Engineering topics

Gopalan, A.

Publications and source records attributed to Gopalan, A..

9 records

NASA NTRS2017

A Consistent EPIC Visible Channel Calibration using VIIRS and MODIS as a Reference

The Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) satellite constantly images the sunlit disk of Earth from the Lagrange-1 (L1) point in 10 spectral channels spanning the UV, VIS, and NIR spectrums. Recently, the DSCOVR EPIC team has publicly released version 2 dataset, which has implemented improved navigation, stray-light correction, and flat-fielding of the CCD array. The EPIC 2-year data record must be well-calibrated for consistent cloud, aerosol, trace gas, land use and other retrievals. Because EPIC lacks onboard calibrators, the observations made by EPIC channels must be calibrated vicariously using the coincident measurements from radiometrically stable instruments that have onboard calibration systems. MODIS and VIIRS are best-suited instruments for this task as they contain similar spectral bands that are well-calibrated onboard using solar diffusers and lunar tracking. We have previously calibrated the EPIC version 1 dataset by using EPIC and VIIRS angularly matched radiance pairs over both all-sky ocean and deep convective clouds (DCC). We noted that the EPIC image required navigations adjustments, and that the EPIC stray-light correction provided an offset term closer to zero based on the linear regression of the EPIC and VIIRS ray-matched radiance pairs. We will evaluate the EPIC version 2 navigation and stray-light improvements using the same techniques. In addition, we will monitor the EPIC channel calibration over the two years for any temporal degradation or anomalous behavior. These two calibration methods will be further validated using desert and DCC invariant Earth targets. The radiometric characterization of the selected invariant targets is performed using multiple years of MODIS and VIIRS measurements. Results of these studies will be shown at the conference.

Haney, C. O.↗

NASA NTRS2007

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Leptoukh, Gregory↗

NASA NTRS2007

A-Train Data Depot: Integrating and Exploring Data Along the A-Train Tracks

The immense potential for new science findings as a result of inter-instrument data analysis has led to the development of a new data portal at GSFC: the A-train Data Depot. The power and utility of this new service to the general public is amplified immensely when the archived data are used in conjunction with online data analysis services like Giovanni. This presentation details some of the challenges of data usage from multiple distinct missions and how the tool sets we have developed can help to overcome these challenges, considerably cut down on analysis overhead and promote science exploration in an otherwise very challenging arena.

Leptoukh, G.↗

NASA NTRS2007

Remote Sensing Data Visualization, Fusion and Analysis via Giovanni

We describe Giovanni, the NASA Goddard developed online visualization and analysis tool that allows users explore various phenomena without learning remote sensing data formats and downloading voluminous data. Using MODIS aerosol data as an example, we formulate an approach to the data fusion for Giovanni to further enrich online multi-sensor remote sensing data comparison and analysis.

Leptoukh, G.↗

NASA NTRS2004

Application of Bayesian Classification to Content-Based Data Management

The high volume of Earth Observing System data has proven to be challenging to manage for data centers and users alike. At the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), about 1 TB of new data are archived each day. Distribution to users is also about 1 TB/day. A substantial portion of this distribution is MODIS calibrated radiance data, which has a wide variety of uses. However, much of the data is not useful for a particular user's needs: for example, ocean color users typically need oceanic pixels that are free of cloud and sun-glint. The GES DAAC is using a simple Bayesian classification scheme to rapidly classify each pixel in the scene in order to support several experimental content-based data services for near-real-time MODIS calibrated radiance products (from Direct Readout stations). Content-based subsetting would allow distribution of, say, only clear pixels to the user if desired. Content-based subscriptions would distribute data to users only when they fit the user's usability criteria in their area of interest within the scene. Content-based cache management would retain more useful data on disk for easy online access. The classification may even be exploited in an automated quality assessment of the geolocation product. Though initially to be demonstrated at the GES DAAC, these techniques have applicability in other resource-limited environments, such as spaceborne data systems.

