Regionalization of post-processed ensemble runoff forecasts
For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compare...
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doaj-7080b6c8af8e45cd863449977cd495f22020-11-24T23:15:29ZengCopernicus PublicationsProceedings of the International Association of Hydrological Sciences2199-89812199-899X2016-05-0137310911410.5194/piahs-373-109-2016Regionalization of post-processed ensemble runoff forecastsJ. O. Skøien0K. Bogner1P. Salamon2P. Smith3F. Pappenberger4European Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability, Ispra, 21027 (VA), ItalySwiss Federal Institute WSL, Mountain Hydrology and Mass Movements, Birmensdorf, 8903, SwitzerlandEuropean Commission, Joint Research Centre (JRC), Institute for Environment and Sustainability, Ispra, 21027 (VA), ItalyEuropean Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UKEuropean Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UKFor many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (<a href="http://www.efas.eu" target="_blank">http://www.efas.eu</a>), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.https://www.proc-iahs.net/373/109/2016/piahs-373-109-2016.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
J. O. Skøien K. Bogner P. Salamon P. Smith F. Pappenberger |
spellingShingle |
J. O. Skøien K. Bogner P. Salamon P. Smith F. Pappenberger Regionalization of post-processed ensemble runoff forecasts Proceedings of the International Association of Hydrological Sciences |
author_facet |
J. O. Skøien K. Bogner P. Salamon P. Smith F. Pappenberger |
author_sort |
J. O. Skøien |
title |
Regionalization of post-processed ensemble runoff forecasts |
title_short |
Regionalization of post-processed ensemble runoff forecasts |
title_full |
Regionalization of post-processed ensemble runoff forecasts |
title_fullStr |
Regionalization of post-processed ensemble runoff forecasts |
title_full_unstemmed |
Regionalization of post-processed ensemble runoff forecasts |
title_sort |
regionalization of post-processed ensemble runoff forecasts |
publisher |
Copernicus Publications |
series |
Proceedings of the International Association of Hydrological Sciences |
issn |
2199-8981 2199-899X |
publishDate |
2016-05-01 |
description |
For many years, meteorological models have been run with perturbated initial
conditions or parameters to produce ensemble forecasts that are used as a
proxy of the uncertainty of the forecasts. However, the ensembles are usually
both biased (the mean is systematically too high or too low, compared with
the observed weather), and has dispersion errors (the ensemble variance
indicates a too low or too high confidence in the forecast, compared with the
observed weather). The ensembles are therefore commonly post-processed to
correct for these shortcomings. Here we look at one of these techniques,
referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al.,
2005). Originally, the post-processing parameters were identified as a fixed
set of parameters for a region. The application of our work is the European
Flood Awareness System (<a href="http://www.efas.eu" target="_blank">http://www.efas.eu</a>), where a distributed model
is run with meteorological ensembles as input. We are therefore dealing with
a considerably larger data set than previous analyses. We also want to
regionalize the parameters themselves for other locations than the
calibration gauges. The post-processing parameters are therefore estimated
for each calibration station, but with a spatial penalty for deviations from
neighbouring stations, depending on the expected semivariance between the
calibration catchment and these stations. The estimated post-processed
parameters can then be used for regionalization of the postprocessing
parameters also for uncalibrated locations using top-kriging in the
rtop-package (Skøien et al., 2006, 2014). We will show results from
cross-validation of the methodology and although our interest is mainly in
identifying exceedance probabilities for certain return levels, we will also
show how the rtop package can be used for creating a set of post-processed
ensembles through simulations. |
url |
https://www.proc-iahs.net/373/109/2016/piahs-373-109-2016.pdf |
work_keys_str_mv |
AT joskøien regionalizationofpostprocessedensemblerunoffforecasts AT kbogner regionalizationofpostprocessedensemblerunoffforecasts AT psalamon regionalizationofpostprocessedensemblerunoffforecasts AT psmith regionalizationofpostprocessedensemblerunoffforecasts AT fpappenberger regionalizationofpostprocessedensemblerunoffforecasts |
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