Skip to main content
Log in

Tracking IMF Fluctuations Nearby Sun Using Wavelet Analysis: Parker Solar Probe First Encounter Data

  • Published:
Geomagnetism and Aeronomy Aims and scope Submit manuscript

Abstract

The Parker Solar Probe (PSP) satellite was launched by NASA in 2018 to study the Sun’s environment from a closer distance than any spacecraft has ever reached before, revealing new insights about our star. Based on past few encounters data, numerous crucial findings have been already discovered; the data collected by the probe during each encounter is serving a unique opportunity to enhance our current understanding of solar behaviour. In the present work, we studied fluctuations on interplanetary magnetic field (IMF) (B) and its components, Bx, By, and Bz, from 0.50 AU (when PSP moved towards the Sun) to 0.17 AU (closest distance covered by PSP from the Sun) to 0.50 AU (when PSP moved back from the Sun) using continuous and discrete wavelet analysis. Continuous wavelet analysis presented localization of abrupt changes in both time and frequency domains. It revealed high power short-term fluctuations between 0.30 AU (when PSP moved towards the Sun) to 0.30 AU (when PSP moved back from the Sun) with key periodicity in the range of 4 to 64 hours. However, Bx showed high-power long-term fluctuation between 0.34 AU (when PSP moved towards the Sun) to 0.40 AU (when PSP moved back from the Sun) on a timescale of 128–256 hours, which was not reported before. Further, we decomposed the data into high and low-frequency signals using discrete wavelet transform (DWT), and the result revealed significant fluctuations on the IMF when the PSP was moving around the closest distance (0.17 AU). CWT and DWT emerged as sophisticated statistical methods capable of detecting variations in solar wind data in order to gain a better understanding of a specific solar event period and uncover scientific understanding about solar behavior near the Sun.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 4.

Similar content being viewed by others

REFERENCES

  1. Adhikari, B., Khatiwada, R., and Chapagain, N.P., Analysis of geomagnetic storms using wavelet transforms, J. Nepal Phys. Soc., 2017, vol. 4, no. 1, pp. 119–124.

    Article  Google Scholar 

  2. Adhikari, B., Dahal, S., Karki, M., Mishra, R.K., Dahal, R.K., Sasmal, S., and Klausner, V., Application of wavelet for seismic wave analysis in Kathmandu Valley after the 2015 Gorkha earthquake, Nepal, Geoenviron. Disasters, 2020, vol. 7, no. 1, pp. 1–16.

    Google Scholar 

  3. Agapitov, O.V., de Wit, T.D., Mozer, F.S., Bonnell, J.W., Drake, J.F., Malaspina, D., et al., Sunward-propagating whistler waves collocated with localized magnetic field holes in the solar wind: Parker Solar Probe observations at 35.7 R radii, Astrophys. J. Lett., 2020, vol. 891, no.1, id L20.

  4. Badman, S.T., Bale, S.D., Martínez Oliveros, J.C., Panasenco, O., Velli, M., Stansby, D., et al., Magnetic connectivity of the ecliptic plane within 0.5 AU: Potential field source surface modeling of the first Parker Solar Probe encounter, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 23.

  5. Bale, S.D., Badman, S.T., Bonnell, J.W., Bowen, T.A., Burgess, D., Case, A.W., et al., Highly structured slow solar wind emerging from an equatorial coronal hole, Nature, 2019, vol. 576, no. 7786, pp. 237–242.

    Article  Google Scholar 

  6. Bale, S.D., Goetz, K., Harvey, P.R., et al. The FIELDS instrument suite for solar probe plus, Space Science Reviews, 2016, vol. 204, no. 1, pp. 49–82.

  7. Balogh, A., Forsyth, R.J., Ahuja, A., Southwood, D.J., Smith, E.J., and Tsurutani, B.T., The interplanetary magnetic field from 1 to 5 AU: Ulysses observations, Adv. Space Res., 1993, vol. 13, no. 6, pp. 15–24.

