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- W4387343387 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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." @default.
- W4387343387 created "2023-10-05" @default.
- W4387343387 creator A5073503222 @default.
- W4387343387 date "2023-10-04" @default.
- W4387343387 modified "2023-10-06" @default.
- W4387343387 title "Reply on RC1" @default.
- W4387343387 doi "https://doi.org/10.5194/hess-2023-111-ac1" @default.
- W4387343387 hasPublicationYear "2023" @default.
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