An optimum initial manifold for improved skill and lead in long-range forecasting of monsoon variability

A Correction to this article was published on 02 April 2021

This article has been updated

Abstract

Using an initial manifold approach, an ensemble forecast methodology is shown to simultaneously increase lead and realizable skill in long-range forecasting of monsoon over continental India. Initial manifold approach distinguishes the initial states that have coherence from a collection of unrelated states. In this work, an optimized and validated variable resolution general circulation model is being adopted for long-range forecasting of monsoon using the multi-lead ensemble methodology. In terms of realizable skill (as against potential) at resolution (~60km) and lead (2–5 months) considered here, the present method performs very well. The skill of the improved methodology is significant, capturing 9 of the 12 extreme years of monsoon during 1980–2003 in seasonal (June–August) scale. Eight-member ensemble-average hindcasts carried out for realizable skill with lead of 2 (for June) to 5 (for August) months and an optimum ensemble is presented.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Change history

References

  1. Abhilash S, Sahai AK, Borah N, Chattopadhyay R, Joseph S, Sharmila S, de S, Goswami BN, Kumar A (2014) Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2. Clim Dyn 42:2801–2815. https://doi.org/10.1007/s00382-013-2045-9

    Article  Google Scholar 

  2. Buizza R (1997) Potential forecast skill of ensemble prediction, and spread and skill distribution of the ECMWF Ensemble Prediction System. Mon Weather Rev 125:99–119

    Article  Google Scholar 

  3. Buizza R, Hollingsworth A, Lalaurette F, Ghelli A (1999a) Probabilistic predictions of precipitation using the ECMWF Ensemble Prediction System. Weather Forecast 14:168–189

    Article  Google Scholar 

  4. Buizza R, Miller M, Palmer TN (1999b) Stochastic representation of modeluncertainties in the ECMWF Ensemble Prediction System. Q J R Meteorol Soc 125:2887–2908. https://doi.org/10.1002/qj.49712556006

    Article  Google Scholar 

  5. Goswami BN (1998) Interannual variations of Indian summer monsoon in a GCM: external conditions versus internal feedbacks. J Clim 11:501–522

    Article  Google Scholar 

  6. Goswami P, Gouda KC (2009) Comparative evaluation of two ensembles for long-range forecasting of monsoon rainfall. Mon Weather Rev 137(9):2893–2907

    Article  Google Scholar 

  7. Goswami P, Gouda KC (2010) Evaluation of a dynamical basis for advance forecasting of the date of onset of monsoon rainfall over India. Mon Weather Rev 138(8):3120–3141

    Article  Google Scholar 

  8. Goswami P, Sijikumar S, Mandal A (2005) Seasonal cycle and intraseasonal oscillations in the interannual variability over the monsoon region. Geophys Res Lett 32:L06810. https://doi.org/10.1029/2004GL022171

    Article  Google Scholar 

  9. Gouda KC, Nahak S, Goswami P (2018) Evaluation of a GCM in seasonal forecasting of extreme rainfall events over continental India. Weather and Climate Extremes 21:10–16

    Article  Google Scholar 

  10. Gouda KC, Nahak S, Goswami P (2020) Deterministic seasonal quantitative Precipitation forecasts: benchmark skill with a GCM. Pure Appl Geophys 177:4443–4456. https://doi.org/10.1007/s00024-020-02463-7

    Article  Google Scholar 

  11. Ham et al (2019) A newly developed APCC SCoPS and its prediction of East Asia seasonal climate variability. Clim Dyn 53:3703–3704. https://doi.org/10.1007/s00382-019-04894-y

    Article  Google Scholar 

  12. Hendon H, Liebmann B (1990) The intraseasonal (30-50 day) oscillation of the Australian summer monsoon. J Atmos Sci 47:2909–2923

    Article  Google Scholar 

  13. Hendon H, Liebmann HB, Newman M, Glick JD, Schemm T (2000) Medium range forecast errors associated with active period of Madden-Julian Oscillation. Mon Weather Rev 128:69–86

    Article  Google Scholar 

  14. Hourdin F, Musat I, Bony S, Braconnot P, Codron F, Dufresne JL, Fairhead L, Filiberti MA, Friedlingstein P, Grandpeix JY, Krinner G, LeVan P, Li ZX, Lott F (2006) The LMDZ4 general circulation model: climate performance and sensitivity to parameterized physics with emphasis on tropical convection. Clim Dyn 27(7-8):787–813

    Article  Google Scholar 

  15. Joshi S, Gouda KC, Goswami P (2020) Seasonal rainfall forecast skill over Central Himalaya with an atmospheric general circulation model. Theor Appl Climatol 139:237–250. https://doi.org/10.1007/s00704-019-02971-0

    Article  Google Scholar 

  16. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc 77:437–471

    Article  Google Scholar 

  17. Kang IS, Shukla J. (2006) Dynamic seasonal prediction and predictability of the monsoon. In: The Asian Monsoon. Springer Praxis Books. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-37722-0_15

  18. Kirtman B, Shukla J (2002) Interactive coupled ensemble: a new coupling strategy for CGCMs. Geophys Res Lett 29:1367. https://doi.org/10.1029/2002GL014834

