Mostrar el registro sencillo del ítem
A multifractal approach to understanding Forbush Decrease events: Correlations with geomagnetic storms and space weather phenomena
dc.contributor.author | Sierra Porta, David | |
dc.date.accessioned | 2024-06-12T16:30:47Z | |
dc.date.available | 2024-06-12T16:30:47Z | |
dc.date.issued | 2024-05-28 | |
dc.date.submitted | 2024-06-12 | |
dc.identifier.citation | Sierra Porta, D. (2024). A multifractal approach to understanding Forbush Decrease events: Correlations with geomagnetic storms and space weather phenomena. sciencedirect, 185. https://doi.org/10.1016/j.chaos.2024.115089 | spa |
dc.identifier.uri | https://hdl.handle.net/20.500.12585/12677 | |
dc.description.abstract | The Forbush decrease phenomenon has significant impacts on several environmental conditions, including interference in radio communications, satellite navigation systems, and the health of astronauts in space, among others. It is characterized by a temporary and noticeable reduction in the observed flux of galactic cosmic rays recorded at the Earth’s surface. This decrease occurs due to the modulation of cosmic rays through their interaction with shock waves generated by coronal mass ejections. As these shock waves traverse the interplanetary medium, which includes the solar wind and galactic cosmic rays, they exert compression forces on the cosmic ray flux, leading to a reduction in observed flux levels at Earth. This study investigates Forbush Decrease events across different solar cycles and explores their correlation with geomagnetic storm conditions using multifractal detrended fluctuation analysis. The findings indicate variations in the multifractal spectra for series under different geomagnetic storm conditions compared to the full Forbush decrease series. Moreover, it is observed that the amplitude of the multifractal spectrum is greater in the series that include events with a maximum index exceeding 6, suggesting a significant influence of geomagnetic storm conditions on the fractality and variability of Forbush Decrease magnitudes. | spa |
dc.format.extent | 13 páginas | |
dc.format.mimetype | application/pdf | spa |
dc.language.iso | eng | spa |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.source | Sciencedirect, vol. 185 | spa |
dc.title | A multifractal approach to understanding Forbush Decrease events: Correlations with geomagnetic storms and space weather phenomena | spa |
dcterms.bibliographicCitation | Gabici S. Low-energy cosmic rays: regulators of the dense interstellar medium. Astron Astrophys Rev 2022;30(1):4. http://dx.doi.org/10.1007/s00159-022- 00141-2. | spa |
dcterms.bibliographicCitation | Owens MJ, Usoskin I, Lockwood M. Heliospheric modulation of galactic cosmic rays during grand solar minima: Past and future variations. Geophys Res Lett 2012;39(19). http://dx.doi.org/10.1029/2012GL053151. | spa |
dcterms.bibliographicCitation | Zhao L-L, Qin G, Zhang M, Heber B. Modulation of galactic cosmic rays during the unusual solar minimum between cycles 23 and 24. J Geophys Res Space Phys 2014;119(3):1493–506. http://dx.doi.org/10.1002/2013JA019550 | spa |
dcterms.bibliographicCitation | Forbush SE. On the effects in cosmic-ray intensity observed during the recent magnetic storm. Phys Rev 1937;51(12):1108. http://dx.doi.org/10.1103/ PhysRev.51.1108.3 | spa |
dcterms.bibliographicCitation | Forbush S. On cosmic-ray effects associated with magnetic storms. Terr Magn Atmos Electr 1938;43(3):203–18. http://dx.doi.org/10.1029/TE043i003p00203. | spa |
dcterms.bibliographicCitation | Forbush SE. On world-wide changes in cosmic-ray intensity. Phys Rev 1938;54(12):975. http://dx.doi.org/10.1103/PhysRev.54.975. | spa |
