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Chapter 20 - Weather and seasonal climate forecasts using the superensemble approach

Published online by Cambridge University Press:  03 December 2009

By
T. N. Krishnamurti ,
T. S. V. Vijaya Kumar ,
Won-Tae Yun ,
Arun Chakraborty  and
Lydia Stefanova
Edited by
Tim Palmer  and
Renate Hagedorn
T. N. Krishnamurti
Affiliation:
Department of Meteorology, Florida State University, Tallahassee
T. S. V. Vijaya Kumar
Affiliation:
Department of Meteorology, Florida State University, Tallahassee
Won-Tae Yun
Affiliation:
Department of Meteorology, Florida State University, Tallahassee
Arun Chakraborty
Affiliation:
Department of Meteorology, Florida State University, Tallahassee
Lydia Stefanova
Affiliation:
Department of Meteorology, Florida State University, Tallahassee
Tim Palmer
Affiliation:
European Centre for Medium-Range Weather Forecasts
Renate Hagedorn
Affiliation:
European Centre for Medium-Range Weather Forecasts
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Summary

In this chapter we present a short overview of the Florida State University (FSU) superensemble methodology for weather and seasonal climate forecasts and cite some examples on application for hurricanes, numerical weather prediction (NWP) and seasonal climate forecasts. This is a very powerful method for producing a consensus forecast from a suite of multimodels and the use of statistical algorithms. The message conveyed here is that the superensemble reduces the errors considerably compared with those of the member models and of the ensemble mean. This is based on results from several recent publications, where varieties of skill scores such as anomaly correlation, root-mean-square (rms) errors and threat scores have been examined. The improvements in several categories such as seasonal climate prediction from coupled atmosphere–ocean multimodels and NWP forecasts for precipitation exceed those of the best models in a consistent manner and are more accurate compared with the ensemble mean. It is difficult to state, soon after a forecast is made, as to which among the member models would have the highest skill. The superensemble is very consistent in this regard and is thus more reliable. In this study, we show walk-through tables that illustrate the workings of the superensemble for a hurricane track and heavy rain forecast for a flooding event. A number of features of the superensemble – number of training days, behaviour as the number of models increased, reduction of systematic errors and use of a synthetic superensemble – illustrate the strength of this new forecast experience.

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Chapter
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Predictability of Weather and Climate , pp. 532 - 560
Publisher: Cambridge University Press
Print publication year: 2006

