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Gap filling of solar wind data by singular spectrum analysis
Author(s) -
Kondrashov D.,
Shprits Y.,
Ghil M.
Publication year - 2010
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2010gl044138
Subject(s) - singular spectrum analysis , solar wind , earth's magnetic field , space weather , geophysics , interplanetary magnetic field , meteorology , noise (video) , sampling (signal processing) , solar physics , physics , computer science , algorithm , magnetic field , astrophysics , singular value decomposition , artificial intelligence , detector , quantum mechanics , optics , image (mathematics)
Observational data sets in space physics often contain instrumental and sampling errors, as well as large gaps. This is both an obstacle and an incentive for research, since continuous data sets are typically needed for model formulation and validation. For example, the latest global empirical models of Earth's magnetic field are crucial for many space weather applications, and require time‐continuous solar wind and interplanetary magnetic field (IMF) data; both of these data sets have large gaps before 1994. Singular spectrum analysis (SSA) reconstructs missing data by using an iteratively inferred, smooth “signal” that captures coherent modes, while “noise” is discarded. In this study, we apply SSA to fill in large gaps in solar wind and IMF data, by combining it with geomagnetic indices that are time‐continuous, and generalizing it to multivariate geophysical data consisting of gappy “driver” and continuous “response” records. The reconstruction error estimates provide information on the physics of co‐variability between particular solar‐wind parameters and geomagnetic indices.