Selection of wave disturbances on background of trends in satellite thermosphere observation’s data

1Lizunov, GV, 1Skorokhod, TV
1Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, Kyiv, Ukraine
Space Sci. & Technol. 2018, 24 ;(6):57-68
https://doi.org/10.15407/knit2018.06.057
Publication Language: Russian
Abstract: 
Registration of the wave processes in atmosphere/ionosphere is linked with the difficulty of recognizing of the wave disturbances against the background of powerful low-frequency trends of atmospheric/ionospheric parameters. The generally accepted approach for selection of a wave under such conditions is exclusion of a trend from original data series. Different groups of the authors use various methods of trend approximation that causes different wave profiles at the output. Especially noticeable differences appear in the spectral domain, in which, depending on the method of trend fitting, various spectral components of the wave process are amplified or suppressed.
                        The goal of present work was to develop a correct method for decomposing signal on a trend and wave process for the case when the trend and the wave are separated in the frequency domain (which is typical, in particular, for acoustic and gravity waves in the Earth's thermosphere). Used research methods were the digital signal processing and spectral analysis.
                        Physical approaches and mathematical methods for data processing were developed. Verification of methods was performed on the model data sets. The problem of estimation of the error inserted to wave characteristic by data decomposition procedure was formulated and solved for the first time. For this purpose the error analysis was transferred from the spatial-temporal domain to the spectral one. On atmospheric acoustic and gravity waves scales the error of the wave spectrum resolution is 1–5 %. This error arises from the basic principles of frequency estimation on finite data interval and could not be eliminated due to “more thorough” data processing. The nature of difficulty of signal decomposition on trend and wave was revealed; it is caused by the trend spectrum spreading that is an artifact of digital signal processing. The solution of the problem of wave disturbance selection was realized using dual data filtering. At the first stage a rough exclusion of the trend was done. This stage aimed on the elimination of trend and wave spectrum overlapping upon the condition that algorithm of trend exclusion doesn’t distort the frequency range of the useful signal. At the second stage the residual data series were passed through an ideal filter tuned to the frequency band of the wave process.
                        After appropriate adaptation the developed data processing method could be applied to the analysis of remote observations of the ionosphere and in other data mining areas.
Keywords: digital signal processing, space experiment, spectral analysis, upper atmosphere, wave processes
References: 
1. Lazorenko, O., Chernogor, L. (2009) Ultra-wideband signals and processes: Monograph. Kharkiv: V. Karazin KhNU [in Russian].
2. Lyons R. G. (2004) Understanding digital signal processing. Prentice Hall, 2 edition [in English].
3. Skorokhod T. V., Lizunov G. V. (2012) Localized packets of acoustic-gravity waves in the ionosphere. Geomagnetism and Aeronomy, 52 (1): 88—93 [in English].
4. Fatkullin, M., Zarutskaya, E., Fatkullina, V. (1990) Global spatial characteristics of medium-scale undulating inhomogeneities at the height of the F region of the ionosphere at different levels of magnetospheric activity. Moskow, IZMIRAN, preprint 41(926) [in Russian].
5. Fedorenko, А. (2009) Reproduction of atmospheric gravitational wave characteristics in polar regions based on mass spectrometric satellite measurements. Radio physics and radio astronomy, 14(3): 254—265 [in Ukrainian].
6. Chernogor, L. (2009) Radiophysical and geomagnetic effects of missile launches. Monograph. Kharkiv: V. Karazin KhNU [in Russian].
7. Dalgarno, A., Hanson, W. B., Spencer, N. W, Schmerling, E. R. (1973) The Atmosphere Explorer mission. Radio Sci., 8(4): 263—266.
8 Eckner, A. (2017, April 3) Algorithms for Unevenly Spaced Time Series: Moving Averages and Other Rolling Operators. ECKNER.com, from http://www.eckner.com/papers/Algorithms%20for%20Unevenly%20Spaced%20Time... .
9. Eckner, A. (2018, June 11) A Note on Trend and Seasonality Estimation for Unevenly Spaced Time Series, from http://www.eckner.com/papers/Trend%20and%20Seasonality%20Estimation%20fo... .
10. Gross S. H., Reber C. A., Huang F. T. (1984) Large-scale waves in the thermosphere observed by the AE-C satellite. The Transactions on Geoscience and Remote sensing, GE-22(4): 340—351.
11. Hedin, A. E., Mayr, H. G. (1987) Characteristics of Wavelike Fluctuations in Dynamics Explorer Neutral Composition Data. J. Geophys. Res., 92(A10): 11159—11172 [in English].
12. Huang, N. E., Wu, Z. (2008) A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Revs Geophysics, 46(2): 23 p.
13. Innis, J. L., Conde, M. (2002) Characterization of acoustic—gravity waves in the upper thermosphere using Dynamics Explorer 2 Wind and Temperature Spectrometer (WATS) and Neutral Atmosphere Composition Spectrometer (NACS) data. J. Geophys. Res., 107(A12): 1418—1439.
14. Kramer, H. J. (2002) Observation of the Earth and Its Environment: monograph, Springer [in English]. 
15. Rice, C. J., Carter, V. L., LaValle, S. R., Chater, W. T., Jones, D. A., King, C. G., Nelson D. F. (1973) Atmosphere Explorer pressure measurements: Ion gauge and capacitance manometer. Radio Sci., 8(4): 305—314. 
https://doi.org/10.1029/RS008i004p00305
16. Spencer, N. W., Niemann, H. B., Carignan, G. R. (1973) The neutral-atmosphere temperature instrument. Radio Sci., 8(4): 287—296.
17. Spencer, N. W., Wharton, L. E., Niemann, H. B., Hedin, A. E., Carignan, G. R., Maurer, J. C. (1981) The Dynamics Explorer Wind and Temperature Spectrometer. Space Sci. Instrumentation, 5: 417—428.