Improved Aura/OMI Solar Spectral Irradiances: Comparisons With Independent Data Sets and Model Predictions
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The Pilot Climate Data System (PCDS) was developed by the Information Management Branch of NASA's Goddard Space Flight Center to manage a large collection of climate-related data of interest to the research community. The PCDS now provides uniform data catalogs, inventories, access methods, graphical displays and statistical calculations for selected NASA and non-NASA data sets. Data manipulation capabilities were developed to permit researchers to easily combine or compare data. The current capabilities of the PCDS include many tools for the statistical survey of climate data. A climate researcher can examine any data set of interest via flexible utilities to create a variety of two- and three-dimensional displays, including vector plots, scatter diagrams, histograms, contour plots, surface diagrams and pseudo-color images. The graphics and statistics subsystems employ an intermediate data storage format which is data-set independent. Outside of the graphics system there exist other utilities to select, filter, list, compress, and calculate time-averages and variances for any data of interest. The PCDS now fully supports approximately twenty different data sets and is being used on a trial basis by several different in-house research grounds.
We present ground‐based, advected aircraft engine emissions from flights taking off at Los Angeles International Airport. 275 discrete engine take‐off plumes were observed on 18 and 25 May 2014 at a distance of 400 m downwind of the runway. CO2 measurements are used to convert the aerosol data into plume‐average emissions indices that are suitable for modelling aircraft emissions. Total and non‐volatile particle number EIs are of order 1016‐1017 kg‐1 and 1014‐1016 kg‐1, respectively. Black‐carbon‐equivalent particle mass EIs vary between 175‐941 mg kg‐1 (except for the GE GEnx engines at 46 mg kg‐1). Aircraft tail numbers recorded for each take‐off event are used to incorporate aircraft‐ and engine‐specific parameters into the data set. Data acquisition and processing follow standard methods for quality assurance. A unique aspect of the data set is the mapping of aerosol concentration time series to integrated plume EIs, aircraft and engine specifications, and manufacturer‐reported engine emissions certifications. The integrated data enable future studies seeking to understand and model aircraft emissions and their impact on air quality.
This contract covered work in data management, acquisition, and analysis for the Earth Science and Applications Division (ESAD) of the Space Science Laboratory of MSFC. Under this contract, a naming convention and data management system were developed for ESAD data sets, data visualization, and standard data format issues were investigated, and specific data analysis and management needs were addressed for several ESAD projects and data sets. In the following sections, details of the research performed over the duration of this contract are summarized.
Microwave imaging techniques, an integral component of nondestructive testing and evaluation (NDTE), have received significant attention in the past decade. These techniques have included the implementation of synthetic aperture focusing (SAF) algorithms for obtaining high spatial resolution images. The next important step in these developments is the implementation of 3-D holographic imaging algorithms. These are well-known wideband imaging technique requiring a swept-frequency (i.e., wideband), which unlike SAF that is a single frequency technique, are not easily performed on a real-time basis. This is due to the fact that a significant number of data points (in the frequency domain) must be obtained within the frequency band of interest. This not only makes for a complex imaging system design, it also significantly increases the image-production time. Consequently in an attempt to reduce the measurement time and system complexity, an investigation was conducted to determine the minimum required number of frequency samples needed to image a specific object while preserving a desired maximum measurement range and range resolution. To this end the 3-D holographic algorithm was modified to use properlyinterpolated frequency data. Measurements of the complex reflection coefficient for several samples were conducted using a swept-frequency approach. Subsequently, holographical images were generated using data containing a relatively large number of frequency samples and were compared with images generated by the reduced data set data. Quantitative metrics such as average, contrast, and signal-to-noise ratio were used to evaluate the quality of images generated using reduced data sets. Furthermore, this approach was applied to both weakly- and strongly-scattering indications. This paper presents the methods used and the results of this investigation.
A convenient reference to space science and supportive data available from the National Space Science Data Center (NSSDC) is provided. Satellite data are organized by NSSDC spacecraft common name. The launch date and NSSDC ID are given. Experiments are listed alphabetically by the principal investigator or team leader. The experiment name and NSSDC ID, data set ID, data set name, data form code, quantity of data, and the time span of the data as verified by NSSDC are shown. Ground-based data, models, computer routines, and composite spacecraft data that are available from NSSDC are listed alphabetically by discipline, source, data type, data content, and data set. The data set name, data form code, quantity of data, and the time span covered where appropriate are included.
