Study on the Objective Improvement of Optimal Threshold Selection Algorithm Based on ECMWF Ensemble Model Precipitation Forecasts

To address the limitation where the traditional Optimal Threshold Selection (OTS) scheme achieves a high Threat Score (TS) at the expense of an increased False Alarm Rate (FAR), this study develops an Objective Improvement of Optimal Threshold Selection (OIOTS) scheme based on the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble precipitation forecasts. The correction performance is verified using high-resolution observations during the post-flood season in Guangdong Province. The results indicate that (1) optimal quantiles exhibit significant spatial heterogeneity and decrease sharply with increasing precipitation intensity, confirming the necessity of grid-specific correction over uniform provincial thresholds. (2) The Optimal Precipitation (OP) threshold remains stable across different lead times but shows distinct regional characteristics influenced by topography, whereas the corresponding Probability Threshold (PT) demonstrates a downward trend as the lead time extends. (3) Verification reveals that, compared with the OTS scheme, the OIOTS scheme effectively rectifies the high FAR inherent in the optimal quantile method while maintaining a comparable TS. By minimizing the absolute difference between TS and FAR, the OIOTS scheme achieves a superior balance between detection accuracy and error suppression, with its FAR showing a significant downward trend as precipitation magnitude and lead time increase. Given its high computational efficiency and robust performance, the proposed scheme offers a reliable solution for operational meteorological forecasting.