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NASA NTRS1986

Central On-Line Data Directory

The National Space Science Data Center (NSSDC) Central On-Line Data Directory (CODD), which allows the general scientist remote access to information about data sets available not only at NSSDC, but throughout the scientific community, is discussed. A user may search for data set information within CODD by specifying spacecraft name, experiment name, investigator name, and/or keywords. CODD will include information on atmospheric science data sets contained not only within the PCDS, but also within other data sets that are deemed important. Keywords to be used in locating these data sets are currently being formulated. The main type of keyword to be used for categorization of data sets will be discipline related. The primary discipline keyword for PCDS-type data sets would be ATMOSPHERIC SCIENCE. A good set of subdiscipline keywords is needed under this discipline to subdivide the data sets. A sheet containing a strawman set of subdiscipline keywords was distributed, and a request was made for the knowledgeable scientists to modify or replace the proposed keywords.

Thieman, J.↗
NASA NTRS1999

The Impact of TRMM on Mesoscale Model Simulation of Super Typhoon Paka

Tropical cyclone Paka formed during the first week of December 1997 and underwent three periods of rapid intensification over the following two weeks. During one of these periods, which initiated early on December 10, Paka's Dvorak-measured windspeed increased from 23 to 60 m/s over a 48-hr period. On December 18, during the last rapid deepening episode, Paka became a supertyphoon with a maximum wind speed of about 80 m/s. In this study, the Penn State/NCAR Mesoscale Model (MM5) with improved physics (i.e., cloud microphysics, radiation, land-soil-vegetation-surface processes, and TOGA COARE flux scheme) and a multiple level nesting technique (135, 45 and 15 km horizontal resolution) will be used to simulate supertyphoon Paka. We performed two runs initialized with Goddard Earth Observing System (GEOS) data sets. The first GEOS data set does not incorporate either TRMM (tropical rainfall measuring mission satellite) or SSM/I (sensor microwave imager) observed rainfall fields into the GEOS's assimilation system while the second one does. Preliminary results show that the MM5 simulated surface pressure deepened by more than 25 mb (45 km resolution domain) in the run initialized with the GEOS data set incorporating TRMM and SSM/I derived rainfall, compared to the one initialized without. However, the track and precipitation patterns are quite similar between the runs. In our presentation, we will show the impact of TRMM rainfall upon the MM5 simulation of Paka at various horizontal resolutions. We will also examine the physical processes associated with initial explosive development by comparing MM5 simulated rainfall and latent heat release. In addition, budget (vorticity, PV, momentum and heat) calculations and sensitivity tests will be performed to examine the upper-tropospheric and SST mechanisms responsible for the explosive development of Paka.

Tao, W.-K.↗
NASA NTRS1986

Intercomparison of UT1 measurements during the MERIT campaign period

Universal time (UT1) measurements obtained using VLBI, lunar laser ranging (LLR), and BIH optical astronomy are compared. The JPL Kalman filter for the earth rotation and the polar motion is utilized to smooth one data set or combine data sets for intercomparison. The differences between raw UT1 data and independently smoothed data are employed to assess the accuracy of the measured series and the sufficiency of the error budget. Systematic errors in periodic signatures and length of day estimates are analyzed. The data reveal that the techniques of LLR and VLBI agree to within their formal errors. It is determined that the residual error for the LLR is too large and the residual error estimated for the BIH data is too small.

Spieth, M. A.↗
NASA NTRS2006

Variations in Global Precipitation: Climate-scale to Floods

Variations in global precipitation from climate-scale to small scale are examined using satellite-based analyses of the Global Precipitation Climatology Project (GPCP) and information from the Tropical Rainfall Measuring Mission (TRMM). Global and large regional rainfall variations and possible long-term changes are examined using the 27- year (1979-2005) monthly dataset from the GPCP. In addition to global patterns associated with phenomena such as ENSO, the data set is explored for evidence of longterm change. Although the global change of precipitation in the data set is near zero, the data set does indicate a small upward trend in the Tropics (25S-25N), especially over ocean. Techniques are derived to isolate and eliminate variations due to ENS0 and major volcanic eruptions and the significance of the trend is examined. The status of TRMM estimates is examined in terms of evaluating and improving the long-term global data set. To look at rainfall variations on a much smaller scale TRMM data is used in combination with observations from other satellites to produce a 3-hr resolution, eight-year data set for examination of weather events and for practical applications such as detecting floods. Characteristics of the data set are presented and examples of recent flood events are examined.

Adler, Robert↗
NASA NTRS2002

Description of Data Archiving Activities

Data restoration and archiving activities for this project have resulted in the restoration of 100% of the original Mariner 9 raw data set as well as many of the secondary analysis data sets. These data sets have been submitted to the Planetary Data System (PDS) Atmospheric Node, long with their PDS labels and descriptive metadata.

