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Seasonal probability of precipitation forecasts using a weighted ensemble approach

  1. D. W. Shin1,*,
  2. S.-D. Kang2,
  3. S. Cocke1,
  4. T.-Y. Goo3,
  5. H.-D. Kim4

Article first published online: 22 FEB 2008

DOI: 10.1002/joc.1690

Issue

International Journal of Climatology

International Journal of Climatology

Volume 28, Issue 14, pages 1971–1976, 30 November 2008

Additional Information

How to Cite

Shin, D. W., Kang, S.-D., Cocke, S., Goo, T.-Y. and Kim, H.-D. (2008), Seasonal probability of precipitation forecasts using a weighted ensemble approach. Int. J. Climatol., 28: 1971–1976. doi: 10.1002/joc.1690

Author Information

  1. 1

    Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA, 32306-2840

  2. 2

    Climate Prediction Division, Korea Meteorological Administration, Seoul, Republic of Korea, 156-720

  3. 3

    Korea Global Atmosphere Watch Center, Korea Meteorological Administration, Seoul, Republic of Korea, 156-720

  4. 4

    Department of Environmental Conservation, Keimyung University, Daegu, Republic of Korea, 704-701

*COAPS, Florida State University, Tallahassee, FL 32306-2840, USA.

Publication History

  1. Issue published online: 22 OCT 2008
  2. Article first published online: 22 FEB 2008
  3. Manuscript Accepted: 4 JAN 2008
  4. Manuscript Revised: 4 DEC 2007
  5. Manuscript Received: 1 MAY 2007

Funded by

  • Korea Research Foundation Grant MOEHRD, KRF-2006-C00696 NOAA Climate Program Office

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Keywords:

  • seasonal precipitation;
  • weighted ensemble;
  • probabilistic forecast;
  • multi-model

Abstract

A weighted ensemble (WE) method is revisited and employed to issue an improved seasonal probability of precipitation (POP) forecast. Nine boreal summer time seasonal precipitation hindcasts obtained from the APEC (Asia-Pacific Economic Cooperation) Climate Center (APCC) multi-model ensemble system are used to assess the suitability of the WE approach for seasonal POP predictions. Owing to its performance-based selective nature for assigning weights, the WE method produced marginally superior seasonal POP forecasts compared to the conventional approach. Copyright © 2008 Royal Meteorological Society

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