Climate Dynamics

, Volume 42, Issue 9–10, pp 2801–2815 | Cite as

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

  • S. Abhilash
  • A. K. SahaiEmail author
  • N. Borah
  • R. Chattopadhyay
  • S. Joseph
  • S. Sharmila
  • S. De
  • B. N. Goswami
  • Arun Kumar
Part of the following topical collections:
  1. Topical Collection on Climate Forecast System Version 2 (CFSv2)


An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001–2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.


Indian summer monsoon Extended range prediction Ensemble prediction system 



IITM is fully supported by the Ministry of Earth Sciences, Government of India, New Delhi. India. We thank NCEP for reanalysis datasets and for transferring the CFS system under the MoU between MoES and NOAA. We also thank IMD for daily rainfall data. S.S. thanks the Council of Scientific and Industrial Research, New Delhi, for a research fellowship.


  1. Abhilash S, Sahai AK, Pattnaik S, Goswami BN, Kumar A (2013) Extended range prediction of active-break spells of Indian summer monsoon rainfall using an ensemble prediction system in NCEP climate forecast system. Int J Climatol. doi: 10.1002/joc.3668 Google Scholar
  2. Barnston AG (1992) Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score. Weather Forecast 7(4):699–709CrossRefGoogle Scholar
  3. Borah N, Sahai AK, Chattopadhyay R, Joseph S, Abhilash S, Goswami BN (2013) A self-organizing map-based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon. J Geophys Res Atmos 118. doi: 10.1002/jgrd.50688
  4. Buizza R, Tribbia J, Molteni F, Palmer T (1993) Computation of optimal unstable structures for a numerical weather prediction model. Tellus 45A:388–407CrossRefGoogle Scholar
  5. Buizza R, Leutbecher M, Isaksen L (2008) Potential use of an ensemble of analyses in the ECMWF ensemble prediction system. Q J R Meteorol Soc 134:2051–2066CrossRefGoogle Scholar
  6. Chattopadhyay R, Sahai AK, Goswami BN (2008) Objective identification of on linear convectively coupled phases of monsoon intraseasonal oscillation: implications for prediction. J Atmos Sci 65:1549–1569CrossRefGoogle Scholar
  7. Du J, Mullen SL, Sanders F (1997) Short-range ensemble forecasting of quantitative precipitation. Mon Weather Rev 125:2427–2459CrossRefGoogle Scholar
  8. Evensen G (1994) Sequential data assimilation with a nonlinear quasigeostrophic model using Monte-Carlo methods to forecast error statistics. J Geophys Res Ocean 99(C5):10143–10162. doi: 10.1029/94JC00572 CrossRefGoogle Scholar
  9. Fu X, Wang B, Li T, McCreary J (2003) Coupling between northward propagating intraseasonal oscillations and sea-surface temperature in the Indian Ocean. J Atmos Sci 60(15):1733–1753CrossRefGoogle Scholar
  10. Fu X, Wang B, Waliser DE, Tao L (2007) Impact of atmosphere–ocean coupling on the predictability of monsoon intraseasonal oscillations. J Atmos Sci 64:157–174CrossRefGoogle Scholar
  11. Fu X, Yang B, Bao Q, Wang B (2008) Sea surface temperature feedback extends the predictability of tropical intraseasonal oscillation. Mon Weather Rev 136:577–597CrossRefGoogle Scholar
  12. Fu X, Wang B, Lee YJ, Wang WQ, Gao L (2011) Sensitivity of dynamical intraseasonal prediction skills to different initial conditions. Mon Weather Rev 139:2572–2592CrossRefGoogle Scholar
  13. Fu X, Lee YJ, Wang B, Wang WQ, Vitart F (2013) Intraseasonal forecasting of Asian summer monsoon in four operational and research models. J Clim 26:4186–4203. doi: 10.1175/JCLI-D-12-00252.1 CrossRefGoogle Scholar
  14. Gerrity JP (1992) A note on Gandin and Murphy’s equitable skill score. Mon Weather Rev 120:2709–2712CrossRefGoogle Scholar
  15. Goswami BN (2005) South Asian monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability of the atmosphere–ocean climate system, chap 2, praxis. Springer, Berlin, pp 19–61Google Scholar
