Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies
| Autor: | Joël Gailhard, Maria-Helena Ramos, Rémy Garçon, I. Zalachori, Thibault Mathevet |
|---|---|
| Přispěvatelé: | Hydrosystèmes et bioprocédés (UR HBAN), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), EDF - Division Technique Générale (DTG), EDF (EDF), Hydrosystèmes et Bioprocédés (UR HBAN) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FLOW FORECASTING
Atmospheric Science 010504 meteorology & atmospheric sciences Meteorology BIAS CORRECTION 0207 environmental engineering MODELE STATISTIQUE 02 engineering and technology Forcing (mathematics) lcsh:QC851-999 01 natural sciences Physics::Geophysics ENSEMBLE PREDICTION Streamflow PREVISION HYDROLOGIQUE Bias correction Precipitation lcsh:Science 020701 environmental engineering Statistical processing Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences Ensemble forecasting PREVISION METEOROLOGIQUE Ecological Modeling Pollution lcsh:QC1-999 PREVISION DE DEBIT Geophysics 13. Climate action Climatology Ensemble prediction [SDE]Environmental Sciences Environmental science lcsh:Q lcsh:Meteorology. Climatology Consensus forecast lcsh:Physics |
| Zdroj: | Advances in Science & Research Advances in Science & Research, 2012, 8, p. 135-p. 141. ⟨10.5194/asr-8-135-2012⟩ Advances in Science and Research, Vol 8, Pp 135-141 (2012) |
| ISSN: | 1992-0636 |
| DOI: | 10.5194/asr-8-135-2012⟩ |
| Popis: | The aim of this paper is to investigate the use of statistical correction techniques in hydrological ensemble prediction. Ensemble weather forecasts (precipitation and temperature) are used as forcing variables to a hydrologic forecasting model for the production of ensemble streamflow forecasts. The impact of different bias correction strategies on the quality of the forecasts is examined. The performance of the system is evaluated when statistical processing is applied: to precipitation and temperature forecasts only (pre-processing from the hydrological model point of view), to flow forecasts (post-processing) and to both. The pre-processing technique combines precipitation ensemble predictions with an analog forecasting approach, while the post-processing is based on past errors of the hydrological model when simulating streamflows. Forecasts from 11 catchments in France are evaluated. Results illustrate the importance of taking into account hydrological uncertainties to improve the quality of operational streamflow forecasts. |
| Databáze: | OpenAIRE |
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