Simmons, K. E.↗
NASA NTRS1974

Evaluation of Skylab data for land use mapping

The present work compares four multispectral data sets acquired within a 24-hour time period over an area in south-central Indiana in a land use analysis of the study area. Data sets were acquired by the 4-channel multispectral scanner (MSS) on the ERTS satellite, the Earth Resource Environmental Package (EREP) 13-channel MSS on Skylab, the color infrared photography from the EREP camera system, and the black and white multiband photography from the EREP camera system. Overall performance of MSS data sets was better than that of digitized photographic data sets. Digitized color IR data sets were better overall than digitized black and white sets. Also, overall performance of 'optimum' four channels in Skylab MSS data set was essentially the same as that for the ERTS MSS data sets. However, when the four channels in the Skylab MSS which most nearly correspond to those in the ERTS MSS were used, overall performance of Skylab MSS data set was significantly lower than that for the ERTS MSS.

Biehl, L. L.↗
NASA NTRS2017

Giovanni - The Bridge Between Data and Science

This article describes new features in the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), a user-friendly online tool that enables visualization, analysis, and assessment of NASA Earth science data sets without downloading data and software. Since the satellite era began, data collected from Earth-observing satellites have been widely used in research and applications; however, using satellite-based data sets can still be a challenge to many. To facilitate data access and evaluation, as well as scientific exploration and discovery, the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) has developed Giovanni for a wide range of users around the world. This article describes the latest capabilities of Giovanni with examples, and discusses future plans for this innovative system.

GPM↗
NASA NTRS1984

Low altitude wind shear statistics derived from measured and FAA proposed standard wind profiles

Wind shear statistics were calculated for a simulated data set using wind profiles proposed as a standard and compared to statistics derived from measured wind profile data. Wind shear values were grouped in altitude bands of 100 ft between 100 and 1400 ft, and in wind shear increments of 0.025 kt/ft between + or - 0.600 kt/ft for the simulated data set and between + or - 0.200 kt/ft for the measured set. No values existed outside the + or - 0.200 kt/ft boundaries for the measured data. Frequency distributions, means, and standard deviations were derived for each altitude band for both data sets, and compared. Also, frequency distributions were derived for the total sample for both data sets and compared. Frequency of occurrence of a given wind shear was about the same for both data sets for wind shears, but less than + or 0.10 kt/ft, but the simulated data set had larger values outside these boundaries. Neglecting the vertical wind component did not significantly affect the statistics for these data sets. The frequency of occurrence of wind shears for the flight measured data was essentially the same for each altitude band and the total sample, but the simulated data distributions were different for each altitude band. The larger wind shears for the flight measured data were found to have short durations.

Dunham, R. E., Jr.↗
NASA NTRS1989

Autoregressive modeling for the spectral analysis of oceanographic data

Over the last decade there has been a dramatic increase in the number and volume of data sets useful for oceanographic studies. Many of these data sets consist of long temporal or spatial series derived from satellites and large-scale oceanographic experiments. These data sets are, however, often 'gappy' in space, irregular in time, and always of finite length. The conventional Fourier transform (FT) approach to the spectral analysis is thus often inapplicable, or where applicable, it provides questionable results. Here, through comparative analysis with the FT for different oceanographic data sets, the possibilities offered by autoregressive (AR) modeling to perform spectral analysis of gappy, finite-length series, are discussed. The applications demonstrate that as the length of the time series becomes shorter, the resolving power of the AR approach as compared with that of the FT improves. For the longest data sets examined here, 98 points, the AR method performed only slightly better than the FT, but for the very short ones, 17 points, the AR method showed a dramatic improvement over the FT. The application of the AR method to a gappy time series, although a secondary concern of this manuscript, further underlines the value of this approach.

Gangopadhyay, Avijit↗
NASA NTRS1980

An improvement in land cover classification achieved by merging microwave data with Landsat multispectral scanner data

The improvement in land cover classification achieved by merging microwave data with Landsat MSS data is examined. To produce a merged data set for analysis and comparison, a registration procedure by which a set of Seasat SAR digital data was merged with the MSS data is described. The Landsat MSS data and the merged Landsat/Seasat data sets were processed using conventional multichannel spectral pattern recognition techniques. An analysis of the classified data sets indicates that while Landsat data delineate different forest types (i.e., deciduous/coniferous) and allow some species separation, SAR data provide additional information related to plant canopy configuration and vegetation density as associated with varying water regimes, and therefore allow for further subdivision in the classification of forested wetlands of the coastal region of the southern United States.