dcterms.bibliographicCitation | Cho K-S, Bong S-C, Moon Y-J, Dryer M, Lee S-E, Kim K-H. An empirical relationship between coronal mass ejection initial speed and solar wind dynamic pressure. J Geophys Res Space Phys 2010;115(A10). http://dx.doi.org/10.1029/ 2009JA015139 | spa |
dcterms.bibliographicCitation | Gosling J, Bame S, McComas D, Phillips J. Coronal mass ejections and large geomagnetic storms. Geophys Res Lett 1990;17(7):901–4. http://dx.doi.org/10. 1029/GL017i007p00901 | spa |
dcterms.bibliographicCitation | Lockwood JA. Forbush decreases in the cosmic radiation. Space Sci Rev 1971;12(5):658–715. http://dx.doi.org/10.1007/BF00173346. | spa |
dcterms.bibliographicCitation | Barouch E, Burlaga L. Causes of Forbush decreases and other cosmic ray variations. J Geophys Res 1975;80(4):449–56. http://dx.doi.org/10.1029/ JA080i004p00449. | spa |
dcterms.bibliographicCitation | Iucci N, Parisi M, Storini M, Villoresi G. Forbush decreases: origin and development in the interplanetary space. Il Nuovo Cimento C 1979;2(1):1–52. http://dx.doi.org/10.1007/BF02507712. | spa |
dcterms.bibliographicCitation | Ifedili S. The two-step Forbush decrease: An empirical model. J Geophys Res Space Phys 2004;109(A2). http://dx.doi.org/10.1029/2002JA009814. | spa |
dcterms.bibliographicCitation | Cane HV. Coronal mass ejections and Forbush decreases. In: Cosmic rays and earth: proceedings of an ISSI workshop, 21–26 March 1999, Bern, Switzerland. Springer; 2000, p. 55–77. http://dx.doi.org/10.1007/978-94-017-1187-6_4. | spa |
dcterms.bibliographicCitation | Belov A, Abunin A, Abunina M, Eroshenko E, Oleneva V, Yanke V, Papaioannou A, Mavromichalaki H, Gopalswamy N, Yashiro S. Coronal mass ejections and non-recurrent Forbush decreases. Sol Phys 2014;289:3949–60. http://dx.doi.org/ 10.1007/s11207-014-0534-6. | spa |
dcterms.bibliographicCitation | Richardson I, Cane H. Geoeffectiveness (Dst and Kp) of interplanetary coronal mass ejections during 1995–2009 and implications for storm forecasting. Space Weather 2011;9(7). http://dx.doi.org/10.1029/2011SW000670. | spa |
dcterms.bibliographicCitation | Papaioannou A, Belov A, Abunina M, Eroshenko E, Abunin A, Anastasiadis A, Patsourakos S, Mavromichalaki H. Interplanetary coronal mass ejections as the driver of non-recurrent Forbush decreases. Astrophys J 2020;890(2):101. http: //dx.doi.org/10.3847/1538-4357/ab6bd1. | spa |
dcterms.bibliographicCitation | Nitta NV, Mulligan T, Kilpua EK, Lynch BJ, Mierla M, O’Kane J, Pagano P, Palmerio E, Pomoell J, Richardson IG, et al. Understanding the origins of problem geomagnetic storms associated with ‘‘stealth’’ coronal mass ejections. Space Sci Rev 2021;217(8):82. http://dx.doi.org/10.1007/s11214-021-00857-0. | spa |
dcterms.bibliographicCitation | Belov A, Eroshenko E, Oleneva V, Struminsky A, Yanke V. What determines the magnitude of Forbush decreases? Adv Space Res 2001;27(3):625–30. http: //dx.doi.org/10.1016/S0273-1177(01)00095-3. | spa |
dcterms.bibliographicCitation | Smith EJ. The heliospheric current sheet and modulation of galactic cosmic rays. J Geophys Res Space Phys 1990;95(A11):18731–43. http://dx.doi.org/10.1029/ JA095iA11p18731. | spa |
dcterms.bibliographicCitation | Matzka J, Stolle C, Yamazaki Y, Bronkalla O, Morschhauser A. The geomagnetic Kp index and derived indices of geomagnetic activity. Space Weather 2021;19(5):e2020SW002641. http://dx.doi.org/10.1029/2020SW002641 | spa |
dcterms.bibliographicCitation | Elliott HA, Jahn J-M, McComas DJ. The Kp index and solar wind speed relationship: Insights for improving space weather forecasts. Space Weather 2013;11(6):339–49. http://dx.doi.org/10.1002/swe.20053. | spa |
dcterms.bibliographicCitation | ] Wanliss JA, Showalter KM. High-resolution global storm index: Dst versus SYM-H. J Geophys Res Space Phys 2006;111(A2). http://dx.doi.org/10.1029/ 2005JA011034. | spa |
