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Record W2760950200 · doi:10.15407/rpra22.03.201

COMPARISON OF AIR TEMPERATURE VARIATIONS ON THE AFRICAN CONTINENT AND THE SCHUMANN RESONANCE INTENSITY BY USING LONG-TERM ANTARCTIC OBSERVATIONS

2017· article· en· W2760950200 on OpenAlexaboutno aff
A. V. Paznukhov, Yu. M. Yampolski, A. P. Nickolaenko, A. V. Koloskov

Bibliographic record

VenueRadio physics and radio astronomy · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsSchumann resonancesThunderstormIntensity (physics)Lightning (connector)Atmospheric sciencesMeteorologyClimatologyEnvironmental scienceCorrelation coefficientResonance (particle physics)PhysicsIonosphereGeologyOpticsGeophysicsPower (physics)Statistics

Abstract

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PACS numbers: 92.60.Pw, 93.30.Bz, 93.30.Ca Purpose: Correlation study of long-term seasonal variations of intensity of the global electromagnetic (Schumann) resonance in the Earth-ionosphere cavity and the air temperature for the African center of the global thunderstorm activity. Design/methodology/approach: The correlation analysis of the time series was used. By using the 13-year data (since 2002 till 2015) of monitoring the natural ELF noise at the Ukrainian Antarctic Vernadsky station, the seasonal variations in intensity of the first Schumann resonance mode were derived, driven by the lightning activity in the African thunderstorm center. The average air temperature of the African continent over the same period was estimated from the data collected by the global network of meteorological stations. The area of maximum thunderstorm activity in Africa was approximated by a simple geometric figure. The correction was made for the source distance (the lightning discharges) when estimating the power of the first resonant maximum in the ELF signal. A stable relationship between the air temperature and the thunderstorm activity at the African continent was established as a result of correlation processing of seasonal variations in the air temperature and the field intensity. Findings: A one month lag between the annual maximum resonance intensity was found with regard to the maximum of air temperature relevant to the delay in the formation of thunderstorms during transition from the dry to the rainy seasons in Africa. The cross-correlation coefficient increases from 0.58 (without compensation) to 0.76 (delay compensated) when this delay is accounted for by the relevant shift of temperature variations. Conclusions: The technique developed can be used in finding the connection between the lightning activity of other thunderstorm centers and the corresponding regional temperature conditions. Such an approach might be used in developing the concept of Schumann resonance records as a “global thermometer”. Key words: extremely low frequency noises, Schumann resonance, global thermometer, African center of global thunderstorm activity, Antarctic Vernadsky station Manuscript submitted 26.05.2017 Radio phys. radio astron. 2017, 22(3): 201-211 REFERENCES 1. PRICE, C. and RIND, D., 1990. The effect of global warming on lightning frequencies. In: Proceedings of the AMS 16 th Conference on Severe Storms and Atmospheric Electricity. Alberta, AB,Canada: American Meteorological Society, p. 748. 2. WILLIAMS, E. R., 1992. The Shuman resonance: A global tropical thermometer. Science . vol. 256, no. 5060, pp. 1184–1186. DOI: https://doi.org/10.1126/science.256.5060.1184 3. PRICE, C., 2000. Evidence for a link between global lightning activity and upper tropospheric water vapor. Nature . vol. 406, no. 6793, pp. 290–293. DOI: https://doi.org/10.1038/35018543 4. NICKOLAENKO, A. P., HAYAKAWA, M., SEKIGUCHI, M. and HOBARA, Y., 2008. Comparison of the variations in the intensity of global electromagnetic resonance and ground surface temperature. Radiophys. Quantum Electron . vol. 51, no. 12, pp. 931–945. DOI: https://doi.org/10.1007/s11141-009-9097-z 5. JONES, P. D., WIGLEY T. M. L. and WRIGHT, P. B., 1986. Global temperature variations between 1861 and 1984. Nature . vol. 322, no. 6078, pp. 430–434. DOI: https://doi.org/10.1038/322430a0 6. SEKIGUCHI, M., HAYAKAWA, M., NICKOLAENKO, A. P. and HOBARA, Y., 2006. Evidence of a link between the intensity of Schumann resonance and global surface temperature. Ann. Geophys . vol. 24, is 7, pp. 1809–1817. DOI: https://doi.org/10.5194/angeo-24-1809-2006 7. HOBARA, Y., HARADA, T., OHTA, K., SEKIGUCHI ,M. and HAYAKAWA, M., 2011. A study of global temperature and thunderstorm activity by using the data of Schumann resonance observed at Nakatsugawa, Japan. J. Atmos. Electr . vol. 31, no. 2, pp. 111–119. DOI: https://doi.org/10.1541/jae.31. 8. PRICE, C.and ASFUR, M., 2006. Can lightning observations be used as an indicator of upper-troposheric water-vapor variability? Bull. Am. Meteorol. Soc . vol. 87, no. 3, pp. 291–298. DOI: https://doi.org/10.1175/BAMS-87-3-291 9. PRICE, C., 2016. ELF electromagnetic waves from lightning: the Shumann resonances. Atmosphere. vol. 7, no. 9, id. 116. DOI: https://doi.org/10.3390/atmos7090116 10. LYTVYNENKO, L. N. and YAMPOLSKI, YU. M., eds., 2005. Electromagnetic manifestations of geophysical effects in Antarctica . Kharkiv: IRA NAS of Ukraine, NASCU MES of Ukraint Publ. (in Russian). 11. KOLOSKOV, A. V., BEZRODNY, V. G., BUDANOV, O. V., PAZNUKHOV, V. E. and YAMPOLSKI, Y. M., 2005. Polarization Monitoring of the Schumann Resonances in the Antarctic and Restoring of the Characteristics of the Global Thunderstorm Activity. Radio Phys. Radio Astron . vol. 10, no. 1, pp. 11–29 (in Russian). 12. BLIOKH, P. V., NICKOLAENKO A. P. and FILIPPOV, YU. F., 1977. Global electromagnetic resonances in the Earth-ionosphere cavity . Kiev: Naukova Dumka Publ. (in Russian). 13. BLIOKH, P. V., NICKOLAENKO A. P. and FILIPPOV, YU. F., 1980. Schumann resonances in the Earth-ionosphere cavity . Oxford, UK: Peter Peregrinus. 14. NICKOLAENKO, A. and HAYAKAWA, M., 2014. Schumann Resonance for Tyros. Essentials of Global Electromagnetic Resonance in the Earth–Ionosphere Cavity . Tokyo–Heidelberg–N. Y. –Dordrecht–London: Springer. 15. NICKOLAENKO, A. P., SHVETS, A. V. and HAYAKAWA, M., 2016. Extremely Low Frequency (ELF) Radio Wave Propagation: A review. Int. J. Electron. Appl. Res . vol. 3 is. 2, pp. 1–91. 16. NICKOLAENKO, A. P., SHVETS, A. V. and HAYAKAWA, M., 2016. Propagation at Extremely Low-Frequency Radio Waves. In: J. WEBSTER, ed. Wiley Encyclopedia of Electrical and Electronics Engineering . Hoboken, USA: John Wiley & Sons, Inc., pp. 1–20. DOI: https://doi.org/10.1002/047134608X.W1257.pub2

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.259
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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