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

Li, Yuan; Xü, Kangning; Wu, Zhiyong; Zhu, Zhiwei; Wang, Quan J.

doi:https://doi.org/10.5194/hess-27-4187-2023

Articles | Volume 27, issue 22

https://doi.org/10.5194/hess-27-4187-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-27-4187-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 27, issue 22

Research article

|

21 Nov 2023

Research article |

| 21 Nov 2023

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

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

Supplement

https://doi.org/10.5194/hess-27-4187-2023-supplement

Data sets

NOAA's Outgoing Longwave Radiation - Daily Climate Data Record (OLR - Daily CDR): PSL Interpolated Version data NOAA https://psl.noaa.gov/thredds/catalog/Datasets/olrcdr/catalog.html

Short summary

A spatial–temporal projection-based calibration, bridging, and merging (STP-CBaM) method is proposed. 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 a Bayesian modelling averaging (BMA) method. The results indicate that the newly developed method can generate skilful and reliable sub-seasonal precipitation forecasts.