    Article  Google Scholar 

  8. Barford, L.A., Fazzio, R.S., and Smith, D.R., An Introduction to Wavelets, Hewlett Packard, 1992.

    Google Scholar 

  9. Behannon, K.W., Heliocentric distance dependence of the interplanetary magnetic field, Rev. Geophys., 1978, vol. 16, no. 1, pp. 125–145.

    Article  Google Scholar 

  10. Binod, A., Dahal, S., Kumar, M.R., Nirakar, S., Nidhi, C.D., Ballav, S.S., and Sarala, A., Analysis of solar, interplanetary, and geomagnetic parameters during solar cycles 22, 23, and 24, Russ. J. Earth Sci., 2019, vol. 19 no. 1.

  11. Bruce, A., Donoho, D., and Gao, H.Y., Wavelet analysis for signal processing, IEEE Spectrum, 1996, vol. 33, no. 10, pp. 26–35.

    Article  Google Scholar 

  12. Chattopadhyay, G., and Chattopadhyay, S., Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network, Eur. Phys. J. Plus, 2012, vol. 127, no. 4, pp. 1–8.

    Article  Google Scholar 

  13. Coifman, R.R., Meyer, Y., and Wickerhauser, V., Wavelet analysis and signal processing, in Wavelets and Their Applications, Boston: Jones and Bartlett, 1992, pp. 153–178.

    Google Scholar 

  14. Daubechies, I., Orthonormal bases of compactly supported wavelets, Commun. Pure Appl. Math., 1988, vol. 41, pp. 909–996.

    Article  Google Scholar 

  15. Daubechies, I., Ten Lectures on Wavelets, Philadelphia, Pa.: SIAM, 1992.

    Book  Google Scholar 

  16. Dong, M., Chang, C.P., Gong, Q., and Chu, Y., Revisiting global economic activity and crude oil prices: A wavelet analysis, Econ. Modell., 2019, vol. 78, pp. 134–149.

    Article  Google Scholar 

  17. Du, Z., Ma, L., Li, H., Li, Q., Sun, G., and Liu, Z., Network traffic anomaly detection based on wavelet analysis., in IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA), IEEE, 2018, pp. 94–101.

  18. Dungey, J.W., Interplanetary magnetic field and the auroral zones, Phys. Rev. Lett., 1961, vol. 6, no. 2, id 47.

  19. Echer, E., Bolzan, M.J.A., and Franco, A.M.S., Statistical analysis of solar wind parameter variation with heliospheric distance: Ulysses observations in the ecliptic plane, Adv. Space Res., 2020, vol. 65, no. 12, pp. 2846–2856.

    Article  Google Scholar 

  20. Fairfield, D.H., and Cahill, L.J., Jr., Transition region magnetic field and polar magnetic disturbances, J. Geophys. Res., 1966, vol. 71, no. 1, pp. 155–169.

    Article  Google Scholar 

  21. Farge, M., and Schneider, K., Wavelets: Application to Turbulence, University of Warwick, 2005.

    Google Scholar 

  22. Fargette, N., Lavraud, B., Rouillard, A., Eastwood, J.P., Bale, S.D., and Phan, T., and Bonnell, J. W., Magnetic increases with central current sheets: Observations with Parker Solar Probe, Astron. Astrophys., 2021, vol. 650, id A11.

  23. Fox, N.J., Velli, M.C., Bale, S.D., Decker, R., Driesman, A., and Howard, R.A., and Szabo, A., The Solar Probe Plus mission: Humanity’s first visit to our star, Space Sci. Rev., 2016, vol. 204, no. 1, pp. 7–48.

    Article  Google Scholar 

  24. Gamage, N., and Blumen, W., Comparative analysis of low-level cold fronts: Wavelet, Fourier, and empirical orthogonal function decompositions, Mon. Weather Rev., 1993, vol. 121, no. 10, pp. 2867–2878.

    Article  Google Scholar 

  25. Giacalone, J., Mitchell, D.G., Allen, R.C., Hill, M.E., McNutt, R.L., Jr., Szalay, J.R., et al., Solar energetic particles produced by a slow coronal mass ejection at ∼0.25 au, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 29.