dcterms.bibliographicCitation | Neupert WM, Pizzo V. Solar coronal holes as sources of recurrent geomagnetic disturbances. J Geophys Res 1974;79(25):3701–9. http://dx.doi.org/10.1029/ JA079i025p03701. | spa |
dcterms.bibliographicCitation | Baker D, Li X, Turner N, Allen J, Bargatze L, Blake J, Sheldon R, Spence HE, Belian R, Reeves G, et al. Recurrent geomagnetic storms and relativistic electron enhancements in the outer magnetosphere: ISTP coordinated measurements. J Geophys Res Space Phys 1997;102(A7):14141–8. http://dx.doi.org/10.1029/ 97JA00565 | spa |
dcterms.bibliographicCitation | Chertok I, Grechnev V, Belov A, Abunin A. Magnetic flux of EUV arcade and dimming regions as a relevant parameter for early diagnostics of solar eruptions– sources of non-recurrent geomagnetic storms and Forbush decreases. Sol Phys 2013;282:175–99. http://dx.doi.org/10.1007/s11207-012-0127-1 | spa |
dcterms.bibliographicCitation | Patra SN, Ghosh K, Panja SC. Scaling and fractal dimension analysis of daily Forbush decrease data. Int J Electron Eng Res 2011;3(2):237–46 | spa |
dcterms.bibliographicCitation | Gil A, Modzelewska R, Moskwa S, Siluszyk A, Siluszyk M, Wawrzynczak A. Indicators of space weather events in cosmic rays during the solar cycle 24. In: 36th international cosmic ray conference - ICRC2019-July 24th - August 1st, 2019 Madison, WI, U.S.A. 2010. | spa |
dcterms.bibliographicCitation | Kozlov V. Forecasting extreme space-weather events on the basis of cosmicray fluctuations. Cosmic Res 2022;60(2):79–88. http://dx.doi.org/10.1134/ S0010952522010063 | spa |
dcterms.bibliographicCitation | Papailiou M, Mavromichalaki H, Belov A, Eroshenko E, Yanke V. The asymptotic longitudinal cosmic ray intensity distribution as a precursor of Forbush decreases. Sol Phys 2012;280:641–50. http://dx.doi.org/10.1007/s11207-012-9945-4. | spa |
dcterms.bibliographicCitation | Papailiou M, Abunina M, Mavromichalaki H, Belov A, Abunin A, Eroshenko E, Yanke V. Precursory signs of large Forbush decreases. Sol Phys 2021;296(6):100. http://dx.doi.org/10.1007/s11207-021-01844-y. | spa |
dcterms.bibliographicCitation | Dumbović M, Vršnak B, Čalogović J, Župan R. Cosmic ray modulation by different types of solar wind disturbances. Astron Astrophys 2012;538:A28. http://dx.doi.org/10.1051/0004-6361/201117710. | spa |
dcterms.bibliographicCitation | Zhang X, Zhang G, Qiu L, Zhang B, Sun Y, Gui Z, Zhang Q. A modified multifractal detrended fluctuation analysis (MFDFA) approach for multifractal analysis of precipitation in dongting lake basin, China. Water 2019;11(5):891. http://dx.doi.org/10.3390/w11050891 | spa |
dcterms.bibliographicCitation | Zhang L, Li H, Liu D, Fu Q, Li M, Faiz MA, Ali S, Khan MI, Li T. Application of an improved multifractal detrended fluctuation analysis approach for estimation of the complexity of daily precipitation. Int J Climatol 2021;41(9):4653–71. http://dx.doi.org/10.1002/joc.7092. | spa |
dcterms.bibliographicCitation | Chakraborty S, Chattopadhyay S. Exploring the Indian summer monsoon rainfall through multifractal detrended fluctuation analysis and the principle of entropy maximization. Earth Sci Inform 2021;14(3):1571–7. http://dx.doi.org/10.1007/ s12145-021-00641-2. | spa |
dcterms.bibliographicCitation | Stavroyiannis S, Babalos V, Bekiros S, Lahmiri S, Uddin GS. The high frequency multifractal properties of bitcoin. Phys A 2019;520:62–71. http://dx.doi.org/10. 1016/j.physa.2018.12.037. | spa |
dcterms.bibliographicCitation | Zhang X, Yang L, Zhu Y. Analysis of multifractal characterization of bitcoin market based on multifractal detrended fluctuation analysis. Phys A 2019;523:973–83. http://dx.doi.org/10.1016/j.physa.2019.04.149. | spa |
