Statistics Seminar: Cressie -- Statistical Modeling of Big, Spatial, Non-Gaussian Data: The MODIS Cloud Mask Product

Speaker:  
Noel Cressie   
National Institute for Applied Statistics Research Australia (NIASRA)
University of Wollongong   
Australia 

Abstract:   

Remote sensing of the earth by satellites yields datasets that can be 
massive in size. To overcome computational challenges, we make use of 
the reduced-rank Spatial Random Effects (SRE) model in our statistical 
analysis of cloud mask data from NASA’s Moderate Resolution Imaging 
Spectroradiometer (MODIS) instrument on board NASA’s Terra satellite, 
launched in December 1999. A set of retrieval algorithms has been 
developed by members of the MODIS atmospheric team for detecting
clouds. Clouds play an important role in climate studies and, hence, 
an accurate quantification of the spatial distribution of clouds is 
necessary. In this paper, we build a statistical model for the 
underlying clear-sky-probability (or conversely, the cloud-probability) 
process, and we quantify the uncertainty in our predictions. We 
consider a hierarchical statistical model for analyzing the cloud data, 
where we postulate a hidden process for the probability of clear sky 
that makes use of the SRE model. Its advantages are considerable: It 
can represent many types of spatial behavior, it permits fast 
computations when datasets are very large, and it has attractive 
change-of-support properties.