Kondrashov D., Y. Shprits, M. Ghil, (2010), Gap filling of solar wind data by singular spectrum analysis, Geophysical Research Letters, 37, doi:10.1029/2010GL044138
Abstract
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.Authors (sorted by name)
Ghil Kondrashov ShpritsJournal / Conference
Geophysical Research LettersAcknowledgments
The basic SSA gap‐filling algorithm is available in the SSA‐MTM Toolkit (http://www.atmos.ucla.edu/tcd/ssa/). This work is supported by the Lab Research Fee grant, 09‐LR‐04‐116720‐SHPY.Grants
09‐LR‐04116720Bibtex
@article{doi:10.1029/2010GL044138,
author = {Kondrashov, D. and Shprits, Y. and Ghil, M.},
title = {Gap filling of solar wind data by singular spectrum analysis},
journal = {Geophysical Research Letters},
year = {2010},
volume = {37},
number = {15},
pages = {},
keywords = {solar wind, singular spectrum, geomagnetic indices},
doi = {10.1029/2010GL044138},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2010GL044138},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2010GL044138},
abstract = {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.}
}