dcterms.bibliographicCitation | Miloş LR, Haţiegan C, Miloş MC, Barna FM, Boţoc C. Multifractal detrended fluctuation analysis (MF-DFA) of stock market indexes. Empirical evidence from seven central and eastern European markets. Sustainability 2020;12(2):535. http: //dx.doi.org/10.3390/su12020535 | spa |
dcterms.bibliographicCitation | Gu R, Chen H, Wang Y. Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis. Phys A 2010;389(14):2805–15. http://dx.doi.org/10.1016/j.physa.2010.03.003 | spa |
dcterms.bibliographicCitation | Yang L, Zhu Y, Wang Y. Multifractal characterization of energy stocks in China: A multifractal detrended fluctuation analysis. Phys A 2016;451:357–65. http: //dx.doi.org/10.1016/j.physa.2016.01.100. | spa |
dcterms.bibliographicCitation | Fuwape I, Ogunjo S, Akinsusi J, Rabiu B, Jenkins G. Multifractal detrended fluctuation analysis of particulate matter and atmospheric variables at different time scales. Meteorol Atmos Phys 2023;135(3):27. http://dx.doi.org/10.1007/ s00703-023-00971-4. | spa |
dcterms.bibliographicCitation | Shang P, Lu Y, Kamae S. Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis. Chaos Solitons Fractals 2008;36(1):82–90. http://dx.doi.org/10.1016/j.chaos.2006.06.019. | spa |
dcterms.bibliographicCitation | Zhao X, Shang P, Lin A, Chen G. Multifractal Fourier detrended cross-correlation analysis of traffic signals. Phys A 2011;390(21–22):3670–8. http://dx.doi.org/10. 1016/j.physa.2011.06.018. | spa |
dcterms.bibliographicCitation | Movahed MS, Jafari G, Ghasemi F, Rahvar S, Tabar MRR. Multifractal detrended fluctuation analysis of sunspot time series. J Stat Mech Theory Exp 2006;2006(02):P02003. http://dx.doi.org/10.1088/1742-5468/2006/ 02/P02003. | spa |
dcterms.bibliographicCitation | Hu J, Gao J, Wang X. Multifractal analysis of sunspot time series: the effects of the 11-year cycle and Fourier truncation. J Stat Mech Theory Exp 2009;2009(02):P02066. http://dx.doi.org/10.1088/1742-5468/2009/ 02/P02066. | spa |
dcterms.bibliographicCitation | Sierra-Porta D, Domínguez-Monterroza A-R. Linking cosmic ray intensities to cutoff rigidity through multifractal detrented fluctuation analysis. Phys A 2022;607:128159. http://dx.doi.org/10.1016/j.physa.2022.128159. | spa |
dcterms.bibliographicCitation | Christodoulakis J, Varotsos C, Mavromichalaki H, Efstathiou M, Gerontidou M. On the link between atmospheric cloud parameters and cosmic rays. J Atmos Sol-Terr Phys 2019;189:98–106. http://dx.doi.org/10.1016/j.jastp.2019.04.012. | spa |
dcterms.bibliographicCitation | Sierra-Porta D. On the fractal properties of cosmic rays and sun dynamics crosscorrelations. Astrophys Space Sci 2022;367(12):1–14. http://dx.doi.org/10.1007/ s10509-022-04151-5. | spa |
dcterms.bibliographicCitation | Echeverría S, Moya PS, Pastén D. On the multifractality of plasma turbulence in the solar wind. Proc Int Astron Union 2019;15(S354):371–4. http://dx.doi.org/ 10.1017/S1743921320000514. | spa |
dcterms.bibliographicCitation | Kasde SK, Sondhiya DK, Gwal AK. Multifractal detrended fluctuation analysis of solar wind parameters during solar cycle 23. In: 42nd COSPAR scientific assembly. Vol. 42, 2018, p. E2–3, url:https://www.cospar-assembly.org/abstractcd/ COSPAR-18/abstracts/E2.3-0042-18.pdf. | spa |
dcterms.bibliographicCitation | Babu SS, Unnikrishnan K. Analysis of fractal properties of horizontal component of earth’s magnetic field of different geomagnetic conditions using MFDFA. Adv Space Res 2023. http://dx.doi.org/10.1016/j.asr.2023.05.052. | spa |
dcterms.bibliographicCitation | Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE. Multifractal detrended fluctuation analysis of nonstationary time series. Phys A 2002;316(1–4):87–114. http://dx.doi.org/10.1016/S0378-4371(02) 01383-3 | spa |
dcterms.bibliographicCitation | Peng C-K, Buldyrev SV, Havlin S, Simons M, Stanley HE, Goldberger AL. Mosaic organization of DNA nucleotides. Phys Rev E 1994;49(2):1685. http://dx.doi. org/10.1103/PhysRevE.49.1685 | spa |
