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...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2016-05-01
|
Series: | Proceedings of the International Association of Hydrological Sciences |
Online Access: | https://www.proc-iahs.net/373/109/2016/piahs-373-109-2016.pdf |
Summary: | 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. |
---|---|
ISSN: | 2199-8981 2199-899X |