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Research article

Measurement and characterization of infrasound waves from the March 25, 2023 thunderstorm at the near equatorial

  • Thunderstorm activity on March 25, 2023 provided a unique opportunity to study the mechanism of lightning events on changes in air pressure. In particular, this event made it possible to study changes in air pressure during thunderstorms using various instruments. This paper presented comprehensive results of infrasound, satellite data, weather radar and weather measurements at the ground during the storm. Observations of lightning events were confirmed using observational data from the International Space Station's Lightning Imaging Sensor (ISS LIS). This work estimated three spectral percentile values on infrasonic sensor data, time series interpolation of standard meteorology profiles, weather radar reflectivity and total radiant energy of lightning from ISS LIS observations during the day and night periods. As a result, during the investigation, it was seen that the recorded infrasound signal in the 0.6–0.8 Hertz (Hz) range was contaminated by background environmental noise, but in the 1–3 Hz band range it was consistent with the appearance of storms that produce high energy blows. Infrasound detection and electromagnetic lightning detection show good correlation up to a distance of 100 km from the infrasonic station. During a thunderstorm, the ISS LIS flight directly above the observation site detected more than 2,000 lightning events. In addition, the application of lightning detection from several independent instruments can provide a complete picture of the observed event.

    Citation: Mario Batubara, Masa-yuki Yamamoto, Islam Hosni Hemdan Eldedsouki Hamama, Musthofa Lathif, Ibnu Fathrio. Measurement and characterization of infrasound waves from the March 25, 2023 thunderstorm at the near equatorial[J]. AIMS Geosciences, 2023, 9(4): 652-671. doi: 10.3934/geosci.2023035

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  • Thunderstorm activity on March 25, 2023 provided a unique opportunity to study the mechanism of lightning events on changes in air pressure. In particular, this event made it possible to study changes in air pressure during thunderstorms using various instruments. This paper presented comprehensive results of infrasound, satellite data, weather radar and weather measurements at the ground during the storm. Observations of lightning events were confirmed using observational data from the International Space Station's Lightning Imaging Sensor (ISS LIS). This work estimated three spectral percentile values on infrasonic sensor data, time series interpolation of standard meteorology profiles, weather radar reflectivity and total radiant energy of lightning from ISS LIS observations during the day and night periods. As a result, during the investigation, it was seen that the recorded infrasound signal in the 0.6–0.8 Hertz (Hz) range was contaminated by background environmental noise, but in the 1–3 Hz band range it was consistent with the appearance of storms that produce high energy blows. Infrasound detection and electromagnetic lightning detection show good correlation up to a distance of 100 km from the infrasonic station. During a thunderstorm, the ISS LIS flight directly above the observation site detected more than 2,000 lightning events. In addition, the application of lightning detection from several independent instruments can provide a complete picture of the observed event.



    Abbreviations: OC: Ovarian cancer; ncRNA: Non-coding RNA; lncRNAs: Long non-coding RNAs; GC: Gastric cancer; GO: Gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes

    Ovarian cancer (OC) is one of the most common gynecological malignant tumors and has been one of the leading causes of cancer-related death in western countries [1,2,3,4,5]. Of note, the incidence of OC is also increasing in China. Despite surgery and chemotherapy have been widely used in the treatment of OC, the prognosis of OC remained to be unsatisfied. Therefore, further understanding of the molecular mechanisms involved in the development of OC is still an urgent need for the diagnosis and treatment of this disease.

    Non-coding RNA (ncRNA) is a class of RNA modules which have no protein-coding function. Recently, ncRNAs had been revealed to play a crucial role in human diseases. Emerging evidence has shown that long non-coding RNAs (lncRNAs) played important roles in both physiological and pathological processes of cancers [6,7,8]. LncRNA could affect a variety of biological processes, such as autophagy, proliferation, apoptosis, differentiation, and cell cycle, in cells. For example, lncRNA Fendrr played an important role in the development of heart and body wall in the mouse, and the lack of Fendrr lead to abnormal mouse embryonic development [9]. Thomas et al. found hundreds of TNFα-induced inflammation-related lncRNAs in mice, in which Lethe was involved in the negative feedback of inflammatory signals [10]. H19 is overexpressed in gastric cancer (GC) tissues and promotes GC proliferation, migration, invasion, and metastasis [11]. Recent studies also demonstrated that lncRNA HOST2 [12], LSINCT5 [13], HOTAIR [14], NEAT1 [15] were dysregulated in OC and associated with the occurrence and progression of OC.