dcterms.bibliographicCitation | Ossadnik S, Buldyrev S, Goldberger A, Havlin S, Mantegna R, Peng C, Simons M, Stanley H. Correlation approach to identify coding regions in DNA sequences. Biophys J 1994;67(1):64–70. http://dx.doi.org/10.1016/S0006-3495(94)80455- 2. | spa |
dcterms.bibliographicCitation | Zhou W-X, et al. Multifractal detrended cross-correlation analysis for two nonstationary signals. Phys Rev E 2008;77(6):066211. http://dx.doi.org/10.1103/ PhysRevE.77.066211. | spa |
dcterms.bibliographicCitation | Jiang Z-Q, Zhou W-X, et al. Multifractal detrending moving-average crosscorrelation analysis. Phys Rev E 2011;84(1):016106. http://dx.doi.org/10.1103/ PhysRevE.84.016106. | spa |
dcterms.bibliographicCitation | Kristoufek L. Multifractal height cross-correlation analysis: A new method for analyzing long-range cross-correlations. Europhys Lett 2011;95(6):68001. http: //dx.doi.org/10.1209/0295-5075/95/68001. | spa |
dcterms.bibliographicCitation | Hedayatifar L, Vahabi M, Jafari G. Coupling detrended fluctuation analysis for analyzing coupled nonstationary signals. Phys Rev E 2011;84(2):021138. http://dx.doi.org/10.1103/PhysRevE.84.021138. | spa |
dcterms.bibliographicCitation | Di Matteo T, Aste T, Dacorogna MM. Scaling behaviors in differently developed markets. Physica A 2003;324(1–2):183–8. http://dx.doi.org/10.1016/ S0378-4371(02)01996-9. | spa |
dcterms.bibliographicCitation | Di Matteo T, Aste T, Dacorogna MM. Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development. J Bank Finance 2005;29(4):827–51. http://dx.doi.org/10.1016/j. jbankfin.2004.08.004. | spa |
dcterms.bibliographicCitation | Di Matteo T. Multi-scaling in finance. Quant Finance 2007;7(1):21–36. http: //dx.doi.org/10.1080/14697680600969727 | spa |
dcterms.bibliographicCitation | Pamuła G, Grech D. Influence of the maximal fluctuation moment order q on multifractal records normalized by finite-size effects. Europhys Lett 2014;105(5):50004. http://dx.doi.org/10.1209/0295-5075/105/50004. | spa |
dcterms.bibliographicCitation | Grech D, Pamuła G. On the multifractal effects generated by monofractal signals. Phys A 2013;392(23):5845–64. http://dx.doi.org/10.1016/j.physa.2013.07.045. | spa |
dcterms.bibliographicCitation | López JL, Contreras JG. Performance of multifractal detrended fluctuation analysis on short time series. Phys Rev E 2013;87(2):022918. http://dx.doi.org/ 10.1103/PhysRevE.87.022918. | spa |
dcterms.bibliographicCitation | Drożdż S, Kwapień J, Oświecimka P, Rak R. Quantitative features of multifractal subtleties in time series. Europhys Lett 2010;88(6):60003. http://dx.doi.org/10. 1209/0295-5075/88/60003. | spa |
dcterms.bibliographicCitation | Dong Q, Wang Y, Li P. Multifractal behavior of an air pollutant time series and the relevance to the predictability. Environ Pollut 2017;222:444–57. http: //dx.doi.org/10.1016/j.envpol.2016.11.090. | spa |
dcterms.bibliographicCitation | ] Grech D, Pamuła G. Multifractal background noise of monofractal signals. Acta Phys Pol A 2012;121(2B). doi:https://bibliotekanauki.pl/articles/1408977.pdf. | spa |
dcterms.bibliographicCitation | Rak R, Grech D. Quantitative approach to multifractality induced by correlations and broad distribution of data. Phys A 2018;508:48–66. http://dx.doi.org/10. 1016/j.physa.2018.05.059 | spa |
dcterms.bibliographicCitation | Barunik J, Kristoufek L. On hurst exponent estimation under heavy-tailed distributions. Physica A 2010;389(18):3844–55. http://dx.doi.org/10.1016/j.physa. 2010.05.025. | spa |
dcterms.bibliographicCitation | Mielniczuk J, Wojdyłło P. Estimation of hurst exponent revisited. Comput Stat Data Anal 2007;51(9):4510–25. http://dx.doi.org/10.1016/j.csda.2006.07.033. | spa |
