43 Spatial Data Fusion with the Multiresolution-Gaussian Process model LatticeKrig

Oregon Convention Center 

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.

change-of-support

data fusion

basis function

satellite data

total column carbon monoxide

spatial statistics 

Section on Statistics and the Environment