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Handbook of Hydrometeorological Ensemble Forecasting pp 1–27Cite as

Seasonal Ensemble Forecast Post-processing

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Abstract

In many parts of the world, water resources systems manage sub-seasonal to seasonal (S2S) variability in climate and runoff in part through the use of operational streamflow forecasts, supplemented by predictions of climate and other hydrologic variables. S2S hydrologic forecasts are produced through both statistical and dynamical (model-based) approaches, and separate S2S forecasts may be combined in multi-model frameworks to increase their skill. Statistical post-processing can be used to enhance the skill and reliability of model-based S2S predictions, and to reduce bias, as well as to merge forecasts from multiple approaches. This chapter describes seasonal hydrologic forecast approaches and products, and presents common techniques used in both the post-processing of single ensemble forecast series as well as the combination of multiple forecasts. Also discussed are the sources of S2S hydrological predictability and particular challenges and opportunities related to post-processing seasonal hydrologic predictions, for which the sample sizes of past simulations, observations and predictions are relatively more limited than in the context of short to medium range prediction.

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Authors and Affiliations

  1. National Center for Atmospheric Research, Boulder, CO, USA

    Andrew W. Wood

  2. Department of Civil Construction and Environmental Engineering, North Carolina State University, Raleigh, NC, USA

    A. Sankarasubramanian

  3. Advanced Mining Technology Center (AMTC), Universidad de Chile, Santiago de Chile, Chile

    Pablo Mendoza

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  1. Andrew W. Wood
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  2. A. Sankarasubramanian
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  3. Pablo Mendoza
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Correspondence to Andrew W. Wood .

Editor information

Editors and Affiliations

  1. Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

    Florian Pappenberger

  3. European Commission DG Joint Research Ce, Ispra, Italy

    Jutta Thielen

  4. National Center for Atmospheric Research, Boulder, Colorado, USA

    Andy Wood

  5. University of Reading, Reading, Berkshire, United Kingdom

    Hannah L. Cloke

  6. Consulting Hydrologist, Annapolis, USA

    John C. Schaake

Section Editor information

  1. National Center for Atmospheric Research, Boulder, USA

    Andy Wood Scientist

  2. Research Applications Laboratory, National Center for Atmospheric Research, Colorado, USA

    Thomas Hopson Scientist

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Wood, A.W., Sankarasubramanian, A., Mendoza, P. (2018). Seasonal Ensemble Forecast Post-processing. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_37-1

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  1. Latest

    Seasonal Ensemble Forecast Post-processing
    Published:
    12 December 2018

    DOI: https://doi.org/10.1007/978-3-642-40457-3_37-2

  2. Original

    Seasonal Ensemble Forecast Post-processing
    Published:
    06 April 2018

    DOI: https://doi.org/10.1007/978-3-642-40457-3_37-1

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