Skip to main content

Advertisement

Log in

Long-term analysis of thunderstorm-related parameters over Visakhapatnam and Machilipatnam, India

  • Research Article - Atmospheric & Space Sciences
  • Published:
Acta Geophysica Aims and scope Submit manuscript

Abstract

In this present work, an attempt has been made to analyze various thunderstorm-related parameters and their influence over the two stations Visakhapatnam (VSK) and Machilipatnam (MTM). The thunderstorm-related parameters used in the present study are convective available potential energy (CAPE), lifted index, K-index, total totals index (TTI), humidity index, convective inhibition, thunderstorm prediction index (TPI), deep convective index (DCI) and updraft vertical velocity. This analysis was carried out using NCEP NCAR reanalysis monthly data for the time period from 1948 to 2012. These parameters have given good guidance for studying the thunderstorm event. We also analyzed IMD thunderstorm occurrence days reported at two stations, i.e., VSK and MTM with NCEP NCAR (daily data) calculated CAPE, TTI, TPI and DCI parameter threshold days in pre-monsoon season for every year during the time period 2010 to 2019. Out of those four parameters, TTI has shown good correlation with the IMD recorded days. So we have attempted the prediction of thunderstorms using artificial neural network (ANN) and auto-regressive moving average (ARMA) techniques for TTI parameter. While using these techniques, we have experimented in three training sets, i.e., 90%, 80% and 70%. Another attempt has been made to assess the skill of ARMA and ANN techniques in forecasting the occurrence of thunderstorm activity at VSK and MTM stations. The present study suggests that ANN has high skill than ARMA. From this study, we can understand that VSK has more chances for thunderstorms than MTM.

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. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Ali AF, Johari D, Ismail NF, Musirin I, Hashim N (2011) Thunderstorm forecasting by using artificial neural network. In: Proceeding of the 5th international power engineering and optimization conference, Selangor, Malaysia, pp 369–374

  • Bhardwaj P, Singh O, Kumar D (2017) Spatial and temporal variations in thunderstorm casualties over India. J Trop Geogr 38:293–312

    Article  Google Scholar 

  • Box GE, Jenkins GM (1976) Time series analysis: forecasting and control, 2nd edn. Holden-Day, San Francisco

    Google Scholar 

  • Chaudhuri S, Middey A (2012) A composite stability index for dichotomous forecast of thunderstorms. Theor Appl Climatol 110:457–469

    Article  Google Scholar 

  • Chaudhuri S, Middey A (2013) Nowcasting lightning flash rate and peak wind gust associated with severe thunderstorms using remotely sensed TRMM-LIS data. Int J Remote Sens 34(5):1576–1590

    Article  Google Scholar 

  • De Coning E, Gijben M, Maseko B, Vanhemert L (2015) Using satellite data to identify and track intense thunderstorms in south and southern Africa. S Afr J Sci 111(7–8):1–5

    Google Scholar 

  • Galway GJ (1956) The lifted index as a predictor of latent instability. Bull Am Meteor Soc 37(10):528–529

    Article  Google Scholar 

  • George GJ (1960) Weather forecasting for aeronautics. Academic Press, London, p 673

    Google Scholar 

  • Goyal S, Kumar A, Mohapatra M, Rathore LS, Dube SK, Saxena R, Giri RK (2017) Satellite-based technique for nowcasting of thun-derstorms over Indian region. J Earth Syst Sci 126(6):1–13

    Article  Google Scholar 

  • Haklander AJ, Delden AV (2003) Thunderstorm predictors and their forecast skill for The Netherlands. Atmos Res 67–68:273–299

    Article  Google Scholar 

  • Jacovides CP, Yonetani T (1990) An Evaluation of stability indices for thunderstorm prediction in greater cyprus. Wether Forecast 5(4):559–569

    Article  Google Scholar 

  • Jayakrishnan RP, Babu AC (2014) Assessment of convective activity using stability indices as inferred from radiosonde and MODIS data. Atmos Clim Sci 4:122–130