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References

Baumhefner, D. P. (1968). Application of a diagnostic numerical model to the tropical atmosphere. Mon. Weather Rev., 96, 218–282.0.CO;2>CrossRefGoogle Scholar
Ferraro, R. R. (1997). Special sensor microwave imager derived global rainfall estimates for climatological applications. J. Geophys. Res., 102, 16715–35CrossRefGoogle Scholar
Ferraro, R. R., Smith, E. A., Berg, W. and Huffman, G. J. (1998). A screening methodology for passive microwave precipitation retrieval algorithms. J. Atmos. Sci., 55, 1583–6002.0.CO;2>CrossRefGoogle Scholar
Frederiksen, C. S., Zhang, H., Balgovind, R. C., Nicholls, N., Drosdowsky, W. and Chambers, L. (2001). Dynamical seasonal forecasts during the 1997/98 ENSO using persisted SST anomalies. J. Climate, 14, 2675–952.0.CO;2>CrossRefGoogle Scholar
Goddard, L., Mason, S. J., Zebiak, S. J., Ropelewski, C. F., Basher, R. and Cane, M. A. (2001). Current approaches to seasonal-to-interannual climate predictions. Int. J. Climatol., 21, 1111–52CrossRefGoogle Scholar
Huffman, G. J., Adler, R. F., Rudolph, B., Schneider, U. and Keehn, P. (1995). Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information. J. Climate, 8, 1284–952.0.CO;2>CrossRefGoogle Scholar
Kalnay, E., Kanamitsu, M., Kistler, R., et al. (1996). The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc., 77, 437–712.0.CO;2>CrossRefGoogle Scholar
Kanamitsu, M., Kumar, A., Juang, H. M. H.., et al. (2002). NCEP dynamical seasonal forecast system 2000. Bull. Am. Meteorol. Soc., 83, 1019–372.3.CO;2>CrossRefGoogle Scholar
Kanamitsu, M., Cheng-Hsuan, L., , J. Schemm and , W. Ebisuzaki (2003). The predictability of soil moisture and near-surface temperature in hindcasts of the NCEP Seasonal Forecast Model. J. Climate., 16, 510–212.0.CO;2>CrossRefGoogle Scholar
Krishnamurti, T. N., Kishtawal, C. M., LaRow, T. E., et al. (1999). Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285, 1548–50CrossRefGoogle ScholarPubMed
Krishnamurti, T. N., Kishtawal, C. M., Zhang, Z., et al. (2000a). Multi-model super ensemble forecasts for weather and seasonal climate, J. Climate, 13, 4196–2162.0.CO;2>CrossRefGoogle Scholar
Krishnamurti, T. N., Kishtawal, C. M., Shin, D. W. and Williford, C. E. (2000b). Improving tropical precipitation forecasts from a multianalysis superensemble. J. Climate, 13, 4217–42272.0.CO;2>CrossRefGoogle Scholar
Krishnamurti, T. N., Surendran, S., Shin, D. W., et al.(2001). Real-time multianalysis-multimodel superensemble forecasts of precipitation using TRMM and SSM/I products, Mon. Weather Rev., 129, 2861–832.0.CO;2>CrossRefGoogle Scholar
Krishnamurti, T. N., Stefanova, L., Chakraborty, A., et al. (2002). Seasonal forecasts of precipitation anomalies for North American and Asian monsoons. J. Meteorol. Soc. Jpn., 80, 1415–26CrossRefGoogle Scholar
Krishnamurti, T. N., Rajendran, K., Kumar, T. S. V. Vijaya, et al. (2003a). Improved skill for the anomaly correlation of geopotential heights at 500 hPa. Mon. Weather Rev., 131, 1082–1022.0.CO;2>CrossRefGoogle Scholar
Krishnamurti, T. N., Kumar, T. S. V. Vijaya, Mackey, B. P., Cubukcu, N., , R. S. Ross and Adler, R.. (2003b). Current thrusts on TRMM and SSM/I based modeling studies on heavy rains and flooding episodes. Mausam, 54, 121–40Google Scholar
Kumar, T. S. V. V., Krishnamurti, T. N., , M. Fiorino and Nagata, M. (2003). Multimodel superensemble forecasting of tropical cyclones in the Pacific. Mon. Weather Rev., 131, 574–832.0.CO;2>CrossRefGoogle Scholar
Kummerow, C., Barnes, W., Kozu, T., Shiue, J. and Simpson, J. (1998). The tropical rainfall measuring mission (TRMM) sensor package. J. Atmos. Ocean., Tech., 15, 809–172.0.CO;2>CrossRefGoogle Scholar
Kummerow, C., Simpson, J., Thiele, O., et al. (2000). The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteorol., 39, 1965–82
Lee, H. S. (2000). Cloud Specification and Forecasts in the Florida State University Global Spectral Model. FSU Report No. 2000–1, January 2000. Available from Dept. of Meteorology, Florida State University, Tallahassee, Fl 32306–4520, USAGoogle Scholar
Mullen, S. L. and Baumhefner, D. P. (1994). Monte Carlo simulations of explosive cyclogenesis. Mon. Weather Rev., 122, 1548–672.0.CO;2>CrossRefGoogle Scholar
Olson, W. S., LaFontaine, F. J., , W. L. Smith and Achtor, T. H. (1990). Recommended algorithms for the retrieval of rainfall rates in the tropics using the SSM/I (DMSP-8). Manuscript, University of Wisconsin, Madison, WIGoogle Scholar
Olson, W. S., Kummerow, C., Heymsfiled, G. M. and , L. Giglio (1996). A method for combined passive-active microwave retrievals of cloud and precipitation profiles. J. Appl. Meteorol., 35, 1763–892.0.CO;2>CrossRefGoogle Scholar
Palmer, T. N., Molteni, F., Mureau, R., Buizza, R., , P. Chapelet and , J. Tribbia (1992). Ensemble prediction. ECMWF Technical Memorandum, 188
Palmer, T. N., Alessandri, A., Andersen, U., et al. (2004). Development of a European multi-model ensemble system for seasonal to inter-annual prediction (DEMETER). Bull. Am. Meteorol. Soc., 85, 853–4CrossRefGoogle Scholar
Powell, M. D. and Aberson, S. D. (2001). Accuracy of United States tropical cyclone landfall forecasts in the Atlantic Basin (1976–2000). Bull. Am. Meteorol. Soc., 82, 2749–682.3.CO;2>CrossRefGoogle Scholar
Roads, J. O., Chen, S-C., and , F. Fujioka (2001). ECPC's weekly to seasonal global forecasts. Bull. Am. Meteorol. Soc., 82, 639–582.3.CO;2>CrossRefGoogle Scholar
Ross, R. S. and Krishnamurti, T. N. (2005). Reduction of forecast error for global numerical weather prediction by the Florida State University (FSU) Superensemble. Meteorol. Atmos. Phys., 88, 215–35CrossRefGoogle Scholar
Stefanova, L. and Krishnamurti, T. N.. (2002). Interpretation of seasonal climate forecast using Brier skill score, FSU superensemble and the AMIP-I dataset. J. Climate, 15, 537–442.0.CO;2>CrossRefGoogle Scholar
Szymczak, H. (2004). Skill of synthetic superensemble hurricane forecasts for the Canadian maritime provinces. Masters Dissertation. Available from Department of Meteorology, Florida State University, Tallahassee, FL 32306, USA
Turk, J., Liou, C.-S., Qiu, S., Scofield, R., , M. Ba and , A. Gruber (2001). Capabilities and characteristics of rainfall estimates from geostationary and geostationary+microwave-based satellite techniques. In Symposium On Precipitation Extremes: Prediction, Impacts and Responses, Albuquerque, NM. Preprints, pp. 191–4. American Meteorological SocietyGoogle Scholar
Voldoire, A., , B. Timbal and Power, S. (2002). Statistical-dynamical seasonal forecasting. BMRC Research Report No. 87. BMRC Australia (Bureau of Met.)
Wang, G., Kleeman, R., Smith, N. and , F. Tseitkin (2002). The BMRC coupled GCM ENSO forecast system. Mon. Weather Rev., 130, 975–912.0.CO;2>CrossRefGoogle Scholar
Williford, C. E., Krishnamurti, T. N., Torres, R. J. C., Cocke, S., Christidis, Z. and Kumar, T. S. V. V. (2003). Real-time multimodel superensemble forecasts of Atlantic tropical systems of 1999. Mon. Weather Rev, 131, 1878–94CrossRefGoogle Scholar
Yun, W. T., Stefanova, L. and Krishnamurti, T. N. (2003). Improvement of the multimodel superensemble technique for seasonal forecasts. J. Climate, 16, 3834–402.0.CO;2>CrossRefGoogle Scholar
Yun, W. T., Stefanova, L., A. K. Mitra, T. S. V. V. Kumar, W. Dewar and , T. N. Krishnamurti (2005). Multi-model synthetic superensemble algorithm for seasonal climate prediction using DEMETER forecasts. Tellus, 57, 280–9CrossRef

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