  26. Guarnieri, F.L., Tsurutani, B.T., Vieira, L.E., Hajra, R., Echer, E., Mannucci, A.J., and Gonzalez, W.D., A correlation study regarding the AE index and ACE solar wind data for Alfvénic intervals using wavelet decomposition and reconstruction, Nonlinear Processes Geophys., 2018, vol. 25, no. 1, pp. 67–76.

    Article  Google Scholar 

  27. Haimovich, A.D., Byrne, B., Ramaswamy, R., and Welsh, W.J., Wavelet analysis of DNA walks, J. Comput. Biol., 2006, vol. 13, no. 7, pp. 1289–1298.

    Article  Google Scholar 

  28. Harang, R., Bonnet, G., and Petzold, L.R., WAVOS: A MATLAB toolkit for wavelet analysis and visualization of oscillatory systems, BMC Res. Notes, 2012, vol. 5, no.1, pp. 1–8.

    Article  Google Scholar 

  29. Howard, R.A., Vourlidas, A., Bothmer, V., Colaninno, R.C., DeForest, C.E., Gallagher, B., et al., Near-Sun observations of an F-corona decrease and K-corona fine structure, Nature, 2019, vol. 576, no. 7786, pp. 232–236.

    Article  Google Scholar 

  30. Jaffard, S., Lashermes, B., and Abry, P., Wavelet leaders in multifractal analysis, Wavelet Analysis and Applications, 2006, pp. 201–246, Birkhäuser Basel.

  31. Joyce, C.J., McComas, D.J., Christian, E.R., Schwadron, N.A., and Wiedenbeck, M.E., McNutt, R.L., Jr., et al., Energetic particle observations from the Parker Solar Probe using combined energy spectra from the IS⊙IS instrument suite, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 41.

  32. Kasper, J.C., Bale, S.D., Belcher, J.W., Berthomier, M., Case, A.W., and Chandran, B.D., and Schwadron, N.A., Alfvénic velocity spikes and rotational flows in the near-Sun solar wind, Nature, 2019, vol. 576, no. 7786, pp. 228–231.

    Article  Google Scholar 

  33. Khabarova, O., and Obridko, V., Puzzles of the interplanetary magnetic field in the inner heliosphere, Astrophys. J., 2012, vol. 761, no. 2, id 82.

  34. Kilpua, E.K., Fontaine, D., Good, S.W., Ala-Lahti, M., Osmane, A., Palmerio, E., et al., Magnetic field fluctuation properties of coronal mass ejection-driven sheath regions in the near-Earth solar wind, Ann. Geophys., 2020, vol. 38, no. 5, pp. 999–1017.

    Article  Google Scholar 

  35. Kim, T.K., Pogorelov, N.V., Arge, C.N., Henney, C.J., Jones-Mecholsky, S.I., Smith, W.P., et al., Predicting the solar wind at the Parker Solar Probe using an empirically driven MHD model, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 40.

  36. Kivelson, M.G. and Russell, C.T., Introduction to Space Physics, Cambridge Univ. Press, 1995.

    Book  Google Scholar 

  37. Klausner, V., Mendes, O., Domingues, M.O., Papa, A.R.R., Tyler, R.H., Frick, P., and Kherani, E. A., Advantage of wavelet technique to highlight the observed geomagnetic perturbations linked to the Chilean tsunami (2010), J. Geophys. Res.: Space Phys., 2014, vol. 119, no. 4, pp. 3077–3093.

    Article  Google Scholar 

  38. Klausner, V., Papa, A.R.R., Cândido, C.M.N., Domin-gues, M.O., and Mendes, O., An alternative way to identify local geomagnetically quiet days: A case study using wavelet analysis, Ann. Geophys., 2016, vol. 34, pp. 451–462.

    Article  Google Scholar 

  39. Korreck, K.E., Szabo, A., Chinchilla, T.N., Lavraud, B., Luhmann, J., and Niembro, T., and Wygant, J., Source and propagation of a streamer blowout coronal mass ejection observed by the Parker Solar Probe, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 69.

