Title: Statistical post-processing of water level forecasts
Authors: Sandor Baran - Faculty of Informatics, University of Debrecen (Hungary) [presenting]
Stephan Hemri - MeteoSwiss (Switzerland)
Mehrez El Ayari - University of Debrecen (Hungary)
Abstract: Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state-of-the-art hydrological ensemble prediction models are usually driven with meteorological ensemble forecasts. Hence, biases and dispersion errors of the meteorological forecasts cascade down to the hydrological predictions and add to the errors of the hydrological models. The systematic parts of these errors can be reduced by applying statistical post-processing. For a sound estimation of predictive uncertainty and an optimal correction of systematic errors, statistical post-processing methods should be tailored to the particular forecast variable at hand. Former studies have shown that it can make sense to treat hydrological quantities as bounded variables. For flexible post-processing of multi-model ensemble forecasts of water level a doubly truncated Bayesian model averaging (BMA) method is introduced, which generalizes the truncated normal BMA model for wind speed calibration. BMA weights and model parameters are estimated with the help of the EM algorithm for truncated normal mixtures. A case study based on water level data for gauge Kaub of river Rhine reveals a good predictive skill of doubly truncated BMA compared both with the raw ensemble and the reference ensemble model output statistics approach.