On the skill of raw and postprocessed ensemble seasonal meteorological forecasts in Denmark

Abstract

Abstract. This study analyzes the quality of the raw and postprocessed seasonal forecasts of the European Center of Medium 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 postprocessing is investigated. We 10 make use of two techniques, namely, linear scaling/delta change (LS) and quantile mapping (QM) to daily bias correct seasonal ensemble predictions of hydrological relevant variables such as precipitation (P), temperature (T) and reference evapotranspiration (E T0 ). 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 spatio-temporal variability more pronounced for P and T. This variability is more stable for 15 E T0 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. E T0 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 T and E T0 , mainly at the first month lead time. QM is better suited to improve statistical consistency of forecasts that exhibit 20 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 P. It may overestimate the ability that LS has in improving sharpness when a positive bias in the mean exists.

Subseasonal hydrometeorological ensemble predictions in small-and medium-size mountainous catchments: Benefits of the NWP approach

“…In contrast to the present analysis, our earlier study used a station wise post-processing 30 of the raw forecasts using the same setup as in the present study. Similarly, different studies emphasize the benefit of preprocessing precipitation (Crochemore et al, 2016) and temperature forecasts (Lucatero et al, 2017) at catchments at various Hydrol. Earth Syst.…”

Potential of seasonal hydrological forecasting of monthly run-off volumes for the Rhone and Arve Rivers from April to July

“…The main problem faced by traditional ESP is the assumption that the measured climatic data are representative of the climate during the forecasted period (Lucatero et al, 2017a(Lucatero et al, , 2017bMendoza et al, 2017). Recently, ensembles of 10 meteorological forcing have tended to be reduced to relevant years or are modified by weighing (Crochemore et al, 2016(Crochemore et al, , 2017).…”