  40. Krivova, N.A., and Solanki, S.K., The 1.3-year and 156‑day periodicities in sunspot data: Wavelet analysis suggests a common origin, Astron. Astrophys., 2002, vol. 394, no. 2, pp. 701–706.

    Article  Google Scholar 

  41. Kumar, P., and Foufoula-Georgiou, E., Wavelet analysis for geophysical applications, Rev. Geophys., 1997, vol. 35, no. 4, pp. 385–412.

    Article  Google Scholar 

  42. Lee, D.T., and Yamamoto, A., Wavelet analysis: Theory and applications, Hewlett Packard J., 1994, vol. 45, pp. 44–52.

    Google Scholar 

  43. Leske, R.A., Christian, E.R., Cohen, C.M.S., Cummings, A.C., Davis, A.J., and Desai, M.I., and Poduval, B., Observations of the 2019 April 4 solar energetic particle event at the parker solar probe, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 35.

  44. Lu, D., The application of wavelet analysis in image processing, in Proceedings of the 2018 International Conference on Information Hiding and Image Processing, 2018, pp. 36–38.

  45. Lyatsky, W., Tan, A., and Lyatskaya, S., Effect of Sun’s magnetic field polarity on interplanetary magnetic field Bz, Geophys. Res. Lett., 2003, vol. 30, no. 24.

  46. Malandraki, O.E., Sarris, E.T., Lanzerotti, L.J., Trochoutsos, P., Tsiropoula, G., and Pick, M., Solar energetic particles inside a coronal mass ejection event observed with the ACE spacecraft, J. Atmos. Sol.–Terr. Phys., 2002, vol. 64, no. 5–6, pp. 517–525.

    Article  Google Scholar 

  47. McComas, D.J., Christian, E.R., Cohen, C.M.S., Cummings, A.C., Davis, A.J., Desai, M.I., et al., Probing the energetic particle environment near the Sun, Nature, 2019, vol. 576, no. 7786, pp. 223–227.

    Article  Google Scholar 

  48. Mendes, O.J., Dominguesand, M.O., and Mendes, A.C., Wavelet analysis applied to magnetograms, J. Atmos. Sol.–Terr. Phys., 2005, vol. 67, pp. 1827–1836.

    Article  Google Scholar 

  49. Meyer-Vernet, N., Basics of the Solar Wind, Cambridge Univ. Press, 2007.

    Book  Google Scholar 

  50. Meyers, S.D., Kelly, B.G., and O’Brien, J.J., An introduction to wavelet analysis in oceanography and meteorology: With application to the dispersion of Yanai waves, Mon. Weather Rev., 1993, vol. 121, no. 10, pp. 2858–2866.

    Article  Google Scholar 

  51. Mozer, F.S., Agapitov, O.V., Bale, S.D., Bonnell, J.W., Case, T., Chaston, C.C., et al.,, Switchbacks in the solar magnetic field: Their evolution, their content, and their effects on the plasma, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 68.

  52. Ning, J., Moore, C.N., and Nelson, J.C., Preliminary wavelet analysis of genomic sequences, in Computational Systems Bioinformatics, CSB2003, Proceedings of the 2003 IEEE Bioinformatics Conference, IEEE, 2003, pp. 509–510.

  53. Ojeda, A., Gonzalez, W.D., Mendes, O., Domingues, M.O., and Rosa, R.R., Nonlinear fluctuation analysis for a set of 41 magnetic clouds measured by the Advanced Composition Explorer (ACE) spacecraft, Nonlinear Processes Geophys., 2014, vol. 21, pp. 1059–1073.

    Article  Google Scholar 

  54. Panasenco, O., Velli, M., D’amicis, R., Shi, C., Reville, V., Bale, S.D., et al., Exploring solar wind origins and connecting plasma flows from the Parker Solar Probe to 1 AU: Nonspherical source surface and Alfvénic fluctuations, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 54.

  55. Parashar, T.N., Goldstein, M.L., Maruca, B.A., Matthaeus, W.H., Ruffolo, D., Bandyopadhyay, R., et al., Measures of scale-dependent Alfvénicity in the first PSP solar encounter, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 58.

