Meteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables. Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures. The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts. Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium‐Range Weather Forecasts. This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step. In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting.
In the verification field, stratification is the process of dividing the sample of forecast–observation pairs into quasi-homogeneous subsets, in order to learn more on how forecasts behave under specific conditions. A general framework for stratification is presented for the case of ensemble forecasts of continuous scalar variables. Distinction is made between forecast-based, observation-based, and external-based stratification, depending on the criterion on which the sample is stratified. The formalism is applied to two widely used verification measures: the continuous ranked probability score (CRPS) and the rank histogram. For both, new graphical representations that synthesize the added information are proposed. Based on the definition of calibration, it is shown that the rank histogram should be used within a forecast-based stratification, while an observation-based stratification leads to significantly nonflat histograms for calibrated forecasts. Nevertheless, as previous studies have warned, statistical artifacts created by a forecast-based stratification may still occur, thus a graphical test to detect them is suggested. To illustrate potential insights about forecast behavior that can be gained from stratification, a numerical example with two different datasets of mean areal precipitation forecasts is presented.
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