Post-processing and forecasts updating in automated systems of flash flood forecasting

This paper completes the series of three articles devoted to automated forecasting of flash floods 3, 5] and describes an effective approach of forecast updating through post-processing operations, which can be useful only in conjunction with such fast and efficient real-time re-calibration algorithms as SLS-based methods are. In particular, a proposed methodology is aimed to reduce negative consequences of scarce or low-quality data that can corrupt optimized parameters and, therefore, lower forecasting efficiency. A new modification of SLS-based optimization that supposes simultaneous re-calibration of the model and correction of the model input by generating of ensemble noises (SLS-E) is presented.