    Article  Google Scholar 

  19. Krishnamurti TN, Ardanuy P (1980) The 10- to 20 day westward propagating mode and breaks in the monsoons. Tellus 32:15–26

    Google Scholar 

  20. Kyouda M, Kusunoki S (2002) Ensemble Prediction System. Outline of the Operational Numerical Weather Prediction at the Japan Meteorological Agency. JMA 59-63

  21. Lorenz EN (1965) A study of the predictability of a 28-variable atmospheric model. Tellus 17:321–333

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. Molteni F, Palmer TN (1993) Predictability and finite-time instability of the northern winter circulation. Quart J Roy Meteor Soc 119:269–298

    Article  Google Scholar 

  24. Moron V, Robertson VAW, Ward MN (2006) Seasonal predictability and spatial coherence of rainfall characteristics in the tropical setting of Senegal. Mon Weather Rev 134:3468–3482

    Article  Google Scholar 

  25. Mullen SL, Buizza R (2001) Quantitative precipitation forecasts over the United States by the ECMWF Ensemble Prediction System. Mon Weather Rev 129:638–663

    Article  Google Scholar 

  26. Mullen SL, Buizza R (2002) The impact of horizontal resolution and ensemble size on probabilistic forecasts of precipitation by the ECMWF Ensemble Prediction System. Weather Forecast 17:173–191

    Article  Google Scholar 

  27. Palmer TN, Alessandri A, Andersen U, Cantelaube P, Davey M, Délécluse P, Déqué M, Díez E, Doblas-Reyes FJ, Feddersen H, Graham R, Gualdi S, Guérémy JF, Hagedorn R, Hoshen M, Keenlyside N, Latif M, Lazar A, Maisonnave E, Marletto V, Morse AP, Orfila B, Rogel P, Terres JM, Thomson MC (2004) Development of a european multimodel ensemble system for seasonal-tointerannual prediction (DEMETER). Bull Amer Meteor Soc 85:853–872

    Article  Google Scholar 

  28. Rajeevan M, Bhate J, Kale JD, Lal B (2006) High resolution daily gridded rainfall data for the Indian region. Curr Sci 91(3):296–306

    Google Scholar 

  29. Saha S et al (2006) The NCEP Climate Forecast System. J Clim 19:3483–3517. https://doi.org/10.1175/JCLI3812.1

    Article  Google Scholar 

  30. Saha S, Moorthi S, Pan H, Wu X, Wang J et al (2010) The NCEP Climate Forecast System Reanalysis. Bull Am Meteorol Soc 91:1015–1057. https://doi.org/10.1175/2010BAMS3001.1

    Article  Google Scholar 

  31. Saha S, Moorthi S, Wu X, Wang J et al (2014) The NCEP Climate Forecast System Version 2. J Clim 27:2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1

    Article  Google Scholar 

  32. Sharma, OP, Upadhyaya, HC, Braine-Bonnaire Th, Sadourney R (1987) Experiments on regional forecasting using a stretched coordinate general circulation model. Short and medium range numerical weather prediction. Special volume of Meteo Soc Japan, Ed T. Matsuno 65: 263–271

  33. Toth Z, Kalnay E (1993) Ensemble forecasting at NMC: the generation of perturbations. Bull Amer Meteor Soc 74:2317–2330

    Article  Google Scholar 

  34. Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP: the breeding method. Mon Weather Rev 12:3297–3319

    Article  Google Scholar 

  35. Wang B, Kang IS, Lee JY (2004) Ensemble simulations of Asian-Australian monsoon variability during 1997/1998 El Nino by 11 AGCMs. J Clim 17:803–818

    Article  Google Scholar 

  36. Yasunari T (1979) Cloudiness fluctuation associated with the Northern Hemisphere summer monsoon. J Meteor Soc Japan 57:227–242

    Article  Google Scholar 

  37. Yun WT, Stefanova L, Krishnamurti TN (2003) Improvement of the multi-model super ensemble technique for seasonal forecasts. J Clim 22:3834–3840

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge NCEP and IMD for the data support. The authors acknowledge the APCC MME Producing Centers for making their hindcast/forecast data available for analysis, the APEC Climate Center for collecting and archiving the data, as well as for producing APCC MME predictions. We acknowledge Department of Science and Technology for the project support.

Availability of data and material

The APCC model simulation, NCEP reanalysis datasets are freely available on the web sites. Gridded rainfall observation data are collected from India Meteorological Department.

Code availability

Model results are available from the corresponding author upon request.

Funding

This work is supported by the projects funded by the Department of Science and Technology (DST) and National Mission on Himalayan Studies (NMHS) of Ministry of Environment, forest and climate change, Govt. of India

Author information

Affiliations

Authors

Contributions

KCG conceived the presented idea and performed computations. KCG, SJ, and NB analyzed the results from the computations and wrote the manuscript.

Corresponding author

Correspondence to K. C. Gouda.

Ethics declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Conflict of interest

The author declares that he has no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: The presentation of Table 2 was incorrect.

Supplementary Information

ESM 1

(DOCX 54 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gouda, K.C., Joshi, S. & Bhat, N. An optimum initial manifold for improved skill and lead in long-range forecasting of monsoon variability. Theor Appl Climatol (2021). https://doi.org/10.1007/s00704-021-03589-x

Download citation