    LncRNA XIST is the first lncRNA reported to be involved in the regulation of X chromosome inactivation. Recent studies have shown that XIST is dysregulated in multiple cancer types, including gastric, liver, nasopharyngeal carcinoma, cervical, non-small cell lung, glioma, pancreatic, osteosarcoma, colorectal, and breast cancer. In cervical cancer, XIST competitively binds to miR-200a to promote tumor progression [16]. In non-small cell lung cancer, XIST inhibits tumor cell proliferation and EMT progression by modulating miR-137 to regulateNotch-1 signaling pathway [17]. However, the functional roles and expression pattern of XIST in OC remain unclear. The present study aimed to explore whether XIST could serve as a new diagnostic target in OC.

    The present study used TCGA (The cancer genome atlas) to analysis the XIST expression levels. The TCGA database, including a total of 305 OC samples (23 Stage Ⅱ OC samples, 244 Stage Ⅲ OC samples, and 38 Stage Ⅳ OC samples). The Kaplan-Meier plotter database was used to analyze the correlation between XIST and survival time in patients.

    The TCGA database was used to analyze XIST co-expressed genes. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis were performed on the top 1000 co-expressed genes by using the DAVID database [18,19] (https://David.ncifcrf.gov/). The interaction network between proteins was performed using the STRING (https://string-db.org/) database. The Starbase database was used to predict the regulatory cascade of XIST-miRNAs. Pearson’s correlation coefficient ≥ 0.4 is selected as a candidate, and the network was presented by Cytoscape.

    The statistical analysis was performed using SPSS version 17.0 software. T-test and Mann-Whitney U-test statistics were used to perform the statistical significance of differences between two groups; One-way ANOVA was used to perform the statistical significance of differences between multiple groups. The survival curve was performed by the Kaplan-Meier method. P < 0.05 was selected as significant.

    To explore the expression levels of XIST in OC, we first used the GTEx database to determine the expression of XIST in multiple human tissues, including ovary, cervix, uterus, testes, brains, and kidney. The results showed that the expression level of XIST in the human ovary, cervix, uterus was significantly higher than that in other tissues (Figure 1A).

    Figure 1.  LncRNA XIST was highly expressed in ovarian tissues. (A) GTEx database analysis showed that XIST expression levels in ovary, cervix, and uterus were significantly higher than other tissues of humans. (B) The GEPIA database analysis showed that the expression level of XIST in ovarian cancer was significantly lower than that in normal tissues. (C) TCGA data showed that the expression level of lncRNA XIST was significantly higher in Stage Ⅲ and Stage Ⅳ ovarian cancer than in Stage Ⅱ. (D) The TCGA database analysis showed that the overall survival time of patients with a high expression level of XIST was significantly longer than that of patients with the low expression level of XIST. P < 0.05 was statistically significant.

    Next, we analyzed the expression of XIST in OC tissues and normal ovarian tissues by using the GEPIA database, which contained RNA sequencing data of 88 normal ovarian tissues and 426 OC samples by integrating TCGA and GTEx databases. As shown in Figure 1B, our results showed that the XIST was significantly downregulated in OC compared to normal tissues (Figure 1B, p < 0.05).

    Furthermore, we analyze the correlation between its expression and stage. Totally, there are 305 OC samples that were with well-defined pathological stages in the TCGA dataset was used to conduct the analysis. The results showed that XIST was significantly downregulated in Stage Ⅲ OC compared that in Stage Ⅱ OC (Figure 1C, p < 0.05) and significantly downregulated in Stage Ⅳ OC compared that in Stage Ⅱ OC (Figure 1C, p < 0.05). However, no significant difference of XIST levels was observed between Stage Ⅲ OC and Stage Ⅳ OC samples.

    To further explore the role of XIST in OC, the present study first analyzed the relationship between XIST and survival time in OC by using the TCGA database. The results showed that higher XIST expression levels were associated with longer overall survival time (Figure 1D, p < 0.05).

    The present study also used the Kaplan-Meier plotter datasets to further explore the relationship between XIST and survival time in different types of OC. The present study found that upregulated XIST was positively correlated with longer survival time in OC (Figure 2A, p < 0.001), Serous OC (Figure 2C, p < 0.001), Grade 1 + 2 OC (Figure 2D, p < 0.01), Grade 3 OC (Figure 2E, p < 0.001), Stage Ⅲ + Ⅳ OC (Figure 2G, p < 0.001). However, the present study did not find the correlation between XIST and survival time in endometrial OC (Figure 2B) and Stage Ⅰ + Ⅱ OC (Figure 2F).

