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Category: postprocessing

If you had to build a probabilistic streamflow forecasting chain from scratch, what components would you pick up?

If you had to build a probabilistic streamflow forecasting chain from scratch, what components would you pick up?

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Contributed by Joseph Bellier Take a meteorological ensemble, use it as input of a hydrological model, but then what? There are many ways to improve a forecasting chain, but which upgrade is going to be the most beneficial? We recently published a paper in which we “play” with a modular forecasting chain, by adding/removing various components and verifying how skillful the streamflow forecasts are. Here are some outputs. The ensemble approach, in its wider definition (not only meteorological ensemble forecasting),…

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What is the value of seasonal forecasts for water resources management?

What is the value of seasonal forecasts for water resources management?

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Contributed by Andres Peñuela, Christopher Hutton and Francesca Pianosi Improved skill of predictions for the North Atlantic circulation and Northern Europe have motivated an increasing effort to improve seasonal weather forecasting systems. Seasonal weather forecasts are expected to be useful for a range of purposes, including to improve the management of water resource systems. To contribute to the assessment of seasonal forecasts value for water managers, we ran a simulation exercise investigating how they could integrate seasonal forecast products in…

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Recent development of post-processing methods in short-term hydrometeorological ensemble forecasting

Recent development of post-processing methods in short-term hydrometeorological ensemble forecasting

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Contributed by Wentao Li and Qingyun Duan  Due to various uncertainties in model inputs and outputs, initial and boundary conditions, model structures and parameters, raw forecasts from meteorological or hydrological models suffer from systematic bias and under/overdispersion errors and they need to be corrected before being used in applications. Various statistical post-processing methods have been developed to correct these errors and achieve “sharp” forecasts subject to “reliability”. As in the book “Statistical methods in the atmospheric sciences” by Wilks, statistical…

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How suitable is quantile mapping for post-processing GCM precipitation forecasts?

How suitable is quantile mapping for post-processing GCM precipitation forecasts?

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Contributed by QJ Wang, University of Melbourne* Back in September 2015 at the highly successful HEPEX Seasonal Hydrological Forecasting Workshop at SMHI in Norrkoping, Sweden, I heard a number of presentations and saw posters on the use of quantile mapping for post-processing or downscaling GCM precipitation forecasts. While quantile mapping was well known to be highly effective in bias correction, I was concerned that some of its limitations might not have been apparent to some people. After discussing with Andy…

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Pre-, post-processing or both?

Pre-, post-processing or both?

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by Marie-Amélie Boucher, a HEPEX 2015 Guest Columnist Do you think it is better to pre-process the meteorological forecasts, to post-process the hydrological forecasts or to do both? Why? Following this blog about future directions for post-processing research, this challenge was mentioned in a comment by James Brown: « Putting aside the choice of technique, I think there are some more fundamental questions about how to use hydrologic post-processing operationally. For example, under what circumstances does it make sense to separate between…

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