  16. Goswami BN, Xavier PK (2003) Potential predictability and extended range prediction of Indian summer monsoon breaks. Geophys Res Lett 30(18):1966. doi: 10.1029/2003GL017,810 CrossRefGoogle Scholar
  17. Goswami BN, Krishnamurthy V, Annamalai H (1999) A broad-scale circulation index for the interannual variability of the Indian summer monsoon. Q J R Meteorol Soc 125:611–633. doi: 10.1002/qj.49712555412 CrossRefGoogle Scholar
  18. Goswami BN, Wheeler M, Gottschalck JC, Waliser DE (2011) Intra-seasonal variability and forecasting: a review of recent research. The global monsoon system: research and forecast, vol 5, 2nd edn. World Scientific Publication Company in collaboration with WMO, pp 389–407Google Scholar
  19. Griffies SM, Harrison MJ, Pacanowski RC Rosati A (2004) A technical guide to MOM4, GFDL ocean group technical report no. 5. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, p 342Google Scholar
  20. Hamill TM, Snyder C, Morss RE (2000) A comparison of probabilistic forecast from bred, singular-vector and perturbed observation ensembles. Mon Weather Rev 128:1835–1851CrossRefGoogle Scholar
  21. Hanssen AW, Kuipers WJA (1965) On the relationship between the frequency of rain and various meteorological parameters. Meded Verh 81:2–15Google Scholar
  22. Harrison MSJ, Palmer TN, Richardson DS, Buizza R (1999) Analysis and model dependencies in medium-range ensembles: two transplant case studies. Q J R Meteorol Soc 125:2487–2515CrossRefGoogle Scholar
  23. Hoffman RN, Kalnay E (1983) Lagged average forecasting, an alternative to Monte Carlo forecasting. Tellus 354:100–118CrossRefGoogle Scholar
  24. Houtekamer PL, Derome J (1995) Methods for ensemble prediction. Mon Weather Rev 123:2181–2196CrossRefGoogle Scholar
  25. Houtekamer PL, Lefaivre L, Derome J (1995) The RPN ensemble prediction system. In: Proceedings of the seminar on predictability, vol II. ECMWF, Reading, Berkshire, UK, pp 121–146Google Scholar
  26. Hoyos CD, Webster PJ (2007) The role of intraseasonal variability in the nature of Asian monsoon precipitation. J Clim 20(17):4402–4424. doi: 10.1175/JCLI4252.1 CrossRefGoogle Scholar
  27. Jiang X, Li T, Wang B (2004) Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J Clim 17:1022–1039CrossRefGoogle Scholar
  28. Jiang X, Waliser DE, Wheeler MC, Jones C, Lee MI, Schubert SD (2008) Assessing the skill of an all-season statistical forecast model for the Madden–Julian oscillation. Mon Weather Rev 136:1940–1956CrossRefGoogle Scholar
  29. Jones C, Carvalho LMV, Wayne HR, Waliser DE, Schemm JKE (2004) A statistical forecast model of tropical intraseasonal convective anomalies. J Clim 17:2078–2095CrossRefGoogle Scholar
  30. Joseph S, Sahai AK, Goswami BN, Terray P, Masson S, Luo JJ (2012) Possible role of warm SST bias in the simulation of boreal summer monsoon in SINTEX-F2 coupled model. Clim Dyn 38:1561–1576. doi: 10.1007/s00382-011-1264-1 CrossRefGoogle Scholar
  31. Kharin VV, Zwiers FW (2003) On the ROC score of probability forecasts. J Clim 16:4145–4150Google Scholar
  32. Kim HM, Kang IS (2008) The impact of ocean–atmosphere coupling on the predictability of boreal summer intraseasonal oscillation. Clim Dyn 31:859–870CrossRefGoogle Scholar
  33. Kim HM, Webster PJ, Hoyos CD, Kang IS (2010) Ocean-atmosphere coupling and the boreal winter MJO. Clim Dyn 35(5):771–784CrossRefGoogle Scholar
  34. Krishnamurti TN, Subramaniam M, Daughenbaugh G, Oosterhof D, Xue JH (1992) One-month forecast of wet and dry spells of the monsoon. Mon Weather Rev 120:1191–1223CrossRefGoogle Scholar
  35. Kumar A, Barnston AG, Hoerling MP (2001) Seasonal predictions, probabilistic verifications, and ensemble size. J Clim 14(7):1671–1676Google Scholar
  36. Lau KM, Wu HT, Yang S (1998) Hydrologic processes associated with the first transition of the Asian summer monsoon: a pilot satellite study. Bull Am Meteorol Soc 79:1871–1882CrossRefGoogle Scholar