    Figure 2.  High expression of XIST was positively correlated with the long survival time of patients with ovarian cancer. Kaplan-Meier plotter data results displayed positive relationship between high XIST expression and long survival time of patients with ovarian cancer (A, p < 0.001), serous ovarian cancer (C, p < 0.001), Grade 1 + 2 ovarian cancer (D, p < 0.01), Grade 3 ovarian cancer (E, p < 0.001), Stage Ⅲ + Ⅳ ovarian cancer (G, p < 0.001). However, no significant difference of XIST levels was observed between Stage Ⅲ OC and Stage Ⅳ OC samples (F). P < 0.05 was statistically significant.

    The present study first analyzed the co-expressed gene of XIST by using TCGA database, and the most significant 1000 co-expressed genes were selected. Gene Ontology analysis (GO analysis) was further used to predict the potential roles of XIST in OC. The results revealed that XIST is involved in a series of biological processes including transcription, protein phosphorylation, transport, protein ubiquitination, DNA repair, ciliary morphogenesis, RNA splicing, cilia assembly, mRNA processing, and cell cycle regulation (Figure 3A). Molecular Function analysis showed that XIST could participate in many functions, including DNA binding, ATP binding, nucleic acid binding, poly (A) RNA binding, transcription factor activity, RNA binding, protein serine/threonine kinase activity, nucleoside Acid binding, protein kinase activity (Figure 3B). KEGG analysis showed that XIST affected many signaling pathways, including taste, mRNA monitoring, Fanconi anemia, small cell lung cancer, ECM-receptor interaction, NF-κB, protein digestion and absorption, transcriptional dysregulation in cancer, lysine degradation, amebic disease (Figure 3C).

    Figure 3.  Bioinformatics analysis revealed the function and role of lncRNA XIST in ovarian cancer. Biological process analysis of XIST (A), molecular function (Molecular Function) analysis (B), KEGG signaling pathway analysis (C). P < 0.05 was statistically significant.

    The present study constructed XIST‑mediated PPI networks by using the top 1000 co-expressing genes of XIST via the STRING database. The results identified three core networks. Among them, Module 1 included 12 core proteins (P2RY4, DRD2, TAS2R4, TAS2R5, TAS2R10, AS2R3, NPBWR2, GRM6, TAS2R39, OXER1, GABBR1, TAS2R50, Figure 4A), Module 2 included 11 core proteins (KBTBD10, KLHL3, HERC2, WSB1, ASB14, FBXL8, ANAPC4, ASB16, RLIM, ASB6, HERC1, Figure 4B) and Module 3 included 9 core proteins (CCDC41, CEP152, CEP164, FBF1, OFD1, CEP290, AHI1) TTBK2, AKAP9, Figure 4C).

    Figure 4.  Protein-protein interaction network mediated by XIST. (A) Hub PPI network 1 of XIST co-expressing genes. Blue nodes represent XIST co-expressing genes. This network included 12 core proteins (P2RY4, DRD2, TAS2R4, TAS2R5, TAS2R10, AS2R3, NPBWR2, GRM6, TAS2R39, OXER1, GABBR1, TAS2R50). (B) Hub PPI network 2 of XIST co-expressing genes. Blue nodes represent XIST co-expressing genes. This network included 11 core proteins (KBTBD10, KLHL3, HERC2, WSB1, ASB14, FBXL8, ANAPC4, ASB16, RLIM, ASB6, HERC1). (C) Hub PPI network 3 of XIST co-expressing genes. Blue nodes represent XIST co-expressing genes. This network included 9 core proteins (CCDC41, CEP152, CEP164, FBF1, OFD1, CEP290, AHI1 TTBK2, and AKAP9). P < 0.05 was statistically significant.

    Recent studies have found that XIST could participate in the regulation of tumor progression as a natural miRNA sponge. However, the mechanisms of XIST in OC remained unclear. Therefore, the present study systematically and comprehensively constructed an XIST-mediated endogenous competitive RNA interaction network. This study predicted the XIST-miRNAs regulatory cascade by using the Starbase database and then used the same method to predict the XIST-miRNAs-mRNAs regulatory cascade. Only XIST-mRNAs with positive correlation expression were selected for the construction of ceRNAs networks based on co-expression analysis results.

