Reply to T. Schneider's comment on "Spatio-temporal filling of missing points in geophysical data sets"

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Spatio-temporal filling of missing points in geophysical data sets

International audienceThe majority of data sets in the geosciences are obtained from observations and measurements of natural systems, rather than in the laboratory. These data sets are often full of gaps, due to to the conditions under which the measurements are made. Missing data give rise to various problems, for example in spectral estimation or in specifying boundary conditions for numerical models. Here we use Singular Spectrum Analysis (SSA) to fill the gaps in several types of data sets. For a univariate record, our procedure uses only temporal correlations in the data to fill in the missing points. For a multivariate record, multi-channel SSA (M-SSA) takes advantage of both spatial and temporal correlations. We iteratively produce estimates of missing data points, which are then used to compute a self-consistent lag-covariance matrix; cross-validation allows us to optimize the window width and number of dominant SSA or M-SSA modes to fill the gaps. The optimal parameters of our procedure depend on the distribution in time (and space) of the missing data, as well as on the variance distribution between oscillatory modes and noise. The algorithm is demonstrated on synthetic examples, as well as on data sets from oceanography, hydrology, atmospheric sciences, and space physics: global sea-surface temperature, flood-water records of the Nile River, the Southern Oscillation Index (SOI), and satellite observations of relativistic electrons

Gap filling of solar wind data by singular spectrum analysis

International audienceObservational 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

Data-adaptive harmonic spectra and multilayer Stuart-Landau models

Harmonic decompositions of multivariate time series are considered for which we adopt an integral operator approach with periodic semigroup kernels. Spectral decomposition theorems are derived that cover the important cases of two-time statistics drawn from a mixing invariant measure. The corresponding eigenvalues can be grouped per Fourier frequency, and are actually given, at each frequency, as the singular values of a cross-spectral matrix depending on the data. These eigenvalues obey furthermore a variational principle that allows us to define naturally a multidimensional power spectrum. The eigenmodes, as far as they are concerned, exhibit a data-adaptive character manifested in their phase which allows us in turn to define a multidimensional phase spectrum. The resulting data-adaptive harmonic (DAH) modes allow for reducing the data-driven modeling effort to elemental models stacked per frequency, only coupled at different frequencies by the same noise realization. In particular, the DAH decomposition extracts time-dependent coefficients stacked by Fourier frequency which can be efficiently modeled---provided the decay of temporal correlations is sufficiently well-resolved---within a class of multilayer stochastic models (MSMs) tailored here on stochastic Stuart-Landau oscillators. Applications to the Lorenz 96 model and to a stochastic heat equation driven by a space-time white noise, are considered. In both cases, the DAH decomposition allows for an extraction of spatio-temporal modes revealing key features of the dynamics in the embedded phase space. The multilayer Stuart-Landau models (MSLMs) are shown to successfully model the typical patterns of the corresponding time-evolving fields, as well as their statistics of occurrence.Comment: 26 pages, double columns; 15 figure

The method of diagnosing machine systems by measuring the accuracy of manufactured parts

© Published under licence by IOP Publishing Ltd. The main provisions of the technique allowing to create a diagnostic complex of the technical state of the machine system, which is informative at the same time of several diagnostic complexes - geometrical accuracy, strain gauge, technological accuracy, the influence of technological heredity - are revealed

Development of the design of a laboratory vibro-grinding machine for preparing samples for metallographic research

© Published under licence by IOP Publishing Ltd. The article presents the results of testing a prototype vibro-grinding laboratory machine for making samples for metallographic examination. The effectiveness of the method and its suitability for the preparation of thin sections during laboratory studies in the discipline "Material Science" have been established

Genomic Selective Constraints in Murid Noncoding DNA

Recent work has suggested that there are many more selectively constrained, functional noncoding than coding sites in mammalian genomes. However, little is known about how selective constraint varies amongst different classes of noncoding DNA. We estimated the magnitude of selective constraint on a large dataset of mouse-rat gene orthologs and their surrounding noncoding DNA. Our analysis indicates that there are more than three times as many selectively constrained, nonrepetitive sites within noncoding DNA as in coding DNA in murids. The majority of these constrained noncoding sites appear to be located within intergenic regions, at distances greater than 5 kilobases from known genes. Our study also shows that in murids, intron length and mean intronic selective constraint are negatively correlated with intron ordinal number. Our results therefore suggest that functional intronic sites tend to accumulate toward the 5' end of murid genes. Our analysis also reveals that mean number of selectively constrained noncoding sites varies substantially with the function of the adjacent gene. We find that, among others, developmental and neuronal genes are associated with the greatest numbers of putatively functional noncoding sites compared with genes involved in electron transport and a variety of metabolic processes. Combining our estimates of the total number of constrained coding and noncoding bases we calculate that over twice as many deleterious mutations have occurred in intergenic regions as in known genic sequence and that the total genomic deleterious point mutation rate is 0.91 per diploid genome, per generation. This estimated rate is over twice as large as a previous estimate in murids