    Google Scholar 

  • Jorgensen DP, LeMone MA (1989) Vertical velocity characteristics of oceanic convection. J Atmos Sci 46(5):621–640

    Article  Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–472

    Article  Google Scholar 

  • Kalsi SR (2002) Satellite based weather forecasting, satellite remote sensing and GIS applications in agricultural meteorology, pp 331–346

  • Kandalgaonkar SS, Tinmaker RIM, Kulkarni RJ, Nath A (2003) Diurnal variation of lightning activity over the Indian region. Geo Res Lett 30(20):2022

    Article  Google Scholar 

  • Kessler E (1982) Thunderstorm morphology and dynamics. National Oceanic and Atmospheric Administration, United States Department of Commerce, Environmental Research Laboratories, p 603

  • Khan JA, Arsalan MH (2007) General climatology. University of Karachi

  • Kong Y, Chai H, Li J, Pan Z, Chong Y (2017) A modified forecast method of ionosphere VTEC series based on ARMA model. In: IEEE in 2017 forum on cooperative positioning and service (CPGPS), pp 90–95

  • Kunz M (2007) The skill of convective parameters and indices to predict isolated and severe thunderstorms. Nat Hazards Earth Syst Sci 7:327–342

    Article  Google Scholar 

  • Manzato A (2005) The use of sounding-derived indices for a neural network short-term thunderstorm forecast. Weather Forecast 20(6):896–917

    Article  Google Scholar 

  • Miller RC (1967) Notes on analysis and severe storm forecasting procedures of the Military Weather Warning Center. Tech. Report 200, AWS, USAF

  • Mukhopadhyay P, Sanjay J, Singh SS (2003) Objective forecast of thundery/non thundery days using conventional indices over three northeast Indian stations. Mausam 54(4):867–880

    Google Scholar 

  • Parker DJ (2002) The response of CAPE and CIN to tropospheric thermal variations. Q J R Meteorol Soc: J Atmos Sci Appl Meteorol Phys Oceanogr 128(579):119–130

    Article  Google Scholar 

  • Purdom JFW (2003) Local severe storms monitoring and prediction using satellite systems. Mausam 54(1):141–154

    Google Scholar 

  • Ratnam DV, Otsuka Y, Sivavaraprasad G, Dabbakuti JK (2019) Development of multivariate ionospheric TEC forecasting algorithm using linear time series model and ARMA over low-latitude GNSS station. Adv Space Res 63(9):2848–2856

  • Ravi N, Mohanty CU, Madan PO, Paliwal KR (1999) Forecastin.g of thunderstorms in the Pre-Monsoon Season at Delhi. Meteorol Appl 6:29–38

    Article  Google Scholar 

  • Ray K et al (2014) PreMonsoon Thunderstorms 2014: A Report, IMD Report No. ESSO/IMD/SMRC STORM PROJECT-2014/01(2014)/03

  • Saha U, Maitra A, Midya SK, Das GK (2014) Association of thunderstorm frequency with rainfall oc-currences over an Indian urban metropolis. Atmos Res 138:240–252

    Article  Google Scholar 

  • Srinivasan V, Ramamurthy K, Nene YR (1973) Summer nor’wester and Andhi and large scale convective activity over peninsula and central parts of the country. India Meteorological Department Forecasting Manual Part 3, p 137

  • Tyagi B, Krishna VN, Satyanarayana ANV (2011) Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008. Nat Hazards 56(3):681–698

    Article  Google Scholar 

  • Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press, London

    Google Scholar 

Download references

Acknowledgements

The data used in this work were supported by NCEP-NCAR reanalysis data from NOAA, USA. This work is supported by CSIR-SRF, Govt. of India under the File No. 09/1068(0001)/2018-EMR-I.

Funding

There are no funding sources for this article work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Umakanth.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Umakanth, N., Satyanarayana, G.C., Simon, B. et al. Long-term analysis of thunderstorm-related parameters over Visakhapatnam and Machilipatnam, India. Acta Geophys. 68, 921–932 (2020). https://doi.org/10.1007/s11600-020-00431-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11600-020-00431-2

Keywords

Navigation