Meteorology and Atmospheric Physics

, Volume 131, Issue 1, pp 1–10 | Cite as

Do the stability indices indicate the formation of deep convection?

  • K. N. UmaEmail author
  • S. K. Das
Original Paper


The present study investigates the relation between the stability indices and different types of precipitating clouds during the active and the suppressed periods of deep convection of the Madden–Julian oscillation. This is achieved by utilizing three-hourly radiosonde (RS92) data and merged cloud radar data over the Gan Island (0.69°S, 73.15°E) from October 2011 to January, 2012. The active and the suppressed periods are defined based on the rainfall. Three periods of active (15–27 October, 15–28 November and 15–27 December) and suppressed periods (01–14 November, 0–14 December and 01–14 January) are identified. During the above periods, the stability indices are calculated to distinguish the background meteorological conditions. The analysis shows that during both the active and the suppressed periods, the magnitude of the stability indices are not much different. During both the periods, the indices attain their respective threshold corresponding to the occurrence of deep convection. However, the third suppressed period shows a dry condition compared to the other two suppressed periods. The relation between the stability indices and the precipitating cloud categories (shallow, congestus and deep) indicate that even though the threshold in the stability indices were attained, deep convective clouds were not observed during the suppressed periods. The active period correlates well with the stability indices. Therefore, the stability indices do not clearly and directly determine the state of the atmosphere during deep convection. The result shows stability indices need to be substantially improved in the context of deep convection prediction.



The extensive contributions and dedication of many scientific and technical staffs from the United States, Taiwan and Japan led to the success of DYNAMO. We thank all of them for providing a unique data set over the Indian Ocean. One of the authors, SKD was supported by the UCAR-VSP at the Earth Observing Laboratory when some part of the study was initiated. We are grateful to two anonymous reviewers and the editor for their constructive comments and suggestions on this manuscript which helped to improve the quality of the manuscript.


