Skip to main content

Advertisement

Log in

High-resolution operational monsoon forecasts: an objective assessment

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Optimization of computational efficiency is indispensable in the incorporation of numerical complexity in a pragmatic climate forecast system. From the resource optimization standpoint, the debate regarding, to what extent increased computing efficiency and expense on resources has reduced the signal-to-noise ratio and improved our understanding towards future climate states on different time scales, still continues. With the recent advancement of real time climate forecasts from different operational agencies with increased computational efficiencies and resources, it has become necessary to perform an objective evaluation of the high resolution operational monsoon forecasts to conform if the high resolution outlooks are skillful enough as compared to a low resolution version. In this paper, we have performed a quantitative comparison of the extended range (~2–3 weeks) forecasts of monsoon intraseasonal oscillations (MISO) obtained from the climate forecast system model version 2 developed at National Centre for Environmental Prediction USA at two different resolutions: T126 (~100 km) and T382 (~38 km). It is observed that, higher model resolution (T382) has provided better basic state for MISO along with large reduction in climatological biases in June–September precipitation than the lower resolution forecast (T126). However, compared to the computing resources, there is no significant improvement in the prediction skill from increased horizontal resolution.

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
Fig. 8

Similar content being viewed by others

References

  • Abhilash S, Sahai AK, Pattnaik S, De S (2013a) Predictability during active break phases of Indian summer monsoon in an ensemble prediction system using climate forecast system. J Atmos Sol-Terr Phys 100–101:13–23. doi:10.1016/j.jastp.2013.03.017

    Article  Google Scholar 

  • Abhilash S, Sahai AK, Pattnaik S, Goswami BN, Kumar A (2013b) 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 1–16. doi:10.1002/joc.3668

  • Benedict JJ, Randall DA (2009) Structure of the madden–julian oscillation in the superparameterized CAM. J Atmos Sci 66:3277–3296. doi:10.1175/2009JAS3030.1

    Article  Google Scholar 

  • Borah N, Abhilash S, Joseph S, Chattopadhyay R, Sharmila S, Sahai AK (2013) Development of extended range prediction system using CFSv2 and its verification. IITM Res. Rep 130 (ISSN 0252-1075). http://www.tropmet.res.in/~lip/Publication/RR-pdf/RR-130.pdf

  • Brunet G et al (2010) Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull Am Meteorol Soc 91:1397–1406. doi:10.1175/2010BAMS3013.1

    Article  Google Scholar 

  • 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 134:2051–2066. doi:10.1002/qj.346

    Article  Google Scholar 

  • Charney J, Shukla J (1981) Predictability of monsoons. In: Lighthill J, Pearce RP (eds) Monsoon dynamics. Cambridge University Press, Cambridge, pp 99–110

    Chapter  Google Scholar 

  • Charney J, Quirk WJ, Chow S-H, Kornfield J (1977) A comparative study of the effects of albedo change on drought in semi-arid regions. J Atmos Sci 34:1366–1385. doi:10.1175/1520-0469(1977)034<1366:ACSOTE>2.0.CO;2

    Article  Google Scholar 

  • Chen S, May P, Doyle J, Flatau M, Schmidt J (2013) Air-sea interaction influence on the intraseasonal forecast of MJO initiation and propagation. Abstract for 2013 first joint workshop of GODAE OceanView & WGNE, 19–21 March, Washington, DC

  • Dias J, Tulich SN, Kiladis GN (2012) An object-based approach to assessing the organization of tropical convection. J Atmos Sci 69:2488–2504. doi:10.1175/JAS-D-11-0293

    Article  Google Scholar 

  • Fu X, Wang B, Bao Q, Liu P, Lee J-Y (2009) Impacts of initial conditions on monsoon intraseasonal forecasting. Geophys Res Lett 36:L08801

    Google Scholar 

  • Fu X, Wang B, Lee J-Y et al (2011) Sensitivity of dynamical intraseasonal prediction skills to different initial conditions. Mon Weather Rev 139:2572–2592. doi:10.1175/2011MWR3584.1

    Article  Google Scholar 

  • Goswami BN (2005) South Asian monsoon intraseasonal variability in the atmosphere-ocean climate system. Springer, Berlin, pp 19–61

    Book  Google Scholar 

  • Goswami BN, Wheeler MC, Gottschalck JC, Waliser DE (2008) Intraseasonal 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 389–407

  • Gottschalck J, Wheeler M, Weickmann K et al (2010) A framework for assessing operational madden–julian oscillation forecasts: a CLIVAR MJO working group project. Bull Am Meteorol Soc 91:1247–1258. doi:10.1175/2010BAMS2816.1

    Article  Google Scholar 

  • Griffies S (2004) Fundamentals of ocean climate models. Princeton University Press, Princeton

    Google Scholar 

  • Jung T, Rhines PB (2007) Greenland’s pressure drag and the atlantic storm track. J Atmos Sci 64:4004–4030. doi:10.1175/2007JAS2216.1

