Konrad Bogner

Trends in the predictive performance of raw ensemble weather forecasts

This study applies statistical postprocessing to ensemble forecasts of near-surface temperature, 24 h precipitation totals, and near-surface wind speed from the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the postprocessed forecasts. Reliability and sharpness, and hence skill, of the former is expected to improve over time. Thus, the gain by postprocessing is expected to decrease. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations, we generate postprocessed forecasts by ensemble model output statistics (EMOS) for each station and variable. Given the higher average skill of the postprocessed forecasts, we analyze the evolution of the difference in skill between raw ensemble and EMOS. This skill gap remains almost constant over time indicating that postprocessing will keep adding skill in the foreseeable future.

Quality control, validation and user feedback of the European Flood Alert System (EFAS)

The quality control, validation and verification of the European Flood Alert System (EFAS) are described. EFAS is designed as a flood early warning system at pan-European scale, to complement national systems and provide flood warnings more than 2 days before a flood. On average 20–30 alerts per year are sent out to the EFAS partner network which consists of 24 National hydrological authorities responsible for transnational river basins. Quality control of the system includes the evaluation of the hits, misses and false alarms, showing that EFAS has more than 50% of the time hits. Furthermore, the skills of both the meteorological as well as the hydrological forecasts are evaluated, and are included here for a 10-year period. Next, end-user needs and feedback are systematically analysed. Suggested improvements, such as real-time river discharge updating, are currently implemented.

Improving the evaluation of hydrological multi-model forecast performance in the upper danube catchment

Medium range flood forecasting activities, driven by various meteorological forecasts ranging from high resolution deterministic forecasts to low spatial resolution ensemble prediction systems, share a major challenge in the appropriateness and design of performance measures. In this paper possible limitations of some traditional hydrological and meteorological prediction quality and verification measures are identified. Some simple modifications are applied in order to circumvent the problem of the autocorrelation dominating river discharge time-series and in order to create a benchmark model enabling the decision makers to evaluate the forecast quality and the model quality. Although the performance period is quite short the advantage of a simple cost-loss function as a measure of forecast quality can be demonstrated.

Building a Multimodel Flood Prediction System with the TIGGE Archive

AbstractIn the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias corre...