Object-based precipitation system bias in grey zone simulation: the 2016 South China Sea summer monsoon onset

  • Chun-Yian Su
  • Chien-Ming WuEmail author
  • Wei-Ting Chen
  • Jen-Her Chen


This study aims to evaluate the precipitation bias in the grey zone simulation (~ 15 km) using the Central Weather Bureau Global Forecast System (CWBGFS). We develop a new evaluation method using the object-based precipitation system (OPS) to examine the bias associated with the degree of convection organization. The 2016 South China Sea (SCS) Summer Monsoon onset is selected to evaluate the model’s performance due to its sharp transition of large-scale circulation, which contributes to the complexity of precipitation pattern. The results based on OPS show that the observed precipitation tends to aggregate toward the central part of SCS during the post-onset period, while the precipitation in the model distributes more sparsely over the ocean. The observed precipitation intensity increases with the size of OPS especially for the extremes; however, the model underrepresents the relationship between the precipitation spectrum and the size of OPS. Moreover, the model simulates earlier diurnal peak time of precipitation over land in the organized systems than observation. The results also suggest that the convection scheme is insensitive to column moisture during the pre-onset period which seems to be one of the key factors to the excessive precipitation in the model. Using high horizontal resolution, however, does not improve the simulation of precipitation much in the model. The current study suggests that the precipitation bias related to aggregation of the convective systems should be regarded as an essential objective of model evaluation and improvement.


Object-based precipitation system Precipitation bias Grey zone simulation Degree of convection organization South China Sea summer monsoon onset 



This study is jointly supported by the Central Weather Bureau in Taiwan (1052281C, 1062221C), and the Ministry of Science and Technology in Taiwan (MOST-106-2111-M-002-005, MOST-106-2111-M-002-008, MOST-106-1502-02-11-01). We acknowledge the Central Weather Bureau providing the computational resources for this work. The IMERG V04 data were provided by the NASA/Goddard Space Flight Center and PPS, which develop and compute the IMERG V04 as a contribution to GPM, and archived at YYYY/MM/DD/imerg/ (accessed at 30 March 2017). The NCEP GDAS/FNL0.25 data were provided by the Computational and Information Systems Laboratory at the National Center for Atmospheric Research, and archived at datasets/ds083.3/ (accessed at 29 October 2017).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Atmospheric SciencesNational Taiwan UniversityTaipeiTaiwan, Republic of China
  2. 2.Central Weather BureauTaipeiTaiwan, Republic of China

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