    The network included 202 miRNAs and 462 mRNAs as shown in Figure 5. Ten of these miRNAs were identified as the core regulator of the network, including hsa-miR-590-3p, hsa-miR-340-5p, hsa-miR-181b-5p, hsa-miR-424-5p, hsa-miR-15b-5p, hsa-miR-495-3p, hsa-miR-15a-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-30c-5p, hsa-miR-497-5p, hsa-miR-16- 5p, hsa-miR-30e-5p, hsa-miR-181c-5p, involved in the regulation of up to 100 different mRNAs. The present study also found that DDX3X, CHD9, POU2F1, YOD1, MAT2A, BRWD1, INO80D, GPCPD1, and MDM4 were regulated by more than 100 different miRNAs, suggesting that they acted as key XIST regulatory genes.

    Figure 5.  ceRNA network mediated by XIST. Red nodes represent the XIST. Blue nodes represent the predictive miRNAs of XIST. Green nodes represent the predictive mRNAs of XIST. P < 0.05 was statistically significant.

    Emerging studies demonstrated lncRNAs play important roles in regulating cell proliferation, apoptosis, and differentiation. For example, lncRNA loc285194 played as a tumor suppressor function by competitively binding to miR-211 in colorectal cancer [20]. UCA1 is overexpressed in liver cancer and sponged miR-216b to inhibit tumor growth [21]. MALAT1 could cause microvascular dysfunction in diabetic patients [22]. Exploring the roles of lncRNAs in human diseases could provide novel biomarkers for prognosis and treatment.

    OC is one of the most common gynecological malignant tumors. Previous studies had reported that lncRNA played an important role in the development and progression of OC. A series of lncRNAs are abnormally expressed in OC, such as GAS5 [23], TP73-AS1 [24], CCAT1, and MALAT1 [25]. LncRNA could regulate a series of OC related biological processes such as proliferation, cycle, and apoptosis. For instance, Lnc-OC1 promotes proliferation and migration of OC cells by competitively binding to miR-34a and miR-34c [26]. LncRNA CTD-2020K17.1 has been reported as an oncogene to promote OC metastasis, invasion, and proliferation [27]. However, it should be noted that the functions of most lncRNAs in OC are still unclear. Several previous studies indicated XIST might be associated with the development of OC. However, there is still a lack of systematic analysis to support XIST as a target for clinical diagnosis in OC. Then in the silico analysis based on multiple platforms in this study showed that the expression level of XIST was significantly increased in normal ovary, uterus, and cervix tissues, while the expression level was significantly decreased in OC tissues.

    Further analysis showed that XIST expression levels in advanced-grade OC were significantly lower than in low-grade OC. Moreover, the overexpression of XIST was positively correlated with the longer survival time of patients with OC, Serous OC, Grade 1 + 2 OC, Grade 3 OC, and Stage Ⅲ + Ⅳ OC. These analyses showed that XIST could serve as a potential diagnostic marker for OC.

    Emerging studies have shown that in addition to routine functional assays in vitro and in vivo, the widespread use of bioinformatics tools such as protein-protein interaction networks and endogenous competitive RNA networks plays an important role in uncovering the underlying pathogenesis of the human disease. To explore the potential functions and mechanisms of XIST in OC, the present study first performed co-expression analysis and bioinformatic analysis and identified three core interaction networks included 12 core proteins, 11 core proteins, and 9 core proteins respectively. The results revealed that XIST was involved in the biological processes of transcription, protein phosphorylation, transport, protein ubiquitination, DNA repair, ciliary morphogenesis, RNA splicing, DNA binding, ATP binding. KEGG signaling pathway analysis showed that XIST affected many signaling pathways, including taste, mRNA monitoring, Fanconi anemia, small cell lung cancer, ECM-receptor interaction, NF-κB, protein digestion and absorption, transcriptional dysregulation in cancer, lysine degradation, and amebic disease.

