Ensemble precipitation down-selection methods using Continuous Ranked Probability Score (CRPS): Balancing accuracy and spread under computational constraints

        An ensemble down-selection method is proposed to improve the analysis and forecast with a small ensemble,  reducing computational needs. A usual problem with ensemble down-selection is that, despite of the reduction of forecast error, ensemble spread sharply decrease. To limit ensemble spread collapse, this study introduces two variations of a novel down-selection method seeking to minimize the sub-ensemble’s Continuous Ranked Probability Score (CRPS), thereby preserving ensemble spread while minimizing forecast error. The approaches are then tested with a regional-scale model whose precipitation forecast we seek to improve. The precipitation forecast performance of sub-ensembles obtained by these CRPS-based methods is evaluated against the full ensemble, and 100 randomly down-selected sets using various verification metrics measuring precipitation forecast skill. Results demonstrate that the CRPS-based sub-ensembles improve probabilistic forecast accuracy by achieving lower CRPS with the lowest Root Mean Square Error (RMSE) value, especially for short forecasts, without increasing false alarms. Additionally, the Brier Score shows improved forecasts, while Fraction Skill Score (FSS) confirms the improved spatial accuracy in light precipitation. These findings suggest that CRPS-based methods are viable sub-ensembling approaches for balancing accuracy, reliability, and computational efficiency in operational forecasting. By preserving ensemble spread, they improve the sub-ensemble's capacity to represent uncertainty, offering a practical and robust solution for ensemble down-selection.