  1. Bharadwaj N, Lindenmaier A, Widener KB, Johnson KL, Venkatesh V (2013) Ka-band ARM zenith profiling radar(KAZR) network for climate study. In: 36th Conference on radar meteorology, Breckenridge, CO, Am Meteor Soc 14A.8. Accessed 16–20 Sept 2013
  2. Ciesielski PE, Yu Johnson RH, Yoneyama K, Katsumata M, Long CN, WangJ Loehrer SM, Young K, Williams SF, Brown W, Braun J, Van Hove T (2014) Quality-controlled upper-air sounding dataset for DYNAMO/CINDY/AMIE development and corrections. J Atmos Oceanic Technol 31:741–764CrossRefGoogle Scholar
  3. Dai A (2000) Global precipitation and thunderstormfrequencies. Part II: diurnal variations. J Clim 14:1112–1118CrossRefGoogle Scholar
  4. Feng Z, McFarlane SA, Schumacher C, Ellis S, Comstock J, Bharadwaj N (2014) Constructing a merged cloud-precipitation radar dataset for tropical convective clouds during the DYNAMO/AMIE experiment at Addu Atoll. J Atmos Tech. doi: 10.1175/JTECH-D-13-00132.1 CrossRefGoogle Scholar
  5. Fliegel JM, Schumacher C (2012) Quality control and census of SMART-R observations from the DYNAMO/CINDY2011 field campaign. M.S. thesis, Department of Atmospheric Sciences, Texas A&M University, p 82Google Scholar
  6. George JJ (1960) Weather forecasting for aeronautics. Academic Press, New YorkGoogle Scholar
  7. Gettelman A, Seidel DJ, Wheeler MC, Ross RJ (2002) Multi decadal trends in tropical convective available potential energy. J Geophys Res 107(D21):4606. doi: 10.1029/2001JD001082 CrossRefGoogle Scholar
  8. Gottschalck J, Roundy PE, Schreck CJ, Vintzileos A, Zhang C (2013) Large-scale atmospheric and oceanic conditions during the 2011–2012 DYNAMO field campaign. Mon Weather Rev 141:4173–4196CrossRefGoogle Scholar
  9. Haklander A, van Delden AJ (2003) Thunderstorm predictors and their forecast skill for the Netherlands. Atmos Res 67–68:273–299CrossRefGoogle Scholar
  10. Jacovides CP, Yonetani T (1990) An evaluation of stability indices for thunderstorm prediction in greater Cyprus. Weather Forecast 5:559–569CrossRefGoogle Scholar
  11. Johnson RH, Ciesielski PE (2013) Structure and properties of Madden–Jul1ian oscillations deduced from DYNAMO sounding arrays. J Atmos Sci 70:3157–3179CrossRefGoogle Scholar
  12. Keeler RJ, Luts J, Vivekanandan J (2000) SPOLKA: NCAR’s polarimetric Doppler research radar. In: Proceedings of IEEE 2000 international geoscience and remote sensing symposium, IGARSS 2000, Honolulu, HI, IEEE, 1570–1573Google Scholar
  13. Madden RA, Julian PR (1971) Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J Atmos Sci 28:702–708. doi:10.1175/1520-0469(1971)028,0702:DOADOI.2.0.CO;2Google Scholar
  14. Miller RC (1967) Notes on analysis and severe storm forecasting procedures of the military weather warning center technical report. 200 AWS, USAFGoogle Scholar
  15. Peppler RA, Lamb PJ (1989) Tropospheric static stability and Central North American growing season rainfall. Mon Weather Rev 117:1156–1180CrossRefGoogle Scholar
  16. Ratnam MV, Durga Santhi Y, Rajeevan M, Vijaya Bhaskara Rao S (2013) Diurnal variability of stability indices observed using radiosonde observations over a tropical station: comparison with microwave radiometer measurements. Atmos Res 124:21–33CrossRefGoogle Scholar
  17. Sapra R, Dhaka SK, Panwar V, Bhatnagar R, Praveen Kumar K, Shibagaki Y, Venkat Ratnam M, Takahashi M (2011) Long-term variations in outgoing long-wave radiation (OLR), convective available potential energy (CAPE) and temperature in the tropopause region over India. J Earth Syst Sci 120(5):807–823CrossRefGoogle Scholar
  18. Showalter AK (1953) A stability index for thunderstorm forecasting. Bull Am Meteor Soc 34:250–252CrossRefGoogle Scholar
  19. Ueno K, Arya R (2007) Impact of tropical convective activity on monthly temperature variability during non monsoon season in the Nepal Himalayas. J Geophys Res 113:D18112. doi: 10.1029/2007JD0095242008 CrossRefGoogle Scholar
  20. Wang J, Zhang L, Dai A, Immler F, Sommer M, Vömel H (2013) Radiation dry bias correction of Vaisala RS92 humidity data and its impact on historical radiosonde data. J Atmos Oceanic Technol 30:197–214. doi: 10.1175/JTECH-D-12-00113.1 CrossRefGoogle Scholar
  21. Xu W, Rutledge SA (2014) Convective characteristics of the Madden–Julian oscillation over the central indian ocean observed by shipborne radar during DYNAMO. J Atmos Sci 71:2859–2877CrossRefGoogle Scholar
  22. Yoneyama K, Zhang C, Long CN (2013) Tracking pulses of the Madden–Julian oscillation. Bull Am Meteor Soc 94:1871–1891CrossRefGoogle Scholar
  23. Zhang C (2005) Madden–Julian oscillation. Rev Geophys 43:RG2003. doi: 10.1029/2004RG000158
  24. Zhang C, Gottschalck J, Maloney ED, Moncrieff MW, Vitart F, Waliser DE, Wang B, Wheeler MC (2013) Cracking the MJO nut. Geophys Res Lett 40:1223–1230. doi: 10.1002/grl.50244 CrossRefGoogle Scholar
  25. Zuluaga MD, Houze RA (2013) Evolution of the population of precipitating convective systems over the equatorial Indian Ocean in active phases of the Madden–Julian Oscillation. J Atmos Sci 70:2713–2725CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.Space Physics LaboratoryVikram Sarabhai Space CentreTrivandrumIndia
  2. 2.Indian Institute of Tropical MeteorologyPuneIndia

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