    Previous studies show XIST plays an important regulatory role in the development of multiple tumors, including gastric cancer, hepatocellular carcinoma, and breast cancer. XIST, as one of the longest lncRNAs in mammals, previous reports have shown that XIST plays a wide range of endogenous competitive RNA in cells, affecting multiple miRNA activity included miR-137, miR-200c, miR-155, miR-320 and miR-34a [28,29,30]. For example, XIST can inhibit the proliferation and metastasis of breast cancer cells by affecting the miR-155/CDX1 cascade control axis. The present study first systematically constructed OC-specific XIST-mediated endogenous competitive RNA network. Several studies in OC also demonstrated that XIST had a regulatory role in cancer progression. However, these reports are often contradictory. For example, Zuo et al. found XIST promoted malignant behavior of epithelial OC, while Wang et al. demonstrated that XIST has anticancer effects on epithelial OC cells through inverse downregulation of hsa-miR-214-3p. Of note, with the development of bioinformatics analysis, emerging studies revealed that bioinformatics analysis could provide more clues to understand the potential roles of genes in human diseases. The network included 202 miRNAs and 462 mRNAs, of which 10 miRNAs were identified as the core regulatory factors of this network, including hsa-miR-590-3p, hsa-miR-340-5p, hsa-miR-181b-5p, which regulated more than 100 different mRNAs in the network. These miRNAs played an important regulatory role in OC. For example, miR-590-3p could promote the proliferation and metastasis of OC by regulating FOXA2. miR-424 could inhibit the metastasis, invasion, and epithelial-mesenchymal transition of OC.

    In this study, there also exist some limitations. Firstly, the expression levels of XIST in OC and normal tissues should be detected to validate the bioinformatics analysis in future study. Secondly, the specific functions of XIST had not been further excavated in this study. Further experimental validation would be required for future verification.

    In summary, the present study showed lncRNA XIST was significantly down-regulated in OC and was associated with the development of OC. Overexpression of XIST was significantly associated with longer survival time in patients with OC. Also, our analysis also showed that lncRNA XIST was closely related to biological processes such as transcription, protein phosphorylation, transport, protein ubiquitination, and DNA repair, which provided a theoretical basis for the diagnosis and treatment of OC.

