By Topic

Using KML and Virtual Globes to Access and Visualize Heterogeneous Datasets and Explore Their Relationships Along the A-Train Tracks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Aijun Chen ; U.S. NASA Goddard Earth Sci. Data & Inf. Service Center, Greenbelt, MD, USA ; Leptoukh, G.G. ; Kempler, S.J.

Keyhole Markup Language (KML), the de facto standard for representing, visualizing and transmitting geospatial data on Virtual Globes, lately approved by the Open Geospatial Consortium (OGC), Inc., has been widely used by the Earth Science communities. Most of the popular virtual globe systems, such as Google Earth and Microsoft Virtual Earth support KML format. This new online approach is changing the way in which scientists and the general public interact with three-dimensional geospatial data in a virtual environment. The so-called A-Train, a series of seven U.S. and international Sun-synchronous satellites, flying in tight formation just seconds to minutes apart, across the local afternoon equator, has been producing abundant measurements of vertical profiles of atmospheric parameters. This paper first discusses the key technical points for access to and visualization of three-dimensional Earth science data by using KML and Virtual Globes. Then, the Virtual Globes are taken as a virtual three-dimensional platform to synergize horizontal data and vertical profiles along the A-Train tracks to explore the scientific relationships among multiple physical phenomena. Two kinds of scientific scenarios are investigated: a) The relationships among cloud, aerosol and atmospheric temperature, and b) the relationships among cloud, wind and precipitation. The seamless visualization and synergy of multiple versatile datasets facilitate scientists to easily explore and find critical relationships between some phenomena that would not be easily found otherwise.

Published in:

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  (Volume:3 ,  Issue: 3 )