Wu, S. T.↗
NASA NTRS1990

NSSDC data listing

In a highly summarized way, data available from the National Space Science Data Center (NSSDC) is identified. Most data are offline data sets (on magnetic tape or as film/print products of various sizes) from individual instruments carried on spacecraft; these compose the Satellite Data Listing. Descriptive names, time spans, data form, and quantity of these data sets are identified in the listing, which is sorted alphabetically-first by spacecraft name and then by the principal investigator's or team leader's last name. Several data sets held at NSSDC, not associated with individual spaceflight instruments, are identified in separate listings following the Satellite Data Listing. These data sets make up the Supplementary Data Listings and include composite spacecraft data sets, ground-based data, models, and computer routines. The identifiers used in the Supplementary Data Listings were created by NSSDC and are explained in the pages preceding the listings. Data set form codes are listed. NSSDC offers primarily archival, retrieval, replication, and dissemination services associated with the data sets discussed in the two major listings identified above. NSSDC also provides documentation which enables the data recipient to use the data received. NSSDC is working toward expanding presently limited capabilities for data subsetting and for promotion of data files to online residence for user downloading. NSSDC data holdings span the range of scientific disciplines in which NASA is involved, and include astrophysics, lunar and planetary science, solar physics, space plasma physics, and Earth science. In addition to the functions mentioned above, NSSDC offers data via special services and systems in a number of areas, including Astronomical Data Center (ADC), Coordinated Data Analysis Workshops (CDAWs), NASA Climate Data System (NCDS), Pilot Land Data System (PLDS), and Crustal Dynamics Data Information System (CDDIS). Furthermore, NSSDC has a no-password account on its SPAN/Telenet-accessible VAX through which the NASA Master Directory and selected online data bases are accessible and through which any data described here may be ordered. Astrophysics data support by NSSDC is not limited to the ADC. Each of these special services/systems is described briefly.

Horowitz, Richard↗
NASA NTRS1994

Experimenter's Laboratory for Visualized Interactive Science

ELVIS (Experimenter's Laboratory for Visualized Interactive Science) is an interactive visualization environment that enables scientists, students, and educators to visualize and analyze large, complex, and diverse sets of scientific data. It accomplishes this by presenting the data sets as 2-D, 3-D, color, stereo, and graphic images with movable and multiple light sources combined with displays of solid-surface, contours, wire-frame, and transparency. By simultaneously rendering diverse data sets acquired from multiple sources, formats, and resolutions and by interacting with the data through an intuitive, direct-manipulation interface, ELVIS provides an interactive and responsive environment for exploratory data analysis.

Hansen, Elaine R.↗
NASA NTRS1982

Local effects of partly-cloudy skies on solar and emitted radiation

A computer automated data acquisition system for atmospheric emittance, and global solar, downwelled diffuse solar, and direct solar irradiances is discussed. Hourly-integrated global solar and atmospheric emitted radiances were measured continuously from February 1981 and hourly-integrated diffuse solar and direct solar irradiances were measured continuously from October 1981. One-minute integrated data are available for each of these components from February 1982. The results of the correlation of global insolation with fractional cloud cover for the first year's data set. A February data set, composed of one-minute integrated global insolation and direct solar irradiance, cloud cover fractions, meteorological data from nearby weather stations, and GOES East satellite radiometric data, was collected to test the theoretical model of satellite radiometric data correlation and develop the cloud dependence for the local measurement site.

Whitney, D. A.↗
NASA NTRS2013

Impact of Surface Roughness and Soil Texture on Mineral Dust Emission Fluxes Modeling

Dust production models (DPM) used to estimate vertical fluxes of mineral dust aerosols over arid regions need accurate data on soil and surface properties. The Laboratoire Inter-Universitaire des Systemes Atmospheriques (LISA) data set was developed for Northern Africa, the Middle East, and East Asia. This regional data set was built through dedicated field campaigns and include, among others, the aerodynamic roughness length, the smooth roughness length of the erodible fraction of the surface, and the dry (undisturbed) soil size distribution. Recently, satellite-derived roughness length and high-resolution soil texture data sets at the global scale have emerged and provide the opportunity for the use of advanced schemes in global models. This paper analyzes the behavior of the ERS satellite-derived global roughness length and the State Soil Geographic data base-Food and Agriculture Organization of the United Nations (STATSGO-FAO) soil texture data set (based on wet techniques) using an advanced DPM in comparison to the LISA data set over Northern Africa and the Middle East. We explore the sensitivity of the drag partition scheme (a critical component of the DPM) and of the dust vertical fluxes (intensity and spatial patterns) to the roughness length and soil texture data sets. We also compare the use of the drag partition scheme to a widely used preferential source approach in global models. Idealized experiments with prescribed wind speeds show that the ERS and STATSGO-FAO data sets provide realistic spatial patterns of dust emission and friction velocity thresholds in the region. Finally, we evaluate a dust transport model for the period of March to July 2011 with observed aerosol optical depths from Aerosol Robotic Network sites. Results show that ERS and STATSGO-FAO provide realistic simulations in the region.

