Gap filling of solar wind data by singular spectrum analysis
نویسندگان
چکیده
منابع مشابه
Gap filling of solar wind data by singular spectrum analysis
[1] 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...
متن کاملGap Filling of Solar Wind Data by Singular 1 Spectrum Analysis
Observational data sets in space physics often contain instrumental and 3 sampling errors, as well as large gaps. This is both an obstacle and an in-4 centive for research, since continuous data sets are typically needed for model 5 formulation and validation. For example, the latest global empirical mod-6 els of Earth's magnetic field are crucial for many space weather applications, 7 and requ...
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The neural network-based nonlinear singular spectrum analysis (NLSSA) is applied to the zonal winds in the 70-10 hPa region (roughly 20-30 km altitude) measured at near-equatorial stations during 1956-2000. The data are pre-filtered by the linear singular spectrum analysis (SSA), with the leading 8 SSA principal components (PCs) used as inputs for the NLSSA. The NLSSA fits a curve to the data i...
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Singular spectrum analysis (SSA), a linear univariate and multivariate time series technique , is essentially principal component analysis (PCA) applied to the time series and additional copies of the time series lagged by 1 to K time steps. Neural network theory has meanwhile allowed PCA to be generalized to nonlinear PCA (NLPCA). In this paper, NLPCA is further extended to perform nonlinear S...
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ژورنال
عنوان ژورنال: Geophysical Research Letters
سال: 2010
ISSN: 0094-8276
DOI: 10.1029/2010gl044138