Journal article
Distributions-Oriented Verification of Ensemble Streamflow Predictions
Journal of hydrometeorology, Vol.5(3), pp.532-545
06/2004
DOI: 10.1175/1525-7541(2004)005<0532:DVOESP>2.0.CO;2
Abstract
Ensemble streamflow prediction systems produce forecasts in the form of a conditional probability distribution for a continuous forecast variable. A distributions-oriented approach is presented for verification of these probability distribution forecasts. First, a flow threshold is used to transform the ensemble forecast into a probability forecast for a dichotomous event. The event is said to occur if the observed flow is less than or equal to the threshold; the probability forecast is the probability that the event occurs. The distributions-oriented approach, which has been developed for meteorological forecast verification, is then applied to estimate forecast quality measures for a verification dataset. The results are summarized for thresholds chosen to cover the range of possible flow outcomes. To aid in the comparison for different thresholds, relative measures are used to assess forecast quality. An application with experimental forecasts for the Des Moines River basin illustrates the approach. The application demonstrates the added insights on forecast quality gained through this approach, as compared to more traditional ensemble verification approaches. By examining aspects of forecast quality over the range of possible flow outcomes, the distributions-oriented approach facilitates a diagnostic evaluation of ensemble forecasting systems.
Details
- Title: Subtitle
- Distributions-Oriented Verification of Ensemble Streamflow Predictions
- Creators
- A. Allen BradleyStuart S SchwartzTempei Hashino
- Resource Type
- Journal article
- Publication Details
- Journal of hydrometeorology, Vol.5(3), pp.532-545
- DOI
- 10.1175/1525-7541(2004)005<0532:DVOESP>2.0.CO;2
- ISSN
- 1525-755X
- eISSN
- 1525-7541
- Language
- English
- Date published
- 06/2004
- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9983992041502771
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