HR: 1340h
AN: GC43A-0949    [Abstracts]
TI: A kinematic representation of spatiotemporal scalar fields to support mining dynamics in data output from general circulation models.
AU: * Bothwell, J
EM: jamesdbothwell@yahoo.com
AF: University of Oklahoma Department of Geography, 100 East Boyd St. SEC Suite 684, Norman, OK 73019, United States
AU: Yuan, M
EM: myuan@ou.edu
AF: University of Oklahoma Department of Geography, 100 East Boyd St. SEC Suite 684, Norman, OK 73019, United States
AB: Effective information analytics is required to decipher massive data output from general circulation models (GCMs) and other spatially explicit models of environmental dynamics. Common approaches with discrete space or time constructs overlook the fundamental characteristics of continuity in dynamics. We propose a representation that centers on the concept of kinetics to effectively capture dynamics by the direction and amount of change in space and time, i.e. velocity. In scalar fields, such as temperature, output from GCMs, we first define isolines to represent spatial variations of the fields. We then determine the velocity of isoline movement across time. Similarly, features of hot or cold spots can be identified from a scalar field of temperature. Velocity determined by the direction and amount of boundary change can capture deformation and movement of these features. The kinematic representation enables the comparison of multiple GCM's output at an increased level of abstraction through isoline and feature identification, and furthermore it enables the analogous comparison of climate change suggested by data output from multiple general circulation models. A comparison of the Center National Weather Research global coupled system and the National Center for Atmospheric Research Community Climate System Model output for IPCC scenario A2 was made. Our results indicate that climate change patterns from the two models are well correlated except for a high latitude band spanning Greenland, the northern Atlantic region and northern Eurasia. The kinematic representation presented by this paper enabled the spatiotemporal analysis of massive data sets, highlighting smaller spatial regions where further research may be productive in understanding the differences between different GCM models.
DE: 0520 Data analysis: algorithms and implementation
DE: 0530 Data presentation and visualization
DE: 0545 Modeling (4255)
DE: 0550 Model verification and validation
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting