A Comparison of Methods for Ocean Reconstruction from Sparse Observations

Monday, 15 December 2014
Gregory J Streletz1, Markus Kronenberger2, Christopher Weber2, Geoffrey Gebbie3, Hans Hagen2, Christoph Garth2, Bernd Hamann1, Oliver Kreylos4, Louise H Kellogg5 and Howard J Spero1, (1)University of California Davis, Davis, CA, United States, (2)University of Kaiserslautern, Kaiserslautern, Germany, (3)Woods Hole Oceanographic Inst., Woods Hole, MA, United States, (4)University of California Davis, KeckCAVES, Davis, CA, United States, (5)University of California - Davis, Davis, CA, United States
We present a comparison of two methods for developing reconstructions of oceanic scalar property fields from sparse scattered observations. Observed data from deep sea core samples provide valuable information regarding the properties of oceans in the past. However, because the locations of sample sites are distributed on the ocean floor in a sparse and irregular manner, developing a global ocean reconstruction is a difficult task. Our methods include a flow-based and a moving least squares -based approximation method. The flow-based method augments the process of interpolating or approximating scattered scalar data by incorporating known flow information. The scheme exploits this additional knowledge to define a non-Euclidean distance measure between points in the spatial domain. This distance measure is used to create a reconstruction of the desired scalar field on the spatial domain. The resulting reconstruction thus incorporates information from both the scattered samples and the known flow field. The second method does not assume a known flow field, but rather works solely with the observed scattered samples. It is based on a modification of the moving least squares approach, a weighted least squares approximation method that blends local approximations into a global result. The modifications target the selection of data used for these local approximations and the construction of the weighting function. The definition of distance used in the weighting function is crucial for this method, so we use a machine learning approach to determine a set of near-optimal parameters for the weighting. We have implemented both of the reconstruction methods and have tested them using several sparse oceanographic datasets. Based upon these studies, we discuss the advantages and disadvantages of each method and suggest possible ways to combine aspects of both methods in order to achieve an overall high-quality reconstruction.