资源环境科技发展态势分析平台

Inland waterway transport benefits from probabilistic forecasts of water levels as they allow to optimize the ship load and, hence, to minimize the transport costs. Probabilistic state-of-the-art hydrologic ensemble forecasts inherit biases and dispersion errors from the atmospheric ensemble forecasts they are driven with. The use of statistical postprocessing techniques like ensemble model output statistics (EMOS) allows for a reduction of these systematic errors by fitting a statistical model based on training data. In this study, training periods for EMOS are selected based on forecast analogs, i.e., historical forecasts that are similar to the forecast to be verified. Due to the strong autocorrelation of water levels, forecast analogs have to be selected based on entire forecast hydrographs in order to guarantee similar hydrograph shapes. Custom-tailored measures of similarity for forecast hydrographs comprise hydrological series distance (SD), the hydrological matching algorithm (HMA), and dynamic time warping (DTW). Verification against observations reveals that EMOS forecasts for water level at three gauges along the river Rhine with training periods selected based on SD, HMA, and DTW compare favorably with reference EMOS forecasts, which are based on either seasonal training periods or on training periods obtained by dividing the hydrological forecast trajectories into runoff regimes.

Plain Language Summary Shipping companies need accurate forecasts of water levels to define the optimal loading of their ships. As the future is always uncertain, such predictions should be probabilistic, that is, they should assign probabilities to different scenarios of future water levels. Typically, probabilistic forecasts of water levels are distorted. They tend to systematically forecast too high or too low water levels. Moreover, their spread is generally not correct. This leads to undesirable differences between the forecast probabilities for particular scenarios and the observed frequencies of these scenarios. We apply statistical methods that are able to correct these issues. Such methods need to be fed with historical data, the training data, of forecast water levels and corresponding observations in order to fit them to a particular forecast problem. In this paper we assess different methods to select the training data. The standard approach to select the training data is to just take historical data from the same season but other years. However, in this paper we show that the forecasts can be improved by selecting the training data based on similarities between current and historical forecasts.