Utilizing 3-Dimensional Data Views to Access Data
NASA's A-Train, comprised of a succession of US and international satellites that follow each other, seconds to minutes apart, across the local afternoon equator, provides great opportunities to increase the number of observations, validate observations, and enable coordination between science observations, resulting in a more complete "virtual science platform". (Kelly, 2003).
The A-Train consists of the following satellites, in order of equator crossing: Orbiting Carbon Observatory-2 (OCO-2), EOS Aqua, CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL), and EOS Aura all in the same sun-synchronous orbit. Unfortunately, when each project was conceived and implemented, formation flying as described above, and the wealth of science that would result, was not considered when data structures, data formats, fields of view, instrument resolutions, etc, were designed for each instrument. Projects like the A-Train Data Depot (ATDD) (Kempler, 2006), and efforts at Colorado State University (Reinke, 2006) and University of Utah (Mace, 2007), have made great strides in providing researchers means to visually explore the A-Trains vertical 'curtains', provide data access, and distribute various datasets along the A-Train tracks, thus bearing the fruits of science promised by formation flying.
Not only have these efforts made otherwise heterogeneous A-train data available from one virtual data portal, they have also provided data pre-subsetted and pre-processed to common grids, thus further facilitating the use of the data while removing the burden of this preprocessing from thousands of users. In addition, A-Train science provides a very unique challenge in that it involves datasets studied specifically in three dimensions. Whereas, Cloudsat and CALIPSO measure vertical profiles, most other instruments measure in the horizontal plane. Some instruments, like NASA's Microwave Limb Sounder (MLS) and Atmospheric Infrared Sounder (AIRS) provide data in 3 dimensions.
The purpose of this project is to develop the tool, A-Train Data in 3 Dimensions (ATD3D), that employs the latest 3 dimensional visualization technology to explore and provide direct data access to heterogeneous A-Train datasets, "operationally", along, and on either side of the A-Train tracks, that emphasize the multi-dimensional significance of cross instrument A-Train data.
Google Earth provides the foundation for displaying vertically and horizontally oriented datasets. For example, visualizations such as Cloudsat and CALIPSO vertical curtains, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud top pressures, and Ozone Monitoring Instrument (OMI) cloud pressure, can reveal cross instrument data signatures not otherwise easily detected. In addition, ground meteorology, such as surface temperature, pressure, winds, and rainfall, will be available to inter-compare atmospheric measurements with synoptic weather conditions.
Utilizing Google Earth's ability zoom, pan, tilt, and rotate, provides users to the opportunity to examine features prior to downloading data, perhaps unnecessarily. Once data of interest is discovered, users will be able to access the specific datasets from the archive in which the raw data resides through web services. Tying A-Train data on Google Earth with the ATDD increases users ability to discover, access, manipulate and analyze A-Train atmospheric data.
This project will make use of: Google Earth for information discovery; NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) Giovanni front-end for data search; The GES DISC S4PA for serving local data, and; OpenDAP for access to remote data. All services are TRL 9. This project will facilitate the further utilization of NASA Earth science data through the integration of publicly available tools.
Steve Kempler - PI, NASA's Goddard Space Flight Center
Last Updated: Nov 16, 2017 at 10:28 AM EST