Abstract

Data Mining In Earth Sciences

Rahul Ramachandran, Sara Graves, and Ken Keiser (University of Alabama in Huntsville) (I)

The volume of raw data being stored by different Earth Science archives today
defies even the partial manual examination by scientists. The Earth Observing
System data volumes will reach a terabyte per day by the time all the planned
satellites are flown. The ADaM (Algorithm Development and Mining) system,
developed at the Information Technology and Systems Center at the University of
Alabama in Huntsville, is a powerful tool that can be used to mine these large
volumes of Earth Science data. This system contains algorithms for detecting a
variety of geophysical phenomena to address the needs of the earth science
community. It has been utilized in a variety of earth science applications.
Short summaries on a couple of these applications are given below:

• Cumulus Cloud Detection: Boundary layer cumulus clouds over land
  are difficult to detect in satellite data, owing to low contrast in both
  visible and infrared channels. For Geostationary Operational Environmental
  Satellite (GOES) data the problem becomes severe, as the infrared channel
  resolution is 4km, compared to 1km in the visible channel. A study was
  conducted analyzing three of the different image processing and pattern
  recognition techniques available in ADaM for cumulus cloud detection. These
  were classifiers based on 1) texture and spectral features, 2) edge
  detection and spectral features, and 3) purely spectral features.


• Phenomena Detection: The ability of ADaM to mine for particular
  data values or geophysical phenomena within a specified data product has
  been actively utilized on several projects. Special Sensor Microwave/Imager
  (SSM/I) data is being mined during ingest to detect and locate Mesoscale
  Convective Systems. In a different project, Advanced Microwave Sounding Unit
  (AMSU) data is mined in real time to locate tropical storms and estimate
  their maximum wind speeds. Operational forecasters at National Hurricane
  Center are utilizing this information to aid in their analysis and tracking
  of hurricanes.

This presentation will describe these and other earth science mining
applications.

Bio:

Rahul Ramachandran is a Research Scientist at the Information Technology and Systems
Center. His academic background is in Atmospheric Science and Computer Science. His
research interests include application of information technology for scientific
research, data mining, automatic pattern recognition and image classification problems.