A class of Matérn-like covariance functions for smooth processes on a sphere

Abstract There have been noticeable advancements in developing parametric covariance models for spatial and spatio-temporal data with various applications to environmental problems. However, literature on covariance models for processes defined on the surface of a sphere with great circle distance as a distance metric is still sparse, due to its mathematical difficulties. It is known that the popular Matern covariance function, with smoothness parameter greater than 0.5, is not valid for processes on the surface of a sphere with great circle distance. We introduce an approach to produce Matern-like covariance functions for smooth processes on the surface of a sphere that are valid with great circle distance. The resulting model is isotropic and positive definite on the surface of a sphere with great circle distance, with a natural extension for nonstationarity case. We present extensive numerical comparisons of our model, with a Matern covariance model using great circle distance as well as chordal distance. We apply our new covariance model class to sea level pressure data, known to be smooth compared to other climate variables, from the CMIP5 climate model outputs.

[1]  Roger Woodard,et al.  Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.

[2]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[3]  Chunfeng Huang,et al.  On the Validity of Commonly Used Covariance and Variogram Functions on the Sphere , 2011 .

[4]  Christopher J Paciorek,et al.  Spatial modelling using a new class of nonstationary covariance functions , 2006, Environmetrics.

[5]  Mikyoung Jun,et al.  Nonstationary covariance models for global data , 2008, 0901.3980.

[6]  T. Gneiting,et al.  Lévy particles : Modelling and simulating star-shaped random sets , 2011 .

[7]  Jianhua Z. Huang,et al.  A full scale approximation of covariance functions for large spatial data sets , 2012 .

[8]  Douglas W. Nychka,et al.  Constructing valid spatial processes on the sphere using kernel convolutions , 2014 .

[9]  M. Stein Nonstationary spatial covariance functions , 2005 .

[10]  Chunsheng Ma,et al.  Variogram Matrix Functions for Vector Random Fields with Second-Order Increments , 2012, Mathematical Geosciences.

[11]  Chunsheng Ma,et al.  Isotropic Variogram Matrix Functions on Spheres , 2013, Mathematical Geosciences.

[12]  K. Miller,et al.  Completely monotonic functions , 2001 .

[13]  M. Fuentes,et al.  Covariance functions for mean square differentiable processes on spheres , 2013 .

[14]  A. Ron,et al.  Strictly positive definite functions on spheres in Euclidean spaces , 1994, Math. Comput..

[15]  A. Balakrishnan,et al.  Spectral theory of random fields , 1983 .

[16]  Mikyoung Jun,et al.  Matérn-based nonstationary cross-covariance models for global processes , 2014, J. Multivar. Anal..

[17]  D. Nychka,et al.  Covariance Tapering for Interpolation of Large Spatial Datasets , 2006 .

[18]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[19]  Mikyoung Jun,et al.  An Approach to Producing Space–Time Covariance Functions on Spheres , 2007, Technometrics.

[20]  Douglas W. Nychka,et al.  Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets , 2008 .

[21]  I. J. Schoenberg Positive definite functions on spheres , 1942 .

[22]  T. Gneiting Strictly and non-strictly positive definite functions on spheres , 2011, 1111.7077.

[23]  Douglas W. Nychka,et al.  Nonstationary modeling for multivariate spatial processes , 2012, J. Multivar. Anal..