Lynnes, Christopher↗

NASA NTRS2004

Visualization, Analysis and Subsetting Tools for EOS Aura Data Products in HDF-EOS5

Aura data products are among the first to use the new version 5 of the Hierarchical Data Format for the Earth Observing System, or HDF-EOS5. This presentation discusses the common HDF-EOS5 file layout that is adopted for most of the EOS Aura standard data products. Details of the various tools that can be used to access, visualize and subset these data will also be provided. Aura, the NASA Earth Observing System's atmospheric chemistry mission, was successfully launched July 15, 2004. The Aura spacecraft includes four instruments: the High Resolution Dynamics Limb Sounder (HIRDLS), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Tropospheric Emission Spectrometer (TES). Data from the HIRDLS, MLS and OMI will be archived at the NASA Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC), while TES data will be archived at the NASA Langley Research Center DAAC. For more information see http://daac.gsfc.nasa.gov/.

Johnson, J.↗

NASA NTRS2004

Aura Atmospheric Data Products and Their Availability from NASA Goddard Earth Sciences DAAC

NASA's EOS-Aura spacecraft was launched successfully on July 15, 2004. The four instruments onboard the spacecraft are the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), the Tropospheric Emission Spectrometer (TES), and the High Resolution Dynamics Limb Sounder (HBDLS). The Aura instruments are designed to gather earth sciences measurements across the ultraviolet, visible, infra-red, thermal and microwave regions of the electromagnetic spectrum. Aura will provide over 70 distinct standard atmospheric data products for use in ozone layer and surface UV-B monitoring, air quality forecast, and atmospheric chemistry and climate change studies (http://eosaura.gsfc.nasa.gov/). These products include earth-atmosphere radiances and solar spectral irradiances; total column, tropospheric, and profiles of ozone and other trace gases, surface W-B flux; clouds and aerosol characteristics; and temperature, geopotential height, and water vapor profiles. The MLS, OMI, and HIRDLS data products will be archived at the NASA Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC), while data from TES will be archived at NASA Langley Research Center DAAC. Some of the standard products which have gone through quick preliminary checks are already archived at the GES DAAC (http://daac.nsfc.nasa.gov/) and are available to the Aura science team and data validation team members for data validation; and to the application and visualization software developers, for testing their application modules. Once data are corrected for obvious calibration problems and partially validated using in-situ observations, they would be made available to the broader user community. This presentation will provide details of the whole suite of Aura atmospheric data products, and the time line of the availability of the rest of the preliminary products and of the partially validated provisional products. Software and took available for data access, visualization, and data mining will also be discussed.

Ahmad, S.↗

NASA NTRS2003

GOME Total Ozone and Calibration Error Derived Usign Version 8 TOMS Algorithm

The Global Ozone Monitoring Experiment (GOME) is a hyper-spectral satellite instrument measuring the ultraviolet backscatter at relatively high spectral resolution. GOME radiances have been slit averaged to emulate measurements of the Total Ozone Mapping Spectrometer (TOMS) made at discrete wavelengths and processed using the new TOMS Version 8 Ozone Algorithm. Compared to Differential Optical Absorption Spectroscopy (DOAS) techniques based on local structure in the Huggins Bands, the TOMS uses differential absorption between a pair of wavelengths including the local stiucture as well as the background continuum. This makes the TOMS Algorithm more sensitive to ozone, but it also makes the algorithm more sensitive to instrument calibration errors. While calibration adjustments are not needed for the fitting techniques like the DOAS employed in GOME algorithms, some adjustment is necessary when applying the TOMS Algorithm to GOME. Using spectral discrimination at near ultraviolet wavelength channels unabsorbed by ozone, the GOME wavelength dependent calibration drift is estimated and then checked using pair justification. In addition, the day one calibration offset is estimated based on the residuals of the Version 8 TOMS Algorithm. The estimated drift in the 2b detector of GOME is small through the first four years and then increases rapidly to +5% in normalized radiance at 331 nm relative to 385 nm by mid 2000. The lb detector appears to be quite well behaved throughout this time period.

Gleason, J.↗

NASA NTRS1992

Infrared absorption-coefficient data on SF6 applicable to atmospheric remote sensing

Spectral absorption coefficients, k(nu)/cm per atm, of SF6 have been measured in the central Q-branches of the nu(3)-fundamental at 947/cm at various temperature-pressure combinations representing tangent heights in solar-occultation experiments or layers in the atmosphere. The data obtained with the Doppler-limited spectral resolution (about 0.0001/cm) of a tunable-diode laser spectrometer are useful in the atmospheric remote sensing of this trace gas.

Varanasi, P.↗