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Category: postprocessing

Interactions between data assimilation and post-processing

Interactions between data assimilation and post-processing

I have recently contributed to a paper where we investigate how statistical post-processing and data assimilation (also called real-time model updating in the engineering community) can be intrinsically related in the hydrological forecasting framework. The paper, co-written with François Bourgin (main author), Guillaume Thirel, and Vazken Andréassian, can be found here. We were basically guided by the following questions: How does data assimilation impact hydrological ensemble forecasts? How does post-processing impact hydrological ensemble forecasts? How does data assimilation interact with…

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HEPEX Webinar: Statistical post-processing of ensemble weather forecasts: current development and future directions

HEPEX Webinar: Statistical post-processing of ensemble weather forecasts: current development and future directions

This webinar is co-hosted together with ECMWF and will be longer than usual, approximately 1 hour. Otherwise the procedure is as usual. Speaker: Tilmann Gneiting, University of Heidelberg Date and time: Wednesday, February 11, 2015 15:30 (UTC) Link to the recording here  Abstract: Statistical post-processing techniques serve to improve the quality of numerical weather forecasts, as they seek to generate calibrated and sharp predictive distributions of future weather quantities and events. I will review the state of the art in post-processing, with focus on ensemble forecasts and ongoing…

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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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