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Advances in Atmospheric Sciences

, Volume 26, Issue 5, pp 813–839 | Cite as

Improving multimodel weather forecast of monsoon rain over China using FSU superensemble

  • T. N. KrishnamurtiEmail author
  • A. D. Sagadevan
  • A. Chakraborty
  • A. K. Mishra
  • A. Simon
Article

Abstract

In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are: ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.

Key words

THORPEX ensemble mean superensemble TRMM South China Sea monsoon 

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References

  1. Anthes, R. A., 1983: Regional models of the atmosphere in middle latitudes. Mon. Wea. Rev., 111, 1306–1330.CrossRefGoogle Scholar
  2. Chakraborty, A., T. N. Krishnamurti, and C. Gnanaseelan, 2007: Prediction of the diurnal cycle using a multimodel superensemble. Part II: Clouds. Mon. Wea. Rev., 135, 4097–4116.CrossRefGoogle Scholar
  3. Krishnamurti, T. N., and J. Sanjay, 2003: A new approach to the cumulus Parameterization issue. Tellus, 55, 275–300.CrossRefGoogle Scholar
  4. Krishnamurti, T. N., H. Bedi, G. Rohaly, and D. Oosterhof, 1996: Partitioning of the seasonal simulation of a monsoon climate. Mon. Wea. Rev., 124, 1499–1520.CrossRefGoogle Scholar
  5. Krishnamurti, T. N., C. M. Kishtawal, T. LaRow, D. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran, 1999: Improved skills for weather and seasonal climate forecasts from multi-model superensemble. Science, 285(5433), 1548–1550.CrossRefGoogle Scholar
  6. Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, C. E. Williford, S. Gadgil, and S. Surendran, 2000a: Multi-model superensemble forecasts for weather and seasonal climate. J. Climate, 13, 4196–4216.CrossRefGoogle Scholar
  7. Krishnamurti, T. N., C. M. Kishtawal, D. W. Shin, and C. E. Williford, 2000b: Improving tropical precipitation forecasts from a multi analysis superensemble. J. Climate, 13, 4217–4227.CrossRefGoogle Scholar
  8. Krishnamurti, T. N., and Coauthors, 2003: Improved skills for the anomaly correlation of geopotential heights at 500 hPa. Mon. Wea. Rev., 131, 1082–1102.CrossRefGoogle Scholar
  9. Krishnamurti, T. N., T. S. V. Kumar, W.-T. Yun, A. Chakraborty, and L. Stefanova, 2006: Weather and seasonal climate forecasts using the superensemble approach. Predictability of Weather and Climate, T. Palmer and R. Hagedorn, Eds., Cambridge University Press, London, 532–560.Google Scholar
  10. Krishnamurti, T. N., S. Basu, J. Sanjay, and C. Gnanaseelan, 2007: Evaluation of several different planetary boundary layer schemes within a single model, a unified model and a multimodel superensemble. Tellus A, 60, 42–61.Google Scholar
  11. Krishnamurti, T. N., C. Gnanaseelan, A. K. Mishra, and A. Chakraborty, 2008: Improved forecasts of diurnal cycle in tropics using multiple global models. Part I: Precipitation. J. Climate, 21, 4029–4043.CrossRefGoogle Scholar
  12. Mishra, A. K., and T. N. Krishnamurti, 2007: Current status of multimodel Superensemble and operational NWP forecast of the Indian summer monsoon. Journal of Earth System Science, 116(5), 369–384.CrossRefGoogle Scholar
  13. Palmer, T. N., F. Molteni, R. Mureau, R. Buizza, P. Chapelet, and J. Tribbia, 1993: Ensemble prediction. Proc. Seminar on Validation of Models over Europe, Vol. 1, Reading, United Kingdom, ECMWF, 21–66.Google Scholar
  14. Richardson, D., R., Buizza, and R. Hagedorn, 2005: Final report of the 1st Workshop on the THORPEX Interactive Grand Global Ensemble (TIGGE). WMO TD No. 1273, WWRP-THORPEX No. 5, 39pp.Google Scholar
  15. Schaefer, J. T., 1990: “The critical success index as an indicator of warning skill”. Wea. Forecasting, 5, 570–575.CrossRefGoogle Scholar
  16. Stefanova, L., and T. N. Krishnamurti, 2002: Interpretation of seasonal climate forecast using Brier skill score, FSU superensemble, and the AMIP-I data set. J. Climate, 15, 537–544.CrossRefGoogle Scholar
  17. Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 2317–2330.CrossRefGoogle Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer Berlin Heidelberg 2009

Authors and Affiliations

  • T. N. Krishnamurti
    • 1
    Email author
  • A. D. Sagadevan
    • 1
  • A. Chakraborty
    • 1
  • A. K. Mishra
    • 1
  • A. Simon
    • 1
  1. 1.Department of MeteorologyFlorida State UniversityTallahasseeUSA

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