Premium
Estimation of global temperature fields from scattered observations by a spherical‐wavelet‐based spatially adaptive method
Author(s) -
Oh HeeSeok,
Li TaHsin
Publication year - 2004
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1046/j.1369-7412.2003.05220.x
Subject(s) - estimator , wavelet , spherical harmonics , extrapolation , smoothing , thresholding , representation (politics) , computer science , algorithm , surface (topology) , mathematics , mathematical optimization , artificial intelligence , mathematical analysis , computer vision , geometry , statistics , image (mathematics) , politics , political science , law
Summary. The paper considers the problem of estimating the entire temperature field for every location on the globe from scattered surface air temperatures observed by a network of weather‐stations. Classical methods such as spherical harmonics and spherical smoothing splines are not efficient in representing data that have inherent multiscale structures. The paper presents an estimation method that can adapt to the multiscale characteristics of the data. The method is based on a spherical wavelet approach that has recently been developed for a multiscale representation and analysis of scattered data. Spatially adaptive estimators are obtained by coupling the spherical wavelets with different thresholding (selective reconstruction) techniques. These estimators are compared for their spatial adaptability and extrapolation performance by using the surface air temperature data.