuracy predictor. The spectral path consisted of laboratory and/or field spectral data, EXOSYS data retrieval, optimum spectral function calculation, data transformation, and statistics calculation. The output of the spectral path was fended into the stratified posterior performance estimator.
A generalized dynamical adjustment procedure was applied to a diabatic model to produce balanced initial conditions. Namely, backward adiabatic model integration is followed by forward diabatic model integration, with a high frequency (low pass) filter in the form of the Euler-backward time differencing scheme being applied throughout the whole integration. As a result of the application of such a diabatic dynamic initialization procedure within the Goddard Laboratory for Atmospheres (GLA) 4-D data assimilation system, the following properties of forecasts from initialized fields are achieved right from the beginning of the usual forecast integration: (1) the forecast tendencies (and fields) are free of any noise due to imbalance in initial conditions; (2) the shocks related to an initial imbalance between model physics and dynamics, and especially the substantial initial imbalance of precipitation and evaporation fields, or the initial spin-up effect, are practically removed. Diabatic dynamic initialization has been compared with implicit nonlinear normal mode initialization, and found to be superior in removing the initial spin-up effect, and also in improving the tropical structure. The diabatic dynamic initialization procedure was successfully tested for the GLA system with the use of all conventional data and the GLA satellite data retrievals. It allows a smooth data insertion without any shocks or imbalances, which is highly desirable for efficient functioning of 4-D data assimilation systems. The developed initialization procedure is computationally efficient and in principle easily applicable to different forecast models.
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.
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.