    Article  Google Scholar 

  • Jung T, Gulev SK, Rudeva I, Soloviov V (2006) Sensitivity of extratropical cyclone characteristics to horizontal resolution in the ECMWF model. Q J R Meteorol Soc 132:1839–1857. doi:10.1256/qj.05.212

    Article  Google Scholar 

  • Jung T, Miller MJ, Palmer TN (2010) Diagnosing the origin of extended-range forecast errors. Mon Weather Rev 138:2434–2446. doi:10.1175/2010MWR3255.1

    Article  Google Scholar 

  • Kim D, Sperber K, Stern W et al (2009) Application of MJO simulation diagnostics to climate models. J Clim 22:6413–6436. doi:10.1175/2009JCLI3063.1

    Article  Google Scholar 

  • Kinter JL et al (2013) Revolutionizing climate modeling with project athena: a multi-institutional, international collaboration. Bull Am Meteorol Soc 94:231–245. doi:10.1175/BAMS-D-11-00043

    Article  Google Scholar 

  • Li C, Jia X, Ling J et al (2009) Sensitivity of MJO simulations to diabatic heating profiles. Clim Dyn 32:167–187. doi:10.1007/s00382-008-0455-x

    Article  Google Scholar 

  • Lin J, Mapes B, Zhang M, Newman M (2004) Stratiform precipitation, vertical heating profiles, and the Madden–Julian oscillation. J Atmos Sci 61:296–309. doi:10.1175/1520-0469(2004)061<0296:SPVHPA>2.0.CO;2

    Article  Google Scholar 

  • Lin H, Brunet G, Derome J (2008) Forecast skill of the Madden–Julian oscillation in two Canadian atmospheric models. Mon Weather Rev 136:4130–4149. doi:10.1175/2008MWR2459.1

    Article  Google Scholar 

  • Lin Y, Donner LJ, Petch J et al (2012) TWP-ICE global atmospheric model intercomparison: convection responsiveness and resolution impact. J Geophys Res. doi:10.1029/2011JD017018

    Google Scholar 

  • Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141

    Article  Google Scholar 

  • Macrae N (1999) The computers from Princeton, 1946–1952. John Von Neumann: the scientific genius who pioneered the modern computer, game theory, nuclear deterrence, and much more. AMS, pp 314–320

  • Madden RA, Julian PR (1994) Observations of the 40–50-day tropical oscillation—a review. Mon Weather Rev 122:814–837. doi:10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2

    Article  Google Scholar 

  • Mitra AK, Gupta MD, Singh SV, Krishnamurti TN (2003) Daily rainfall for the Indian monsoon region from merged satellite and rain gauge values: large-scale analysis from real-time data. J Hydrometeorol 4:769–781. doi:10.1175/1525-7541(2003)004<0769:DRFTIM>2.0.CO;2

    Article  Google Scholar 

  • Mylne K (2006) Predictability from a forecast provider’s perspective. Predictability of weather and climate, 1 edn. Cambridge University Press, Cambridge, pp 596–613. doi:10.1017/CBO9780511617652.024

  • Newman M, Sardeshmukh PD, Winkler CR, Whitaker JS (2003) A study of subseasonal predictability. Mon Weather Rev 131:1715–1732. doi:10.1175//2558.1

    Article  Google Scholar 

  • Orlanski I (2008) The rationale for why climate models should adequately resolve the mesoscale. In: Hamilton K, Ohfuchi W (eds) High resolution numerical modelling of the atmosphere and ocean. Springer, New York, pp 29–44

    Chapter  Google Scholar 

  • 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

    Article  Google Scholar 

  • Palmer TN, Doblas-Reyes FJ, Weisheimer A, Rodwell MJ (2008) Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull Am Meteorol Soc 89:459–470. doi:10.1175/BAMS-89-4-459

    Article  Google Scholar 

  • Pegion K, Kirtman BP (2008) The impact of air-sea interactions on the simulation of tropical intraseasonal variability. J Clim 21:6616–6635. doi:10.1175/2008JCLI2180.1

    Article  Google Scholar 

  • Pegion K, Sardeshmukh PD (2011) Prospects for improving subseasonal predictions. Mon Weather Rev 139:3648–3666. doi:10.1175/MWR-D-11-00004.1

    Article  Google Scholar 

  • Rajeevan M, Gadgil S, Bhate J (2010) Active and break spells of the Indian summer monsoon. J Earth Syst Sci 119:229–247. doi:10.1007/s12040-010-0019-4

    Article  Google Scholar 

  • Rashid HA, Hendon HH, Wheeler MC, Alves O (2011) Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system. Clim Dyn 36:649–661. doi:10.1007/s00382-010-0754-x

    Article  Google Scholar 

  • Reynolds CA, McLay JG, Goerss JS et al (2011) Impact of resolution and design on the US Navy global ensemble performance in the tropics. Mon Weather Rev 139:2145–2155

