Special Issue Paper
Tree structure generation from ensemble forecasts for real time control
Version of Record online: 26 SEP 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Special Issue: Hydrological Ensemble Prediction Systems (HEPS)
Volume 27, Issue 1, pages 75–82, 1 January 2013
How to Cite
Raso, L., van de Giesen, N., Stive, P., Schwanenberg, D. and van Overloop, P. J. (2013), Tree structure generation from ensemble forecasts for real time control. Hydrol. Process., 27: 75–82. doi: 10.1002/hyp.9473
- Issue online: 21 DEC 2012
- Version of Record online: 26 SEP 2012
- Accepted manuscript online: 17 AUG 2012 09:16PM EST
- Manuscript Accepted: 2 JUL 2012
- Manuscript Received: 2 NOV 2011
- multistage stochastic programming;
- ensemble forecasts;
- tree structure;
- real time control;
- optimal control
This paper presents a new methodology to generate a tree from an ensemble. The reason to generate a tree is to use the ensemble in multistage stochastic programming. A correct tree structure is of critical importance because it strongly affects the performance of the optimization. A tree, in contrast to an ensemble, specifies when its trajectories diverge from each other.
A tree can be generated from the ensemble data by aggregating trajectories over time until the difference between them becomes such that they can no longer be assumed to be similar, at such a point, the tree branches.
The proposed method models the information flow: it takes into account which observations will become available, at which moment, and their level of uncertainty, i.e. their probability distributions (pdf). No conditions are imposed on those distributions.
The method is well suited to trajectories that are close to each other at the beginning of the forecasting horizon and spread out going on in time, as ensemble forecasts typically are. Copyright © 2012 John Wiley & Sons, Ltd.