dcterms.bibliographicCitation | Barnes G, Leka K, Schrijver C, Colak T, Qahwaji R, Ashamari O, Yuan Y, Zhang J, McAteer R, Bloomfield D, et al. A comparison of flare forecasting methods. I. Results from the ‘‘all-clear’’ workshop. Astrophys J 2016;829(2):89. http://dx.doi.org/10.3847/0004-637X/829/2/89. | spa |
dcterms.bibliographicCitation | Leka K, Park S-H, Kusano K, Andries J, Barnes G, Bingham S, Bloomfield DS, McCloskey AE, Delouille V, Falconer D, et al. A comparison of flare forecasting methods. II. Benchmarks, metrics, and performance results for operational solar flare forecasting systems. Astrophys J Suppl Ser 2019;243(2):36. http://dx.doi. org/10.3847/1538-4365/ab2e12 | spa |
dcterms.bibliographicCitation | Leka K, Park S-H, Kusano K, Andries J, Barnes G, Bingham S, Bloomfield DS, McCloskey AE, Delouille V, Falconer D, et al. A comparison of flare forecasting methods. III. Systematic behaviors of operational solar flare forecasting systems. Astrophys J 2019;881(2):101. http://dx.doi.org/10.3847/1538-4357/ab2e11. | spa |
dcterms.bibliographicCitation | Park S-H, Leka K, Kusano K, Andries J, Barnes G, Bingham S, Bloomfield DS, McCloskey AE, Delouille V, Falconer D, et al. A comparison of flare forecasting methods. IV. Evaluating consecutive-day forecasting patterns. Astrophys J 2020;890(2):124. http://dx.doi.org/10.3847/1538-4357/ab65f0 | spa |
dcterms.bibliographicCitation | Ledvina VE, Palmerio E, McGranaghan RM, Halford AJ, Thayer A, Brandt L, MacDonald EA, Bhaskar A, Dong C, Altintas I, et al. How open data and interdisciplinary collaboration improve our understanding of space weather: A risk and resiliency perspective. Front Astron Space Sci 2022;9:1067571. http: //dx.doi.org/10.3389/fspas.2022.1067571. | spa |
dcterms.bibliographicCitation | Riley P, Baker D, Liu YD, Verronen P, Singer H, Güdel M. Extreme space weather events: From cradle to grave. Space Sci Rev 2018;214:1–24. http: //dx.doi.org/10.1007/s11214-017-0456-3. | spa |
dcterms.bibliographicCitation | Riley P, Baker D, Liu YD, Verronen P, Singer H, Güdel M. Extreme space weather events: From cradle to grave. Space Sci Rev 2018;214:1–24. http: //dx.doi.org/10.1007/s11214-017-0456-3. | spa |
dcterms.bibliographicCitation | Tsurutani BT, Lakhina GS, Hajra R. The physics of space weather/solar-terrestrial physics (STP): what we know now and what the current and future challenges are. Nonlinear Process Geophys 2020;27(1):75–119. http://dx.doi.org/10.5194/ npg-27-75-2020 | spa |
dcterms.bibliographicCitation | Kusano K, Ichimoto K, Ishii M, Miyoshi Y, Yoden S, Akiyoshi H, Asai A, Ebihara Y, Fujiwara H, Goto T-N, et al. PSTEP: project for solar–terrestrial environment prediction. Earth Planets Space 2021;73:1–29. http://dx.doi.org/ 10.1186/s40623-021-01486-1. | spa |
datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
oaire.version | http://purl.org/coar/version/c_b1a7d7d4d402bcce | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.hasversion | info:eu-repo/semantics/draft | spa |
dc.identifier.doi | 10.1016/j.chaos.2024.115089 | |
dc.subject.keywords | Multifractal behavior | spa |
dc.subject.keywords | Forbush decrease | spa |
dc.subject.keywords | Space weather | spa |
dc.subject.keywords | Cosmic rays | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.rights.cc | CC0 1.0 Universal | * |
dc.identifier.instname | Universidad Tecnológica de Bolívar | spa |
dc.identifier.reponame | Repositorio Universidad Tecnológica de Bolívar | spa |
dc.publisher.place | Cartagena de Indias | spa |
dc.subject.armarc | LEMB | |
dc.type.spa | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
dc.audience | Público general | spa |
oaire.resourcetype | http://purl.org/coar/resource_type/c_2df8fbb1 | spa |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
Productos de investigación [1453]
Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.