25 Jul 2023
 | 25 Jul 2023
Status: this preprint is currently under review for the journal HESS.

A statistical-dynamical approach for probabilistic prediction of sub-seasonal precipitation anomalies over 17 hydroclimatic regions in China

Yuan Li, Zhiyong Wu, Zhiwei Zhu, and Quan J. Wang

Abstract. Skillful and reliable sub-seasonal precipitation forecasts are of great social and economic value. In this study, we develop a Spatial Temporal Projection based Calibration, Bridging, and Merging (STP-CBaM) method to improve probabilistic sub-seasonal precipitation forecast skill by combining the strengths of both dynamical models and statistical models. The calibration model is established by post-processing ECMWF raw forecasts using the Bayesian Joint Probability (BJP) approach. The bridging models are built using large-scale atmospheric intraseasonal predictors (U200, U850, OLRA, H200, H500, and H850) defined by the Spatial-Temporal Projection method (STPM). The calibration model and bridging models are then merged through the Bayesian Modeling Averaging (BMA) method. Our results indicate that the forecast skill of calibration model is higher compared to bridging models when the lead time is within 5–10 days. The U200 and OLRA-based bridging models outperform the calibration model in certain months and certain regions. The BMA merged forecasts take advantage of both calibration model and bridging models. The forecast skill is further improved compared to the calibration model and bridging models, especially at longer lead times. Meanwhile, the BMA merged forecasts also show high reliability for all regions, months, and lead times. These findings demonstrate the great potential of combing dynamical models and statistical models in improving sub-seasonal precipitation forecasts.

Yuan Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-111', Anonymous Referee #1, 23 Aug 2023
  • RC2: 'Comment on hess-2023-111', Anonymous Referee #2, 12 Sep 2023

Yuan Li et al.


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Short summary
A Spatial Temporal Projection based Calibration, Bridging, and Merging (STP-CBaM) method is proposed in this study. The calibration model is built by post-processing ECMWF raw forecasts, while the bridging models are built using atmospheric intraseasonal signals as predictors. The calibration model and bridging models are merged through the BMA method. The results indicate that the newly developed method can generate skillful and reliable sub-seasonal precipitation forecasts.