Abstract
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.
Highlights
Seasonal forecasting has gained increasing attention during the last three decades due to high societal impacts of extreme meteorological events that affect a plethora of weatherrelated sectors such as agriculture, environment, health, transport and energy, and tourism (Dessai and Soares, 2013)
For the given example here, the reference forecast is wider than the European Centre for Medium-Range Weather Forecast (ECMWF) System 4 forecast
Raw temperature predictions from ECMWF System 4 improve, on average, on the reference forecast by 22 % in terms of accuracy
Summary
Seasonal forecasting has gained increasing attention during the last three decades due to high societal impacts of extreme meteorological events that affect a plethora of weatherrelated sectors such as agriculture, environment, health, transport and energy, and tourism (Dessai and Soares, 2013). The main one, and specific to forecasting in Europe and North America, is that the signal of the main driver of seasonal predictability, the ENSO, has been found to be weak or nonexistent (Molteni et al, 2011; Saha et al, 2013) in these regions, leading to poor skill of atmospheric variables such as precipitation. We analyze the bias, skill and statistical consistency of the ECMWF System 4 for Denmark focusing on precipitation, temperature and ET0 of relevance for seasonal streamflow forecasting at the catchment scale. We argue that the forecast to be used must be better than climatology, having a higher skill both in terms of accuracy and sharpness and giving priority to the former These characteristics for an “acceptable forecast” follow the principle that the purpose of post-processing is to maximize sharpness subject to statistical consistency as discussed by Gneiting et al (2007).
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