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Activity Number:
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386
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #309024 |
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Title:
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Characterizing the Dependence Structure of Space-Time Processes Using Computer-Model Output and Sparse Observations
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Author(s):
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Candace Berrett*+ and Catherine A. Calder and Tao Shi
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Companies:
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The Ohio State University and The Ohio State University and The Ohio State University
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Address:
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3504 Prestwick Ct, Columbus, OH, 43220,
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Keywords:
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Atmospheric science ; Environmental science ; Remote sensing ; Spatial statistics ; Time Series
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Abstract:
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Characterizing the dependence structure of a space-time process from remote-sensing data can be a difficult task. Large amounts of missing observations, as well as systematic variation in the quality of observations, do not allow important features such as nonstationarity and anisotropy to be readily identified. In this talk, we discuss how output from numerical models can be used to guide space-time modeling of a process for which only limited data are available. In particular, we focus on modeling the distribution of carbonaceous aerosols over mainland Southeast Asia using data collected by the MISR and MODIS instruments onboard the Terra and Aqua satellites and output from the aerosol transportation simulator MOZART.
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