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Spatial Data Fusion with a Multiresolusion-Gaussian Process model: Fusion with LatticeKrig

Abstract Number:

3319 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Ryan Peterson (1), Douglas Nychka (2), Dorit Hammerling (2)

Institutions:

(1) Colorado School Of Mines, N/A, (2) Colorado School of Mines, N/A

Co-Author(s):

Douglas Nychka  
Colorado School of Mines
Dorit Hammerling  
Colorado School of Mines

First Author:

Ryan Peterson  
Colorado School Of Mines

Presenting Author:

Ryan Peterson  
Colorado School Of Mines

Abstract Text:

Remotely sensed observations of the atmosphere play an important role in climate research since they often have more extensive spatial coverage than surface measurements. One challenge with satellite data in particular is that an observation represents a spatial average over the satellite footprint rather than a point location. Moreover, this problem is compounded when the footprints vary in size and degree of overlap between successive observations. Our goal is to combine observations of the same process from different remotely-sensed platforms into a single model, precisely accounting for heterogeneity across multiple observations and spatial averaging. We adapt earlier data fusion methods, often referred to as change-of-support methods in geostatistics, using LatticeKrig, a fixed-rank multiresolution-Gaussian Process model. This framework leverages sparse linear algebra and efficient basis representations to provide computational efficiency when faced with large data volumes. We demonstrate our method by fusing total column carbon monoxide (CO) from the MOPITT and TROPOMI satellite instruments for the Australasia and Maritime Southeast Asia regions.

Keywords:

change-of-support|data fusion|basis function|satellite data|total column carbon monoxide|spatial statistics

Sponsors:

Section on Statistics and the Environment

Tracks:

Miscellaneous

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