  56. Parker Solar Probe, Science Gateway, 2021. https://sppgway.jhuapl.edu/.

  57. Phan, T.D., Bale, S.D., Eastwood, J.P., Lavraud, B., Drake, J.F., and Oieroset, M., and Velli, M., Parker solar probe in situ observations of magnetic reconnection exhausts during encounter 1, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 34.

  58. Poudel, P., Simkhada, S., Adhikari, B., Sharma, D., and Nakarmi, J.J., Variation of solar wind parameters along with the understanding of energy dynamics within the magnetospheric system during geomagnetic disturbances, Earth Space Sci., 2019, vol. 6, no. 2, pp. 276–293.

    Article  Google Scholar 

  59. Poudel, P., Parajuli, N., Gautam, A., Sapkota, D., Adhikari, H., Adhikari, B., et al., Wavelet and cross-correlation analysis of relativistic electron flux with sunspot number, solar flux, and solar wind parameters, J. Nepal Phys. Soc., 2020, vol. 6, no. 2, pp. 104–112.

    Article  Google Scholar 

  60. Prabhakar, S., Mohanty, A.R., and Sekhar, A.S., Application of discrete wavelet transform for detection of ball bearing race faults, Tribol. Int., 2002, vol. 35, no. 12, pp. 793–800.

    Article  Google Scholar 

  61. Priest, E., Magnetohydrodynamics of the Sun, Cambridge Univ. Press, 2014.

    Book  Google Scholar 

  62. Riabova, S., Application of wavelet analysis to the analysis of geomagnetic field variations, J. Phys.: Conf. Ser., 2018, vol. 1141, no. 1, id 012146.

  63. Sanchez-Diaz, E., Rouillard, A.P., Davies, J.A., Lavraud, B., Pinto, R.F., and Kilpua, E., The temporal and spatial scales of density structures released in the slow solar wind during solar activity maximum, Astrophys. J., 2017, vol. 851, no. 1, id 32.

  64. Sanchez-Diaz, E., Rouillard, A.P., Lavraud, B., Kilpua, E., and Davies, J.A., In situ measurements of the variable slow solar wind near sector boundaries, Astrophys. J., 2019, vol. 882, no. 1, pp. 51.

    Article  Google Scholar 

  65. Santos, C.A., Galvao, C.D.O., Suzuki, K., and Trigo, R.M., Matsuyama city rainfall data analysis using wavelet transform, Proc. Hydraul. Eng., 2001, vol. 45, pp. 211–216.

    Article  Google Scholar 

  66. Siddiqi, A.A., Beyond Earth: A Chronicle of Deep Space Exploration, 1958–2016, NASA Office of Communications, NASA History Division, 2018.

    Google Scholar 

  67. Sifuzzaman, M., Islam, M.R., and Ali, M.Z., Application of wavelet transform and its advantages compared to Fourier transform, J. Phys. Sci., 2009, vol. 13, pp. 121–134.

    Google Scholar 

  68. Silwal, A., Gautam, S. P., Chaudhary, K., Khanal, M., Joshi, S., Dangaura, S., and Adhikari, B., Study of solar wind parameters during geomagnetic storm of August 26, 2018 and September 28 2017, Thai J. Phys., 2021, vol. 38, no. 2, pp. 54–68.

    Google Scholar 

  69. Solanki, S.K., Solar variability and climate change: Is there a link?, Astron. Geophys., 2002, vol. 43, no. 5, pp. 5–9.

    Article  Google Scholar 

  70. Subedi, A., Adhikari, B., and Mishra, R.K., Variation of solar wind parameters during intense geomagnetic storms, Himalayan Phys., 2017, vol. 6, pp. 80–85.

    Article  Google Scholar 

  71. Szabo, A., Larson, D., Whittlesey, P., Stevens, M.L., Lavraud, B., Phan, T., et al., The heliospheric current sheet in the inner heliosphere observed by the parker solar probe, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 47.

  72. Tary, J.B., Herrera, R.H., and van der Baan, M., Analysis of time-varying signals using continuous wavelet and synchro squeezed transforms, Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci., 2018, vol. 376, no. 2126, id 20170254.

