Post-processing and Forecasts Updating in Automated Systems of Flash Flood Forecasting

V. A. Kuzmin and A. Zaman

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.