Gap filling of solar wind data by singular spectrum analysis

D. Kondrashov (1) , Y. Shprits (1) , M. Ghil (1, 2)

1 Department of Atmospheric and Oceanic Sciences and Institute of Geophysics and Planetary Physics
2 LMD - Laboratoire de Météorologie Dynamique (UMR 8539)

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. Copyright 2010 by the American Geophysical Union.

Domaines: Sciences de la Terre

D. Kondrashov, Y. Shprits, M. Ghil. Gap filling of solar wind data by singular spectrum analysis. Geophysical Research Letters, 2010, 37 (15), pp.L15101. ⟨10.1029/2010GL044138⟩. ⟨hal-01136783⟩