The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s Precipitation Best-Estimate (PrecipBE) Value-Added Product integrates multiple precipitation datastreams, accounting for data quality and instrument limitations, to deliver comprehensive per-precipitation event properties alongside ancillary ARM data set data. PrecipBE bundles all valid surface rainfall samples into artificial intelligence (AI)-ready tabular and time-series formats, reporting bundle means and uncertainty ranges. This per-event structure provides an insightful and easy-to-use resource for researchers analyzing precipitation characteristics.
A relationship between a person's lean body mass and the amount of maximum torque that can be produced with each isolated joint of the upper extremity was investigated. The maximum dynamic isolated joint torque (upper extremity) on 14 subjects was collected using a dynamometer multi-joint testing unit. These data were reduced to a table of coefficients of second degree polynomials, computed using a least squares regression method. All the coefficients were then organized into look-up tables, a compact and convenient storage/retrieval mechanism for the data set. Data from each joint, direction and velocity, were normalized with respect to that joint's average and merged into files (one for each curve for a particular joint). Regression was performed on each one of these files to derive a table of normalized population curve coefficients for each joint axis, direction, and velocity. In addition, a regression table which included all upper extremity joints was built which related average torque to lean body mass for an individual. These two tables are the basis of the regression model which allows the prediction of dynamic isolated joint torques from an individual's lean body mass.
The proposed research involves the application of multispectral satellite data in combination with ground truth measurements to monitor surface properties of the Greenland ice sheet which are essential for describing the energy and mass of the ice sheet. Several key components of the energy balance are parameterized using satellite data and in situ measurements. The analysis will be done for a ten year time period in order to get statistics on the seasonal and interannual variations of the surface processes and the climatology. Our goal is to investigate to what accuracy and over what geographic areas large scale snow properties and radiative fluxes can be derived based upon a combination of available remote sensing and meterological data sets. Data analysis showed the following results: (1)cloud classification based on longwave sky radiation revealed that overcast sky occurred for 25% of the time in winter, and for 15% in spring and summer respectively (winter and summer both show the same occurrence of clear sky of approximately 26%); (2) comparison of aerodynamic profile method with eddy correlation method to derive sensible and latent heat flux showed good agreement in the diurnal cycle and the turbulent fluxes were underestimated with the aerodynamic method by 10 - 30% as compared to the in situ eddy flux method; (3) the katabatic wind shows a distinct diurnal cycle with a maximum in the morning (7-9 h solar time) and a minimum in the later afternoon (18 h solar time); (4) snow grain size was modeled with a surface energy balance model (SNTHERM) and compared with in situ measurements. Sharp decreases in the modeled snow grain size, caused by accumulation events such as precipitation and deposition, could be verified with observational data; (4) radiative transfer modeling of firn supports our beliefs that the observed trends in 18 and 19 GHz passive microwave brightness temperatures are attributable to accumulation rate changes (modeling also indicates the above relationship is detectable because of the presence of depth hoar; (5) snow melt can be detected by a distinct signal in the passive microwave cross-polarized gradient ratio (19h-37v)/(19h+37v) and has been used for wet/dry snow classification; (6) top of the atmosphere (TOA) broadband albedos were derived from AVHRR visible and near infrared reflectances for the entire ice sheet from May 1990 - June 1991, and the highest albedo values are found along the southeast coast of the ice sheet which is consistent with the summer peak of precipitation due to onshore flow loaded with high water vapor content (TOA albedo values dropped to around 40% along the south-western coast during July and August due to bare ice surfaces); and (7) the net all-wave radiation balance at the top of the atmosphere is negative over the entire ice sheet except for the summer months - June-July-August (in June, the net radiation balance is slightly positive over the dry snow areas--15 W/m2).
Data mining is the automatic discovery of patterns, associations, and anomalies in data sets. Data mining requires numerically and statistically intensive queries. Our assumption is that data mining requires a specialized data management infrastructure to support the aforementioned intensive queries, but because of the sizes of data involved, this infrastructure is layered over a hierarchical storage system. In this paper, we discuss the architecture of a system which is layered for modularity, but exploits specialized lightweight services to maintain efficiency. Rather than use a full functioned database for example, we use light weight object services specialized for data mining. We propose using information repositories between layers so that components on either side of the layer can access information in the repositories to assist in making decisions about data layout, the caching and migration of data, the scheduling of queries, and related matters.
