Preprints
https://doi.org/10.5194/hess-2022-67
https://doi.org/10.5194/hess-2022-67
 
03 Mar 2022
03 Mar 2022
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Sub-seasonal precipitation forecasts using preceding atmospheric intraseasonal oscillation signals in a Bayesian perspective

Yuan Li, Zhiyong Wu, Hai He, and Hao Yin Yuan Li et al.
  • College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

Abstract. Accurate and reliable sub-seasonal precipitation forecasts remain challenging. The atmospheric intraseasonal oscillation (ISO), which is one of the leading sources of sub-seasonal predictability, could be potentially used as predictors for sub-seasonal precipitation forecasts.. However, the relationships between ISO signals and sub-seasonal precipitation are of high uncertainty. In this study, we first define potential predictors by analyzing the relationship between preceding atmospheric ISO signals and precipitation for 17 hydroclimatic regions over China during the boreal summer monsoon season. The Least Absolute Shrinkage and Selection Operator (LASSO) and stepwise regression approaches are used to narrow down the number of potential predictors. A Bayesian hierarchical model is then established to predict sub-seasonal precipitation. The model performance is evaluated through a leave one-year-out cross-validation strategy for both deterministic and probabilistic forecasts. The results suggest that the statistical model we built in this study could provide skillful deterministic sub-seasonal precipitation forecasts over southeastern and southwestern hydroclimatic regions at a lead time of 20–25 days. However, the deterministic forecast skills are much lower over northeastern China, owing to the underestimation of intraseasonal variability in these regions. The probabilistic forecasts are more promising, and the results indicate that the Bayesian hierarchical model could provide skillful and reliable sub-seasonal precipitation forecasts for all hydroclimatic regions from 0-day to 25-day leads. Other sources of sub-seasonal predictability would be included in the future to further improve sub-seasonal precipitation forecast skills.

Yuan Li et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-67', Anonymous Referee #1, 28 Mar 2022
    • AC1: 'Reply on RC1', Zhiyong Wu, 12 Apr 2022
      • RC2: 'Reply on AC1', Anonymous Referee #1, 20 Apr 2022
        • AC3: 'Reply on RC2', Zhiyong Wu, 07 Jun 2022
  • RC3: 'Comment on hess-2022-67', Anonymous Referee #2, 11 May 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-67', Anonymous Referee #1, 28 Mar 2022
    • AC1: 'Reply on RC1', Zhiyong Wu, 12 Apr 2022
      • RC2: 'Reply on AC1', Anonymous Referee #1, 20 Apr 2022
        • AC3: 'Reply on RC2', Zhiyong Wu, 07 Jun 2022
  • RC3: 'Comment on hess-2022-67', Anonymous Referee #2, 11 May 2022

Yuan Li et al.

Yuan Li et al.

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Short summary
The atmospheric intraseasonal oscillation (ISO) could be potentially used as predictors for sub-seasonal precipitation forecasts. In this study, we define potential predictors by analyzing the relationship between preceding atmospheric ISO signals and precipitation. A Bayesian hierarchical model (BHM) is then established to generate probabilistic forecasts. The results suggest that the model we built in this study is skillful and reliable for sub-seasonal precipitation forecasts.