    All authors declare no conflicts of interest in this paper



    [1] Lyons JJ, Haney MM, Fee D, et al. (2019) Infrasound from giant bubbles during explosive submarine eruptions. Nat Geosci 12: 952–958. https://doi.org/10.1038/s41561-019-0461-0 doi: 10.1038/s41561-019-0461-0
    [2] Johnson JB, Ripepe M (2011) Volcano infrasound: A review. J Volcanol Geotherm Res 206: 61–69. https://doi.org/10.1016/j.jvolgeores.2011.06.006 doi: 10.1016/j.jvolgeores.2011.06.006
    [3] Fee D, Matoza RS (2013) An overview of volcano infrasound: From hawaiian to plinian, local to global. J Volcanol Geotherm Res 249: 123–139. https://doi.org/10.1016/j.jvolgeores.2012.09.002 doi: 10.1016/j.jvolgeores.2012.09.002
    [4] Matoza RS, Hedlin MAH, Garces MA (2007) An infrasound array study of Mount St. Helens. J Volcanol Geotherm Res 160: 249–262. https://doi.org/10.1016/j.jvolgeores.2006.10.006 doi: 10.1016/j.jvolgeores.2006.10.006
    [5] Matoza RS, Fee D, Garcés MA, et al. (2009) Infrasonic jet noise from volcanic eruptions. Geophys Res Lett 36: L08303. https://doi.org/10.1029/2008GL036486 doi: 10.1029/2008GL036486
    [6] Cook RK (1971) Infrasound Radiated During the Montana Earthquake of 1959 August 18. Geophys J R Astron Soc 26: 191–198. https://doi.org/10.1111/j.1365-246X.1971.tb03393.x doi: 10.1111/j.1365-246X.1971.tb03393.x
    [7] Mutschlecner JP, Whitaker RW (2005) Infrasound from earthquakes. J Geophys Res 110: D01108. https://doi.org/10.1029/2004JD005067 doi: 10.1029/2004JD005067
    [8] Arrowsmith SJ, Whitaker R, Taylor SR, et al. (2008) Regional monitoring of infrasound events using multiple arrays: Application to Utah and Washington State. Geophys J Int 175: 291–300. https://doi.org/10.1111/j.1365-246X.2008.03912.x doi: 10.1111/j.1365-246X.2008.03912.x
    [9] Johnson JB, Mikesell TD, Anderson JF, et al. (2020) Mapping the sources of proximal earthquake infrasound. Geophys Res Lett 47. https://doi.org/10.1029/2020GL091421
    [10] Arrowsmith SJ, Burlacu R, Pankow K, et al. (2012) A seismoacoustic study of the 2011 January 3 Circleville earthquake: Seismoacoustic study: Circleville earthquake. Geophys J Int 189: 1148–1158. https://doi.org/10.1111/j.1365-246X.2012.05420.x doi: 10.1111/j.1365-246X.2012.05420.x
    [11] Le Pichon A, Herry P, Mialle P, et al. (2005) Infrasound associated with 2004–2005 large Sumatra earthquakes and tsunami. Geophys Res Lett 32: L19802. https://doi.org/10.1029/2005GL023893 doi: 10.1029/2005GL023893
    [12] Lin TL, Langston CA (2007) Infrasound from thunder: A natural seismic source. Geophys Res Lett 34: L14304. https://doi.org/10.1029/2007GL030404 doi: 10.1029/2007GL030404
    [13] Chimonas G (1977) A possible source mechanism for mountain-associated infrasound. J Atmos Sci 34: 806–811. https://doi.org/10.1175/1520-0469(1977)034%3C0806:APSMFM%3E2.0.CO;2 doi: 10.1175/1520-0469(1977)034%3C0806:APSMFM%3E2.0.CO;2
    [14] Hupe P (2018) Global infrasound observations and their relation to atmospheric tides and mountain waves. Ph.D. thesis, Ludwig-Maximilians-Universität München, Germany. Available from: https://edoc.ub.uni-muenchen.de/23790/
    [15] Le Pichon A, Ceranna L, Pilger C, et al. (2013) The 2013 Russian fireball large ever detected by CTBTO infrasound sensors. Geophys Res Lett 40: 3732–3737. https://doi.org/10.1002/grl.50619 doi: 10.1002/grl.50619
    [16] Landes M, Ceranna L, Le Pichon A, et al. (2012) Localization of microbarom sources using the IMS infrasound network. J Geophys Res 117: D06102. https://doi.org/10.1029/2011JD016684 doi: 10.1029/2011JD016684
    [17] Arrowsmith SJ, Hedlin, MAH, Stump B, et al. (2008) Infrasonic Signals from Large Mining Explosions. Bull Seismol Soc Amer 98. https://doi.org/10.1785/0120060241
    [18] Bowman DC, Krishnamoorthy S (2021) Infrasound from a buried chemical explosion recorded on a balloon in the lower stratosphere. Geophys Res Lett 48: e2021GL094861. https://doi.org/10.1029/2021GL094861 doi: 10.1029/2021GL094861
    [19] Il-Young Che, Park J, Kim I, et al. (2014) Infrasound signals from the underground nuclear explosions of North Korea. Geophys J Int 198: 495–503. https://doi.org/10.1093/gji/ggu150 doi: 10.1093/gji/ggu150
    [20] Applbaum, D, Averbuch G, Price C, et al. (2019) Infrasound observations of sprites associated with winter thunderstorms in the eastern mediterranean. Atmos Res 235: 104770. https://doi.org/10.1016/j.atmosres.2019.104770 doi: 10.1016/j.atmosres.2019.104770