An ongoing NASA-funded Data Rods (time series) project has demonstrated the removal of a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series) for selected variables of the North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) and other NASA data sets. Data rods are pre-generated or generated on-the-fly (OTF), leveraging the NASA Simple Subset Wizard (SSW), a gateway to NASA data centers. Data rods Web services are accessible through the CUAHSI Hydrologic Information System (HIS) and the Goddard Earth Sciences Data and Information Services Center (GES DISC) but are not easily discoverable by users of other non-NASA data systems. An ongoing GEOSS Water Services project aims to develop a distributed, global registry of water data, map, and modeling services cataloged using the standards and procedures of the Open Geospatial Consortium and the World Meteorological Organization. Preliminary work has shown GEOSS can be leveraged to help provide access to data rods. A new NASA-funded project is extending this early work.
A representative set of data from ISIS 2 covering a range of operating modes and geophysical conditions is presented. The data show the typical values and range of ionospheric and magnetospheric characteristics, as viewed from 1400 km with the ISIS 2 instruments. The definition of each data set depends partly on geophysical parameters and partly on satellite operating mode. Preceding the data set is a description of the organizational parameters and a review of the objectives and general characteristics of the data set. The data are shown as a selection from 12 different data formats. Each data has a different selection of formats, but uniformity of a given format selection is preserved throughout each data set.
The main focus of our work was to solidify underlying data sets, the data processing tools and the modeling environment needed to perform a series of long-term global and regional hydrological simulations leading eventually to routine hydrometeorological predictions. A water and energy budget synthesis was developed for the Mississippi River Basin (Roads et al. 2003), in order to understand better what kinds of errors exist in current hydrometeorological data sets. This study is now being extended globally with a larger number of observations and model based data sets under the new NASA NEWS program. A global comparison of a number of precipitation data sets was subsequently carried out (Fekete et al. 2004) in which it was further shown that reanalysis precipitation has substantial problems, which subsequently led us to the development of a precipitation assimilation effort (Nunes and Roads 2005). We believe that with current levels of model skill in predicting precipitation that precipitation assimilation is necessary to get the appropriate land surface forcing.
SDMS is a data base management system developed specifically to support scientific programming applications. It consists of a data definition program to define the forms of data bases, and FORTRAN-compatible subroutine calls to create and access data within them. Each SDMS data base contains one or more data sets. A data set has the form of a relation. Each column of a data set is defined to be either a key or data element. Key elements must be scalar. Data elements may also be vectors or matrices. The data elements in each row of the relation form an element set. SDMS permits direct storage and retrieval of an element set by specifying the corresponding key element values. To support the scientific environment, SDMS allows the dynamic creation of data bases via subroutine calls. It also allows intermediate or scratch data to be stored in temporary data bases which vanish at job end.
The concept of the sphere quadtree (SQT) is introduced to enable the structuring of spherically distributed data to be consistent with its geometry and facilitate mapping of the data onto a flat file system. The SQT is based on the recursive subdivision of the spherical triangles that result from the projection of the faces of an icosahedron onto a sphere. The SQT concept is insensitive to the distortions that occur far from the equator in spherically distributed data sets. Geographic data can be shown at several levels and at any resolution, allowing a system of referencing between data sets of different resolutions as well as data that are not geographically registered. SQTs are found to facilitate the search for particular spherically distributed data sets and improve the efficiency of surface rendering algorithms.
Satellite and nonsatellite data available from the National Space Science Data Center are listed. The Satellite Data listing includes the spacecraft name, launch date, and an alphabetical list of experiments. The Non-Satellite Data listing contains ground based data, models, computer routines, and composite spacecraft data. The data set name, data form code, quantity of data, and the time space covered are included in the data sets of both listings where appropriate. Geodetic tracking data sets are also included.
This activity focuses on Lightning Imaging Sensor (LIS)/Lightning Mapper Sensor (LMS) algorithm development and applied research. Specifically we are exploring the relationships between (1) global and regional lightning activity and rainfall, and (2) storm electrical development, physics, and the role of the environment. U.S. composite radar-rainfall maps and ground strike lightning maps are used to understand lightning-rainfall relationships at the regional scale. These observations are then compared to SSM/I brightness temperatures to simulate LIS/TRMM multi-sensor algorithm data sets. These data sets are supplied to the WETNET project archive. WSR88-D (NEXRAD) data are also used as it becomes available. The results of this study allow us to examine the information content from lightning imaging sensors in low-earth and geostationary orbits. Analysis of tropical and U.S. data sets continues. A neural network/sensor fusion algorithm is being refined for objectively associating lightning and rainfall with their parent storm systems. Total lightning data from interferometers are being used in conjunction with data from the national lightning network. A 6-year lightning/rainfall climatology has been assembled for LIS sampling studies.