2022/08/15 by Jian Cao, Jingjie Zhang, Cao, Jian +5
Environmental Science · #Climate variability and models #FOS: Computer and information sciences #Hydrology and Drought Analysis #Methodology (stat.ME) #Soil Geostatistics and Mapping
paper · pdf · doi:10.48550/arxiv.2208.07431
openalex publication_date 2022/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that can capture spatial dependence on spherical domains. We propose a general method of constructing nonstationary, locally anisotropic covariance functions on the sphere based on covariance functions in R3. We also provide theorems that specify the conditions under which the resulting correlation function is isotropic or axially symmetric. For large datasets on the sphere commonly seen in modern applications, the Vecchia approximation is used to achieve higher scalability on statistical inference. The importance of flexible covariance structures is demonstrated numerically using simulated data and a precipitation dataset.