    [21] Campus P, Christie DR (2010) Worldwide Observations of Infrasonic Waves, In: Le Pichon A, Blanc E, Hauchercorne A. (eds), Infrasound Monitoring for Atmospheric studies, Springer Netherlands, 185–234. https://doi.org/10.1007/978-1-4020-9508-5_6
    [22] Christie DR, Campus P (2010) The IMS Infrasound Network: Design and Establishment of Infrasound Stations, In: Le Pichon A, Blanc E, Hauchercorne A, (eds), Infrasound Monitoring for Atmospheric studies, Springer Netherlands. 29–75. https://doi.org/10.1007/978-1-4020-9508-5_2
    [23] Batubara M, Yamamoto MY (2020) Infrasound Observations of Atmospheric Disturbances Due to a Sequence of Explosive Eruptions at Mt. Shinmoedake in Japan on March 2018. Remote Sens 12: 728. https://doi.org/10.3390/rs12040728
    [24] Houghton HG (1951) On the Physics of Clouds and Precipitation. In: Malone TF, (eds), Compendium of Meteorology. American Meteorological Society, Boston, MA. https://doi.org/10.1007/978-1-940033-70-9_14
    [25] Gaskell W (1981) A laboratory study of the inductive theory of thunderstorm electrification. Quart J Roy Meteor Soc 107: 955–966. https://doi.org/10.1002/qj.49710745413 doi: 10.1002/qj.49710745413
    [26] Gilmore MS, Straka JM, Rasmussen EN (2004) Precipitation and evolution sensitivity in simulated deep convective storms: Comparisons between liquid-only and simple ice and liquid phase microphysics. Mon Weather Rev 132: 1897–1916. https://doi.org/10.1175/1520-0493(2004)132<1897:PAESIS>2.0.CO;2 doi: 10.1175/1520-0493(2004)132<1897:PAESIS>2.0.CO;2
    [27] Jayaratne ER, Saunders CPR (1985) Thunderstorm electrification: The effect of cloud droplets. J Geophys Res 90: 13063–13066. https://doi.org/10.1029/JD090iD07p13063 doi: 10.1029/JD090iD07p13063
    [28] Lang TJ, Jay Miller L, Weisman M, et al. (2004) The Severe Thunderstorm Electrification and Precipitation Study. Bull Amer Meteor Soc 85: 1107–1126. https://doi.org/10.1175/BAMS-85-8-1107 doi: 10.1175/BAMS-85-8-1107
    [29] Pereyra RG, Avila EE, Castellano NE, et al. (2000) A laboratory study of graupel charging. J Geophys Res 105: 20803–20812. https://doi.org/10.1029/2000JD900244 doi: 10.1029/2000JD900244
    [30] Qie X, Yuan S, Chen Z, et al. (2021) Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region. Sci China Earth Sci 64: 10–26. https://doi.org/10.1007/s11430-020-9656-8 doi: 10.1007/s11430-020-9656-8
    [31] Reynolds SE, Brook M, Gourley MF (1957) Thunderstorm charge separation. J Atmos Sci 14: 426–436. https://doi.org/10.1175/1520-0469(1957)014<0426:TCS>2.0.CO;2 doi: 10.1175/1520-0469(1957)014<0426:TCS>2.0.CO;2
    [32] Saunders CPR (1993) A review of thunderstorm electrification processes. J Appl Meteor Climatol 32: 642–655. https://doi.org/10.1175/1520-0450(1993)032<0642:AROTEP>2.0.CO;2 doi: 10.1175/1520-0450(1993)032<0642:AROTEP>2.0.CO;2
    [33] Takahashi T (1978) Riming electrification as a charge generation mechanism in thunderstorms. J Atmos Sci 35: 1536–1548. https://doi.org/10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2 doi: 10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2
    [34] Wang X, Liao R, Li J, et al. (2020) Thunderstorm identification algorithm research based on simulated airborne weather radar reflectivity data. J Wireless Com Network 37. https://doi.org/10.1186/s13638-020-1651-6
    [35] Petersen WA, Rutledge SA (1998) On the relationship between cloud-to-ground lightning and convective rainfall. J Geophys Res 103: 14025–14040. https://doi.org/10.1029/97JD02064 doi: 10.1029/97JD02064
    [36] Price C, Federmesser B (2006) Lightning-rainfall relationships in Mediterranean winter thunderstorms. Geophys Res Lett 33: L07813. https://doi.org/10.1029/2005GL024794 doi: 10.1029/2005GL024794
    [37] Blakeslee R, Koshak W (2016) LIS on ISS: Expanded Global Coverage and Enhanced Applications. Earth Obs 28: 4–14.
    [38] Blakeslee RJ, Lang TJ, Koshak WJ, et al. (2020) Three Years of the Lightning Imaging Sensor Onboard the International Space Station: Expanded Global Coverage and Enhanced Applications. J Geophys Res Atmos 125: e2020JD032918. https://doi.org/10.1029/2020JD032918 doi: 10.1029/2020JD032918
