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Verification of Short-Range Hydrological Forecasts

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Handbook of Hydrometeorological Ensemble Forecasting

Abstract

For the mitigation of floods and flashfloods, operational nowcast and forecast systems are crucial. This chapter provides practical illustrations of the verification of hydrological ensemble prediction systems with a temporal horizon of up to 5 days.

Section 2 shows the application of two ensemble approaches for discharge nowcasts. The results show that both ensemble approaches have added value compared to deterministic nowcasts.

Section 3 presents the evaluation of an operational flood forecasting system. The system is run with the two deterministic COSMO-2 and COSMO-7 weather forecasts and with the probabilistic COSMO-LEPS weather forecast. The evaluation with several skill scores suggests that decisions that need to be taken with a lead time of 1 day and more should be based on the ensemble forecast.

Ensemble forecasts can be difficult to interpret. Section 4 provides a helpful tool for the estimation of flood peak timing and magnitude based on probabilistic forecasts.

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Authors and Affiliations

  1. Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

    Katharina Liechti & Massimiliano Zappa

Authors
  1. Katharina Liechti
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  2. Massimiliano Zappa
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Corresponding author

Correspondence to Katharina Liechti .

Editor information

Editors and Affiliations

  1. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Qingyun Duan

  2. European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, UK

    Florian Pappenberger

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

    Andy Wood

  4. Department of Meteorology, Reading University, Reading, UK

    Hannah L. Cloke

  5. U.S. National Weather Service (retired), Annapolis, MD, USA

    John C. Schaake

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Liechti, K., Zappa, M. (2019). Verification of Short-Range Hydrological Forecasts. In: Duan, Q., Pappenberger, F., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39925-1_5

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