    Article  Google Scholar 

  • Richardson DS (2001) Measures of skill and value of ensemble prediction systems, their interrelationship and the effect of ensemble size. Q J R Meteorol Soc 127:2473–2489. doi:10.1002/qj.49712757715

    Article  Google Scholar 

  • Roxy M, Tanimoto Y, Preethi B et al (2012) Intraseasonal SST-precipitation relationship and its spatial variability over the tropical summer monsoon region. Clim Dyn. doi:10.1007/s00382-012-1547-1

    Google Scholar 

  • Saha S et al (2014) The NCEP climate forecast system version 2. J Clim. doi:10.1175/JCLI-D-12-00823.1

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Sahai AK, Sharmila S, Abhilash S et al (2013) Simulation and extended range prediction of monsoon intraseasonal oscillations in NCEP CFS/GFS version 2 framework. Curr Sci (Spec Sect: Atmos Ocean Sci) 104:1394–1408

    Google Scholar 

  • Seo K-H, Wang W, Gottschalck J et al (2009) Evaluation of MJO forecast skill from several statistical and dynamical forecast models. J Clim 22:2372–2388. doi:10.1175/2008JCLI2421.1

    Article  Google Scholar 

  • Sharmila S, Pillai PA, Joseph S et al (2013) Role of ocean–atmosphere interaction on northward propagation of Indian summer monsoon intra-seasonal oscillations (MISO). Clim Dyn 41:1651–1669. doi:10.1007/s00382-013-1854-1

    Article  Google Scholar 

  • Shukla J (2009) Seamless prediction of weather and climate: a new paradigm for modeling and prediction research. http://www.nws.noaa.gov/ost/climate/STIP/FY09CTBSeminars/shukla_021009.htm. Accessed 29 Nov 2013

  • Shukla J et al (2009) Strategies: revolution in climate prediction is both necessary and possible: a declaration at the world modelling summit for climate prediction. Bull Am Meteorol Soc 90:175–178. doi:10.1175/2008BAMS2759.1

    Article  Google Scholar 

  • Sooraj KP, Seo K-H (2012) Boreal summer intraseasonal variability simulated in the NCEP climate forecast system: insights from moist static energy budget and sensitivity to convective moistening. Clim Dyn 1–26. doi:10.1007/s00382-012-1631-6

  • 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 

  • Van Den Dool HM, Saha S (1990) Frequency dependence in forecast skill. Mon Weather Rev 118:128–137. doi:10.1175/1520-0493(1990)118<0128:FDIFS>2.0.CO;2

    Article  Google Scholar 

  • Vitart F (2004) Monthly forecasting at ECMWF. Mon Weather Rev 132:2761–2779. doi:10.1175/MWR2826.1

    Article  Google Scholar 

  • Vitart F, Woolnough S, Balmaseda MA, Tompkins AM (2007) Monthly forecast of the Madden–Julian oscillation using a coupled GCM. Mon Weather Rev 135:2700–2715. doi:10.1175/MWR3415.1

    Article  Google Scholar 

  • Waliser DE, Stern W, Schubert S, Lau KM (2003) Dynamic predictability of intraseasonal variability associated with the Asian summer monsoon. Q J R Meteorol Soc 129:2897–2925. doi:10.1256/qj.02.51

    Article  Google Scholar 

  • Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon Weather Rev 132:1917–1932. doi:10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2

    Article  Google Scholar 

  • Wilks DS (2005) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press, London p 183

    Google Scholar 

  • Waliser D (2006) Intraseasonal variability. In: Wang B (ed) The Asian monsoon. Springer, Heidelberg, p 844

    Google Scholar 

  • Zhang C (2005) Madden–Julian oscillation. Rev Geophys 43:RG2003. doi:10.1029/2004RG000158

    Google Scholar 

  • Zhang C et al (2013) Cracking the MJO nut. Geophys Res Lett 40:1223–1230. doi:10.1002/grl.50244

    Article  Google Scholar 

Download references

Acknowledgments

We are thankful to the anonymous Reviewers for their constructive suggestions on an earlier version of this manuscript. IITM is fully supported by the Ministry of Earth Sciences, Govt. India, New Delhi. The TRMM satellite estimated rainfall 3B42RT data were provided by the NASA/GSFC via ftp. TRMM is a joint project of NASA, USA and JAXA, Japan. We thankfully acknowledge the use of TRMM data in this project. The plots in this manuscript were prepared using the GrADS (http://www.iges.org/grads/head.html) and NCL (http://dx.doi.org/10.5065/D6WD3XH5) software. SS thanks Council for Scientific and Industrial Research (CSIR), New Delhi, India for research fellowship.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. K. Sahai.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahai, A.K., Abhilash, S., Chattopadhyay, R. et al. High-resolution operational monsoon forecasts: an objective assessment. Clim Dyn 44, 3129–3140 (2015). https://doi.org/10.1007/s00382-014-2210-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-014-2210-9

Keywords

Navigation