    [39] Baldini L, Roberto N, Montopoli M, et al. (2018) Ground-Based Weather Radar to Investigate Thunderstorms. In: Andronache C, (eds), Remote Sensing of Clouds and Precipitation. Springer Remote Sensing/Photogrammetry. Springer, Cham. https://doi.org/10.1007/978-3-319-72583-3_4
    [40] Hwang SH, Kim KB, Han D (2020) Comparison of methods to estimate areal means of short duration rainfalls in small catchments, using rain gauge and radar data. J Hydrol 588: 125084. https://doi.org/10.1016/j.jhydrol.2020.125084 doi: 10.1016/j.jhydrol.2020.125084
    [41] Prajapati R, Silwal P, Duwal S, et al. (2022) Detectability of rainfall characteristics over a mountain river basin in the Himalayan region from 2000 to 2015 using ground-and satellite-based products. Theor Appl Climatol 147: 185–204. https://doi.org/10.1007/s00704-021-03820-9 doi: 10.1007/s00704-021-03820-9
    [42] Wu F, Cui X, Zhang DL, et al. (2017) The relationship of lightning activity and short-duration rainfall events during warm seasons over the Beijing metropolitan region. Atmos Res 195: 31–43. https://doi.org/10.1016/j.atmosres.2017.04.032 doi: 10.1016/j.atmosres.2017.04.032
    [43] Williams E, Mkrtchyan H, Mailyan B, et al. (2022) Radar diagnosis of the thundercloud electron accelerator. J Geophys Res Atmos 127: e2021JD035957. https://doi.org/10.1029/2021JD035957 doi: 10.1029/2021JD035957
    [44] Uman MA (2001) The lightning discharges, Courier Corporation.
    [45] Rakov VA, Uman MA (2003) Lightning, Physics and Effects, Cambridge University Press, New York. 687. https://doi.org/10.1017/CBO9781107340886
    [46] Few AA, Teer TL (1974) The accuracy of acoustic reconstructions of lightning channels. J Geophys Res 79: 5007–5011. https://doi.org/10.1029/JC079i033p05007 doi: 10.1029/JC079i033p05007
    [47] MacGorman DR, Few AA, Teer TL (1981) Layered lightning activity. J Geophys Res 86: 9900–9910. https://doi.org/10.1029/JC086iC10p09900 doi: 10.1029/JC086iC10p09900
    [48] Hagenguth JH (1951) The Lightning Discharge. In: Malone TF, Eds., Compendium of Meteorology. American Meteorological Society, Boston, MA. https://doi.org/10.1007/978-1-940033-70-9_11
    [49] Dessler AJ (1973) Infrasonic thunder. J Geophys Res 78: 1889–1896. https://doi.org/10.1029/JC078i012p01889 doi: 10.1029/JC078i012p01889
    [50] Pasko VP (2009) Mechanism of lightning associated infrasonic pulses from thunderclouds. J Geophys Res 114: D08205. https://doi.org/10.1029/2008JD011145 doi: 10.1029/2008JD011145
    [51] Liszka L (2004) On the possible infrasound generation by sprites. J Low Freq Noise V A 23: 85–93. https://doi.org/10.1260/0263092042869838 doi: 10.1260/0263092042869838
    [52] Farges T, Blanc E, le Pichon A, et al. (2005) Identification of infrasound produced by sprites during the Sprite2003 campaign. Geophys Res Lett 32: L01813. https://doi.org/10.1029/2004GL021212 doi: 10.1029/2004GL021212
    [53] Liszka L, Hobara Y (2006) Sprite-attributed infrasonic chirps—Their detection, occurrence and properties between 1994 and 2004. J Atmos Solar-Terr Phys 68: 1179–1188. https://doi.org/10.1016/j.jastp.2006.02.016 doi: 10.1016/j.jastp.2006.02.016
    [54] Blanc E (1985) Observations in the upper atmosphere of infrasonic waves from natural or artificial sources: A summary. Ann Geophys 3: 673–688.
    [55] Few AA (1995) Acoustic radiations from lightning. Handbook of Atmospheric Electrodynamics, CRC Press, Boca Raton, 1–31. https://doi.org/10.1201/9780203713297
    [56] Drob DP, Picone JM, Garces M (2003) Global morphology of infrasound propagation. J Geophys Res 108: 4680. https://doi.org/10.1029/2002JD003307 doi: 10.1029/2002JD003307
    [57] Nugroho GA, Sinatra T, Fathrio I (2018) Application of rain scanner SANTANU and transportable weather radar in analyze of Mesoscale Convective System (MCS) events over Bandung, West Java. IOP Conf Ser Earth Environ Sci 149: 012058. https://iopscience.iop.org/article/10.1088/1755-1315/149/1/012058
    [58] Zel'dovich YB, Raizer YP (2002) Physics of shock waves and high-temperature hydrodynamic phenomena, Academic Press, New York & London.
    [59] Few AA (1982) Acoustic radiations from lightning. Handbook of Atmospherics, CRC Press, Inc., Boca Raton, 257–289.
    [60] Few AA (1985) The production of lightning-associated infrason ic acoustic sources in thunderclouds. J Geophys Res 90: 6175–6180. https://doi.org/10.1029/JD090ID04P06175 doi: 10.1029/JD090ID04P06175
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