  73. Teolis, A. and Benedetto, J. J., Computational Signal Processing with Wavelets, Boston, Mass., Birkhäuser, 1988.

    Google Scholar 

  74. Torrence, C., and Compo, G.P., A practical guide to wavelet analysis, Bull. Am. Meteorol. Soc., 1998, vol. 79, no. 1, pp. 61–78.

    Article  Google Scholar 

  75. Venzmer, M.S., and Bothmer, V., Solar-wind predictions for the Parker Solar Probe Orbit–Near-Sun extrapolations derived from an empirical solar-wind model based on Helios and OMNI observations, Astron. Astrophys., 2018, vol. 611, id A36.

  76. Veselovsky, I.S., and Kaportseva, K.B., Role of averaging in statistical analysis of solar wind data from the DSCOVR spacecraft for the first year of operation, Geomagn. Aeron. (Engl. Transl.), 2019, vol. 59, pp. 3, pp. 257–264.

  77. Webb, D.F., and Howard, R.A., The solar cycle variation of coronal mass ejections and the solar wind mass flux, J. Geophys. Res.: Space Phys., 1994, vol. 99, no. A3, pp. 4201–4220.

    Article  Google Scholar 

  78. Wiedenbeck, M.E., Bučik, R., Mason, G.M., Ho, G.C., Leske, R.A., Cohen, et al., 3He-rich solar energetic particle observations at the Parker Solar Probe and near Earth, Astrophys. J. Suppl. Ser., 2020, vol. 246, no. 2, id 42.

  79. Wirsing, K., Time frequency analysis of wavelet and Fourier transform, in Wavelet Theory, IntechOpen, 2020.

    Google Scholar 

  80. Woo, R., Radial dependence of solar wind properties deduced from Helios 1/2 and Pioneer 10/11 radio scattering observations, Astrophys. J., 1978, vol. 219, pp. 727–739.

    Article  Google Scholar 

  81. Yashiro, S., Gopalswamy, N., and Michalek, G., St. Cyr, O.C., Plunkett, S P., Rich, N.B., and Howard, R.A., A catalog of white light coronal mass ejections observed by the SOHO spacecraft, J. Geophys. Res.: Space Phys., 2004, vol. 109, no. A7.

  82. Zhang, J., Dere, K.P., Howard, R.A., and Bothmer, V., Identification of solar sources of major geomagnetic storms between 1996 and 2000, Astrophys. J., 2003, vol. 582, no. 1, id 520.

  83. Zhang, Y., Liu, B., Ji, X., and Huang, D., Classification of EEG signals based on autoregressive model and wavelet packet decomposition, Neural Process. Lett., 2017, vol. 45, no. 2, pp. 365–378.

    Article  Google Scholar 

Download references

5. ACKNOWLEDGMENTS

We want to acknowledge Stuart D. Bale, UC Berkeley Space Science Laboratory, and Natalia Papitashvili, GSFC, NASA, for providing data used in this work via the PSP data explorer platform CDAWeb: https://cdaweb. gsfc.nasa.gov/cgi-bin/eval1.cgi. All the datasets used for this work can be assessed from the CSAWeb through the website previously mentioned.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Silwal.

Ethics declarations

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest.

AUTHOR’S CONTRIBUTION

The authors S.P. Gautam, A. Silwal, and B. Adhikari combinedly developed the idea of this manuscript. S.P. Gautam and A. Silwal have worked on the overall preparation of the manuscript; they have major contributions to the computational and data analysis parts. A. Basyal, K. Chaudhary, M. Khanal, and B. Ale have prepared the introduction and methodology sections. P. Paudel and M. Karki have worked on grammar corrections, and adding necessary information in all the sections. This work was conducted under the regular guidance of the research advisors, B. Adhikari and N.P. Chapagain.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gautam, S.P., Silwal, A., Bashyal, A. et al. Tracking IMF Fluctuations Nearby Sun Using Wavelet Analysis: Parker Solar Probe First Encounter Data. Geomagn. Aeron. 62, 138–150 (2022). https://doi.org/10.1134/S0016793222020074

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0016793222020074

Navigation