  37. Lee JY et al (2010) How are seasonal prediction skills related to models performance on mean state and annual cycle? Clim Dyn 35:267–283CrossRefGoogle Scholar
  38. Lorenz EN (1985) The growth of errors in prediction. In: Ghil M, Benzi R (eds) Turbulence and predictability in geophysical fluid dynamics and climate dynamics. Corso Soc. It. di Fis., Bologna, pp 243–265Google Scholar
  39. Mason IB (2003) Binary events. In: Jolliffe IT, Stephenson DB (eds) Forecast verification: a practitioner’s guide in atmospheric science. Wiley, England, pp 45–46Google Scholar
  40. Mason SJ, Graham NE (1999) Conditional probabilities, relative operating characteristics, and relative operating levels. Weather Forecast 14:713–725CrossRefGoogle Scholar
  41. Matthews AJ (2008) Primary and successive events in the Madden–Julian oscillation. Q J R Meteorol Soc 134:439–453CrossRefGoogle Scholar
  42. Mitra AK, Bohra AK, Rajeevan MN, Krishnamurti TN (2009) Daily Indian precipitation analyses formed from a merged of rain-gauge with TRMM TMPA satellite derived rainfall estimates. J Meteorol Soc Jpn 87A:265–279CrossRefGoogle Scholar
  43. Molteni F, Buizza R, Palmer TN, Petroliagis T (1996) The ECMWF ensemble prediction system: methodology and validation. Q J R Meteorol Soc 122:73–119CrossRefGoogle Scholar
  44. Palmer TN (1993) Extended-range atmospheric prediction and the Lorenz model. Bull Am Meteorol Soc 74:49–65. doi: 10.1175/1520-0477(1993)074<0049:ERAPAT>2.0.CO;2 CrossRefGoogle Scholar
  45. Palmer TN, Molteni F, Mureau R, Buizza R, Chapelet P, Tribbia J (1993) Ensemble prediction. In: Proceedings of the ECMWF seminar on validation of models over Europe, vol 1, 7–11 September 1992, Reading, UK, pp 21–66Google Scholar
  46. Pattanaik DR, Kumar A (2010) Prediction of summer monsoon rainfall over India using the NCEP climate forecast system. Clim Dyn 34:557–572CrossRefGoogle Scholar
  47. Rai S, Krishnamurthy V (2011) Error growth in climate forecast system daily retrospective forecasts in south Asian monsoon. J Geophys Res 116:D0310. doi: 10.1029/2010JD014840 Google Scholar
  48. Rajeevan M, Bhate J, Kale JD, Lal B (2006) High resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Curr Sci 91:296–306Google Scholar
  49. Rajendran K, Kitoh A (2006) Modulation of tropical intraseasonal oscillations by atmosphere–ocean coupling. J Clim 19:366–391CrossRefGoogle Scholar
  50. Richardson DS (1998) The relative effect of model and analysis differences on ECMWF and UKMO operational forecasts. In: Proceedings of the ECMWF workshop on predictability, ECMWF, Shinfield Park, Reading RG2 9AX, UKGoogle Scholar
  51. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057. doi: 10.1175/2010BAMS3001 CrossRefGoogle Scholar
  52. Saha S et al (2013) The NCEP climate forecast system version 2. J Clim (submitted). Available at
  53. Sahai AK, Chattopadhyay R, Goswami BN (2008) A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall. Geophys Res Lett 35:L19705. doi: 10.1029/2008GL035461 CrossRefGoogle Scholar
  54. Sahai AK, Sharmila S, Abhilash S, Chattopadhyay R, Borah N, Krishna RPM, Joseph S, Roxy M, De S, Pattnaik S, Pillai PA (2013) Simulation and extended range prediction of monsoon intraseasonal oscillations in NCEP CFS/GFS version 2 framework. Curr Sci (special section Atmos Ocean Sci) 104:10Google Scholar
  55. Sengupta D, Goswami BN, Senan R (2001) Coherent intraseasonal oscillations of ocean and atmosphere during the Asian summer monsoon. Geophys Res Lett 28(21):4127–4130CrossRefGoogle Scholar
  56. Seo KH, Wang WQ, Gottschalck J, Zhang Q, Schemm JKE, Higgins WR, Kumar A (2009) Evaluation of MJO forecast skill from several statistical and dynamical forecast models. J Clim 22:2372–2388CrossRefGoogle Scholar
