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Category: postprocessing

Introducing CBaM for post-processing GCM seasonal climate forecasts

Introducing CBaM for post-processing GCM seasonal climate forecasts

Contributed by QJ Wang, Andrew Schepen and David Robertson The CBaM (calibration, bridging and merging) method aims to make the best use of General circulation model (GCM) outputs to produce the most skillful and reliable forecasts for operational applications. Calibration is to overcome the problem that raw GCM forecasts are generally biased and unreliable in ensemble spread. In CBaM, Calibration models are established using a Bayesian joint probability (BJP) approach (presented here: Wang and Robertson 2011; Wang et al. 2009)…

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HEPEX-SIP Topic: Post-processing (3/3)

HEPEX-SIP Topic: Post-processing (3/3)

Contributed by Nathalie Voisin, Jan Verkade and Maria-Helena Ramos So, what are the current challenges and research needs in post-processing?  At the HEPEX meetings and workshops, several challenges related to the use of statistical post-processors in hydrological ensemble prediction were identified: How to select suitable / best predictors to make an efficient use of prior knowledge and information available at the moment of forecasting? ‘Stationarity is dead; whither postprocessing?’ How can existing postprocessors adapt their modeling approach to non-stationarities in…

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HEPEX-SIP Topic: Post-processing (2/3)

HEPEX-SIP Topic: Post-processing (2/3)

Contributed by Maria-Helena Ramos, Nathalie Voisin and Jan Verkade What can we find about post-processors for hydrological prediction in the literature? A small review to be completed by you! In hydrologic uncertainty analysis, the Bayesian framework prevails to analytically derive the joint distribution of forecasts and observations. Based on existing prior knowledge and likelihood functions, new data is used to update this prior knowledge and provide a conditional posterior distribution, which summarizes the uncertainty about the variable of interest (i.e.,…

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HEPEX-SIP Topic: Post-processing (1/3)

HEPEX-SIP Topic: Post-processing (1/3)

Contributed by Jan Verkade, Maria-Helena Ramos and Nathalie Voisin Before summarizing achievements and challenges for this topic, let’s first discuss what is behind the word ‘post-processing’ (as we understand it!) Hydrological ensemble forecasts are characterized by uncertainties originating from multiple sources: observed data, weather forecasts, model parameterization and structure, initial conditions, etc. This may result in forecasts being biased in either mean (under/over estimation) or spread (under/over dispersion), or in both. Statistical processing of model outputs can correct for or…

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Intercomparison of streamflow post-processors Post-Processing hydrologic model simulations (Phase 1)

Intercomparison of streamflow post-processors Post-Processing hydrologic model simulations (Phase 1)

Contributed by James Brown, Nathalie Voisin and Maria-Helena Ramos The experiment was launched in June 2012 and the first results are currently being anlaysed. See our poster presented at EGU 2013: Posters HS4.3/AS4.20/NH1.13 – Ensemble hydro-meteorological forecasting for improved risk management: across scales and applications Our aims is: To establish the advantages and disadvantages of different post-processing techniques and strategies when accounting for hydrologic uncertainty To foster ongoing collaboration among the HEPEX participants on the topic of streamflow post-processing and…

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