Abstract. This study analyzes the quality of the raw and post-processed
seasonal forecasts of the European Centre for Medium-Range Weather Forecasts
(ECMWF) System 4. The focus is given to Denmark, located in a region where
seasonal forecasting is of special difficulty. The extent to which there are
improvements after post-processing is investigated. We make use of two
techniques, namely linear scaling or delta change (LS) and quantile mapping (QM), to
daily bias correct seasonal ensemble predictions of hydrologically relevant
variables such as precipitation, temperature and reference evapotranspiration
(ET0). Qualities of importance in this study are the reduction of
bias and the improvement in accuracy and sharpness over ensemble climatology.
Statistical consistency and its improvement is also examined. Raw forecasts
exhibit biases in the mean that have a spatiotemporal variability more
pronounced for precipitation and temperature. This variability is more stable
for ET0 with a consistent positive bias. Accuracy is higher than
ensemble climatology for some months at the first month lead time only and,
in general, ECMWF System 4 forecasts tend to be sharper. ET0 also
exhibits an underdispersion issue, i.e., forecasts are narrower than their
true uncertainty level. After correction, reductions in the mean are seen.
This, however, is not enough to ensure an overall higher level of skill in
terms of accuracy, although modest improvements are seen for temperature and
ET0, mainly at the first month lead time. QM is better suited to
improve statistical consistency of forecasts that exhibit dispersion issues,
i.e., when forecasts are consistently overconfident. Furthermore, it also
enhances the accuracy of the monthly number of dry days to a higher extent
than LS. Caution is advised when applying a multiplicative factor to bias
correct variables such as precipitation. It may overestimate the ability that
LS has in improving sharpness when a positive bias in the mean exists.
Quality of Dry Desalted Lees of Soy-Sauce from Whole Soy-Beans