  57. Sharmila S, Pillai PA, Joseph S, Roxy M, Krishna RPM, Chattopadhyay R, Abhilash S, Sahai AK, Goswami BN (2013) Role of ocean–atmosphere interaction on northward propagation of Indian summer monsoon intra-seasonal oscillations (MISO). Clim Dyn. doi: 10.1007/s00382-013-1854-1 Google Scholar
  58. Shi L, Hendon HH, Alves O (2012) How predictable is the Indian Ocean dipole? Mon Weather Rev 140:3867–3884CrossRefGoogle Scholar
  59. Shukla J (1993) Predictability of short-term climate variations. In: Shukla J (ed) Prediction of interannual climate variations. NATO ASI series I: global environmental change, vol 6. Springer, Berlin, pp 217–232Google Scholar
  60. Shukla J, Gutzler DS (1983) Interannual variability and predictability of 500 mb geopotential heights over the northern hemisphere. Mon Weather Rev 111:1273–1279CrossRefGoogle Scholar
  61. Sikka DR, Gadgil S (1980) On the maximum cloud zone and the ITCZ over Indian longitudes during the southwest monsoon. Mon Weather Rev 108:1840–1853CrossRefGoogle Scholar
  62. Suhas E, Neena JM, Goswami BN (2012) An Indian monsoon intraseasonal oscillations (MISO) index for real time monitoring and forecast verification. Clim Dyn. doi: 10.1007/s00382-012-1462-5 Google Scholar
  63. Toth Z, Kalnay E (1993) Ensemble forecasting at NMC: the generation of perturbations. Bull Am Meteorol Soc 74:2317–2330CrossRefGoogle Scholar
  64. Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Weather Rev 125:3297–3319CrossRefGoogle Scholar
  65. Vitart F, Woolnough S, Balmaseda MA et al (2007) Monthly forecast of the Madden–Julian oscillation using a coupled GCM. Mon Weather Rev 135:2700–2715CrossRefGoogle Scholar
  66. Waliser DE, Jones C, Schemm JK, Graham NE (1999) A statistical extended-range tropical forecast model based on the slow evolution of the MJO. J Clim 12:1918–1939CrossRefGoogle Scholar
  67. Waliser DE, Lau KM, Stern W, Jones C (2003a) Potential predictability of the Madden–Julian oscillation. Bull Am Meteorol Soc 84:33–50CrossRefGoogle Scholar
  68. Waliser DE, Stern W, Schubert S, Lau KM (2003b) Dynamic predictability of intraseasonal variability associated with the Asian summer monsoon. Q J R Meteorol Soc 129:2897–2925CrossRefGoogle Scholar
  69. Wang B (2005) Theory. Intraseasonal variability in the atmosphere–ocean climate system. Springer, Berlin, pp 307–351CrossRefGoogle Scholar
  70. Wang W, Chen M, Kumar A (2009) Impacts of ocean surface on the northward propagation of the boreal summer intraseasonal oscillation in the NCEP climate forecast system. J Clim 22:6561–6576CrossRefGoogle Scholar
  71. Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO Index: development of an index for monitoring and prediction. Mon Weather Rev 132:1917–1932CrossRefGoogle Scholar
  72. Wheeler H, Weickmann KM (2001) Real-time monitoring and prediction of modes of coherent synoptic to intraseasonal tropical variability. Mon Weather Rev 129:2677–2694CrossRefGoogle Scholar
  73. Wilks DS (2005) Statistical methods in the atmospheric sciences. International geophysical series, vol 100. Academic Press, London, p 464Google Scholar
  74. Yasunari T (1980) A quasi-stationary appearance of the 30–40 day period in the cloudiness fluctuations during the summer monsoon over India. J Meteorol Soc Jpn 58:225–229Google Scholar
  75. Zhang Q, Van den Dool H (2012) Relative merit of model improvement versus availability of retrospective forecasts: the case of climate forecast system MJO prediction. Weather Forecast 27:1045–1051CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • S. Abhilash
    • 1
  • A. K. Sahai
    • 1
    Email author
  • N. Borah
    • 1
  • R. Chattopadhyay
    • 1
  • S. Joseph
    • 1
  • S. Sharmila
    • 1
  • S. De
    • 1
  • B. N. Goswami
    • 1
  • Arun Kumar
    • 2
  1. 1.Indian Institute of Tropical MeteorologyPuneIndia
  2. 2.National Center for Environmental PredictionCamps SpringsUSA

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