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References

Journal article

  • Bretherton, C. S., C. Smith, and J. M. Wallace, 1992: An intercomparison of methods for finding coupled patterns in climate data, J. Climate, 5, 541-560.
  • Gillett, N. P., F. W. Zwiers, A. J. Weaver, G. C. Hegerl, M. R. Allen, and P. A. Stott, 2002: Detecting anthropogenic influence with a multi-model ensemble, GRL, 29(20), doi:10.1029/2002GL015836
  • Hagedorn, R., F. J. Doblas-Reyes and T. N. Palmer, 2005: The Rationale Behind the Success of Multi-model Ensembles in Seasonal Forecasting - I. Basic Concept, Tellus, 57A, 219-233.
  • Hagedorn, R., F. J. Doblas-Reyes and T. N. Palmer, 2005: The Rationale Behind the Success of Multi-model Ensembles in Seasonal Forecasting - II. Calibration and Combination, Tellus, 57A, 234-252.
  • Hoffman, R. N., and E. Kalnay, 1983: Lagged average forecasting, an alternative to Monte Carlo forecasting. Tellus., 35A, 100-118.
  • Hsieh, W. W. and B. Tang, 1998: Applying neural network models to prediction and data analysis in meteorology and oceanography, Bull. Amer. Meteor. Soc., 79, 1855-1870.
  • Kharin, V. V., and F. W. Zwiers, 2002: Climate predictions with Multimodel Ensembles, J. Climate, 15, 793-799.
  • Krishnamurti, T. N., C. M. Kishtawal, Timothy E. LaRow, David R. Bachiochi, Zhan Zhang, C. Eric Williford, Sulochana Gadgil, Sajani Surendran, 1999: Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble, Science, 285, 1548-1550.
  • Krishnamurti, T. N., C. M. Kishtawal, Zhan Zhang, Timothy Larow, David Bachiochi, And Eric Williford, 2000: Multimodel Ensemble Forecasts for Weather and Seasonal Climate, J. Climate, 13, 4196-4216.
  • Krishnamurti, T. N., S. Surendran, D. W. Shin, R. J. Correa-Torres, T.S.V. Vijaya Kumar, E. Williford, C. Kummerow, R. F. Adler, J. Simpson, R. Kakr, W. S. Olson, and F. J. Turk, 2001: Real-time multianalysis- multimodel superensemble forecasts of precipitation using TRMM and SSM/I products. Mon. Wea. Rev., 129, 2861-2883.
  • Krishnamurti, T. N., K. Rajendan et al., 2003: Improved skill for the anomaly correlation of geopotential heights at 500hPa, Mon. Wea. Rev., 131, 1082-1102.
  • Leith, C, E.,1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409-418.
  • Krishinamurti, T. N., A. K. Mitra, W.-T. Yun, And T.S. V.V. Kumar, W. K. Dewar, 2006: Seasonal climate forecasts of the Asian monsoon using multiple coupled models. Tellus A, 58(4), 487-507.
  • Krishinamurti, T. N., T. S. V. Vijaya Kumar, Won-Tae Yun, Arun Chakraborty and Lydia Stefanova, 2006. Chapter 20: Weather and seasonal climate forecasts using the superensemble approach, In: Book of Predictability of weather and Climate, Eds. Tim Palmer and R. Hagedorn, Cambridge University Press, London, ISBN-13 9780-521-84882-4 hardback, ISBN-10 0-521-84882-2 hardback.
  • Matthew S. Wandishin and Steven L. Mullen, David J. Stensrud and Harold E. Brooks, 2001: Evaluation of a Short-Range Multimodel Ensemble System, Mon. Wea. Rev., 129, 730.
  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The new ECMWF ensemble prediction system: Methodology and validation, Quart. J. Roy. Meteor. Soc., 122, 73-119.
  • Palmer, T.N., A. Aleessandri, U. Andersen, P. Cantelaube, M. Davey, P. Delecluse, M. Deque, E. Diez, F.J. Doblas-Reyes, H. Feddersen, R. Graham, S. Gualdi, J.-f. Gueremy, R. Hagedorn, M. Hoshen, N. keenlyside, M. Latif, A. Lazar, E. Maisonnave, V. Marletto, A.P. Morse, B. Orfila, P. Rogel, J.-J. Terres, M.C. Thomson, 2004: Development of a Europena Multimodel Ensemble System for Seasonal-to -Interannual Prediction (DEMETER), BAMS, 853-872.
  • Park, S, S.-D. Kang, W.-T. Kwon, 2005: Prediction of Boreal Winter Precipitation by Nonlinear Multimodel Ensemble Technique, J. Korean Meteorological Society, 41(6) 1015-1028.
  • Peng, P., Arun Kumar, and Huug van den Dool, 2002: An analysis of multimodel ensemble predictions for seasonal climate anomalies, J. Geophys. Res., 107(D23), doi:10.1029/2002JD002712
  • Phillips, T. J., 1996: Documentation of the AMIP models on the World Wide Web, Bull. Amer. Meteor. Soc., 77, 1191-1196.
  • Raisanen J., and T. N. Palmer, 2001: A Probability and Decision-Model Analysis of a Multimodel Ensemble of Climate Change Simulations, J. Climate, 14, 3212-3226.
  • Reed, R. D. and R. J. Marks, 1999: Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, MIT Press, ISBN 0-262-18190-8.
  • Stephenson, D. B. and F. J., Doblasreyes, 2000: Statistical methods for interpreting Monte Carlo ensemble forecasts, 52(3), 300-322.
  • Toth, Z., and E. Kalnay, 1997: Ensemble forecasting at NCEP: the breeding method. Mon. Wea. Rev., 125, 3297-3318.
  • Toth, Z., and E. Kalnay, 1993: Ensemble Forecasting at the NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317-2330
  • Yun, W. T., L. Stefanova, A.K. Mitra, T. S. V. Vijaya Kumar, W. Dewar and T. N. Krishnamurti ,2005: A Multi-model Superensemble Algorithm for Seasonal Climate Prediction using DEMETER Forecast, Tellus, 57A, 280-289.
  • Yun, W. T., L. Stefanova, and T. N. Krishnamurti., 2003: Improvement of the superensemble technique for seasonal forecasts. J. Climate., 16, 3834-3840.

Proceedings

  • Thomson, M. C., F. J. Doblas-Reyes, S. J. Mason, R. Hagedorn, S. J. Connor, T.Phindela, A.P. Morse, and T.N. Palmer, 2006: Malaria early warnings based on seasonal climate forecasts from multi-model ensemble, Nature, doi:10.1038/nature04503.