Improvements of the Sub-Seasonal Precipitation Predicted by the BCC_S2S Forecast System Over Eastern China in Summer Using the Singular Value Decomposition Bias Correction Method

Zhipeng Fu,Qu Guo,Anning Huang

doi:10.3389/feart.2021.770167

Zhipeng Fu, Qu Guo + Show 1 more

Open Access

https://doi.org/10.3389/feart.2021.770167

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Abstract

A bias correction scheme has been developed based on the singular value decomposition (SVD) analysis in this study, and it is further validated and used to improve the skill of sub-seasonal precipitation over Eastern China in summer predicted by the Beijing Climate Center sub-seasonal to seasonal forecast system (BCC_S2SFS). Results show that the BCC_S2SFS prediction skill of summer precipitation over Eastern China at the sub-seasonal scale is up to 1 days in advance and exhibits clear regional and inter-annual differences. Further adopting the bias correction scheme can significantly improve the skill of BCC_S2SFS in predicting the sub-seasonal precipitation over Eastern China in summer with different lead time especially longer than 10 days. Compared to the original prediction of BCC_S2SFS, the temporal (spatial) correlation coefficient between the bias corrected predictions and observations over Eastern China can be increased by 0.15, 0.55, and 0.56 (0.14, 0.17, and 0.19) during the forecast lead time of 0–10, 11–20, and 21–30 days, respectively. The bias correction scheme developed in this study shows large potential application prospects in the operational forecast.

Highlights

Sub-seasonal to seasonal (S2S) meteorological forecasts (15–60 days) take the key time range for linking short-term weather forecast to seasonal climate prediction (Jie et al, 2017)
The prediction skill of BCC_S2SFS shows exceptionally large inter-annual fluctuation with the temporal correlation coefficient (TCC) ranging from −0.05 to 0.77
A bias correction scheme based on singular value decomposition (SVD) analysis has been developed to correct the BCC_S2SFS predictions and thereafter further improve the prediction skill of the summer precipitation over Eastern China at the sub-seasonal scale

Summary

Introduction

Sub-seasonal to seasonal (S2S) meteorological forecasts (15–60 days) take the key time range for linking short-term weather forecast to seasonal climate prediction (Jie et al, 2017). The meteorological prediction with the S2S time range has long been considered a “desert of predictability” (Vitart, 2017; Olaniyan et al, 2018). It is urgent to improve the accuracy of the S2S forecast to meet the increasing demand of reliable meteorological services. With the increase in extreme weather events (e.g., continuous rain, drought, and snowstorm), the meteorological departments were expected to make more accurate predictions on the dynamics and duration of these events. The S2S forecast has strong guiding and prompting effects on power dispatching, fishery production, agricultural development, and other industries closely related to weather and climate conditions (Tang et al, 2017)

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