Open AccessRemoval of nonconstant daily variation by means of wavelet and functional data analysis
Author(s) -
Maslova I.,
Kokoszka P.,
Sojka J.,
Zhu L.
Publication year - 2009
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2008ja013685
Subject(s) - wavelet , principal component analysis , index (typography) , variation (astronomy) , construct (python library) , mathematics , functional data analysis , constant (computer programming) , computer science , wavelet transform , component (thermodynamics) , statistics , biological system , artificial intelligence , physics , thermodynamics , world wide web , astrophysics , programming language , biology
We propose a novel approach based on wavelet and functional principal component analysis to produce a cleaner index of the intensity of the symmetric ring current. We use functional canonical correlations to show that the new approach more effectively extracts symmetric global features. The main result of our work is the construction of a new index, which is an improved version of the existing wavelet‐based index (WISA) and the old Dst index, in which a constant daily variation is removed. Here, we address the fact that the daily component varies from day to day and construct a “cleaner” index by removing nonconstant daily variations.