textures↗
NASA NTRS1998

LITE data processing

The Lidar In-space Technology Experiment (LITE) was a payload onboard the Space Shuttle Discovery as part of the September 1994 STS-64 mission. LITE successfully made both atmospheric and surface measurements at the 355 nm, 532 nm, and 1064 nm wavelengths [Winker et al., 1996]. At mission completion, nearly 45 Gbytes of data had been obtained. As the data were collected they were organized into three data sets. The largest of these data sets contained the single-shot full resolution digitized lidar signals for all three wavelengths. The other two data sets contained the instrument status data block (ISDB) and the quick look science data (QLSD). ISDB information was generated once per second and contained timing parameters and engineering data related to the health, status, and configuration of the instrument. The QLSD are 100shot averages of the 355 nm and 532 nm lidar signals and were generated at ten second intervals. As the LITE data were generated, they were output to low-rate and high-rate telemetry streams. The low-rate data contained the ISDB and QLSD. The lowrate data were transmitted to the ground by the Shuttle S-band system through the Tracking and Data Relay Satellite System (TDRSS). The high-rate data contained the full resolution digitized lidar signals for all three wavelengths, the ISDB, and QLSD. The high-rate data were transmitted in real time by the Ku-band system through TDRSS downlink to the LITE operations center at the Johnson Space Center (JSC). High-rate data were only obtained when the orbiter and TDRSS telemetry link coincided with the real-time operations of LITE. The low-rate data were backed up by a Shuttle recorder during periods when the telemetry link was unavailable, and the entire lowrate data stream was transferred to the ground over the course of the mission. A total of 53 hours of lowrate data and 45.5 hours of high-rate data were acquired. All of the LITE raw telemetry data have been archived at the NASA Langley Research Center (LaRC), and the LITE high-rate data have been processed to the level 1 data product. This paper describes the data processing steps required to convert the LITE raw, high-rate telemetry data to the LITE level 1 data product.

Powell, Kathleen A.↗
NASA NTRS2004

Validation and Improvement of SRTM Performance over Rugged Terrain

We have previously reported work related to basic technique development in phase unwrapping and generation of digital elevation models (DEM). In the final year of this work we have applied our technique work to the improvement of DEM's produced by SRTM. In particular, we have developed a rigorous mathematical algorithm and means to fill in missing data over rough terrain from other data sets. We illustrate this method by using a higher resolution, but globally less accurate, DEM produced by the TOPSAR airborne instrument over the Galapagos Islands to augment the SRTM data set in this area, We combine this data set with SRTM to use each set to fill in holes left over by the other imaging system. The infilling is done by first interpolating each data set using a prediction error filter that reproduces the same statistical characterization as exhibited by the entire data set within the interpolated region. After this procedure is implemented on each data set, the two are combined on a point by point basis with weights that reflect the accuracy of each data point in its original image. In areas that are better covered by SRTM, TOPSAR data are weighted down but still retain TOPSAR statistics. The reverse is true for regions better covered by TOPSAR. The resulting DEM passes statistical tests and appears quite feasible to the eye, but as this DEM is the best available for the region we cannot fully veri@ its accuracy. Spot checks with GPS points show that locally the technique results in a more comprehensive and accurate map than either data set alone.

Zebker, Howard A.↗
DOE OSTI2026

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗
NASA NTRS2016

Restoration of Apollo Data by the Lunar Data Project/PDS Lunar Data Node: An Update

The Apollo 11, 12, and 14 through 17 missions orbited and landed on the Moon, carrying scientific instruments that returned data from all phases of the missions, included long-lived Apollo Lunar Surface Experiments Packages (ALSEPs) deployed by the astronauts on the lunar surface. Much of these data were never archived, and some of the archived data were on media and in formats that are outmoded, or were deposited with little or no useful documentation to aid outside users. This is particularly true of the ALSEP data returned autonomously for many years after the Apollo missions ended. The purpose of the Lunar Data Project and the Planetary Data System (PDS) Lunar Data Node is to take data collections already archived at the NASA Space Science Data Coordinated Archive (NSSDCA) and prepare them for archiving through PDS, and to locate lunar data that were never archived, bring them into NSSDCA, and then archive them through PDS. Preparing these data for archiving involves reading the data from the original media, be it magnetic tape, microfilm, microfiche, or hard-copy document, converting the outmoded, often binary, formats when necessary, putting them into a standard digital form accepted by PDS, collecting the necessary ancillary data and documentation (metadata) to ensure that the data are usable and well-described, summarizing the metadata in documentation to be included in the data set, adding other information such as references, mission and instrument descriptions, contact information, and related documentation, and packaging the results in a PDS-compliant data set. The data set is then validated and reviewed by a group of external scientists as part of the PDS final archive process. We present a status report on some of the data sets that we are processing.

Lunar↗