Gap filling of solar wind data by singular spectrum analysis

Details Gap filling of solar wind data by singular spectrum analysis Gap filling of solar wind data by singular spectrum analysis

Aug 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010georl..3715101k&link_type=abstract

Geophysical Research Letters, Volume 37, Issue 15, CiteID L15101

Physics

Geophysics

3

Interplanetary Physics: Solar Wind Plasma, Magnetospheric Physics: Solar Wind/Magnetosphere Interactions, Hydrology: Time Series Analysis (1988, 3270, 4277, 4475), Mathematical Geophysics: Spectral Analysis (3205, 3280)

Scientific paper

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.

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