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    Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

    Source: Journal of Hydrometeorology:;2019:;volume 020:;issue 007::page 1379
    Author:
    Han, Shasha
    ,
    Coulibaly, Paulin
    DOI: 10.1175/JHM-D-18-0251.1
    Publisher: American Meteorological Society
    Abstract: AbstractRecent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes?s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.
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      Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4263884
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    contributor authorHan, Shasha
    contributor authorCoulibaly, Paulin
    date accessioned2019-10-05T06:56:11Z
    date available2019-10-05T06:56:11Z
    date copyright5/17/2019 12:00:00 AM
    date issued2019
    identifier otherJHM-D-18-0251.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4263884
    description abstractAbstractRecent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes?s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.
    publisherAmerican Meteorological Society
    titleProbabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts
    typeJournal Paper
    journal volume20
    journal issue7
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-18-0251.1
    journal fristpage1379
    journal lastpage1398
    treeJournal of Hydrometeorology:;2019:;volume 020:;issue 007
    contenttypeFulltext
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