In this report, we systematically evaluated the possible relationship between "the capability of Climate Forecast System version 2 (i.e. CFSv2) in predicting the boreal summer intraseasonal oscillations (i.e. BSISOs)" and "the capability of CFSv2 in predicting the occurrence of Meiyu season rainfall in Taiwan and related heavy rainfall events". It is noted from the research results of first two years of the project that using day as the lead time is more suitable for illustrating the capability of CFSv2 in predicting the rainfall in Taiwan. Thus, in this report, we mainly evaluate the performance of CFSv2 at lead time from day 1 to day 45 (hereafter LT1~LT45). Two issues are focused in this report: (1) For the Meiyu seasons of 1999~2016, the capability of CFSv2 at LT1~45 in predicting the changes of circulation and rainfall over East Asia (including Taiwan) under the modulation of BSISOs is evaluated. (2) For the 2017 Meiyu season, the capability of CFSv2 at LT1~45 in predicting the changes of rainfall over Taiwan under the modulation of BSISOs is evaluated. For issue (1), our results show that CFSv2 can have a better forecast skill in depicting the changes of circulation than the changes of rainfall during 1999~2016 Meiyu seasons. As for the changes of rainfall intensity and rainfall frequency over Taiwan under the modulation of BSISOs, the forecast skill of CFSv2 at LT1~10 is better than the forecast skill at LT11~45. The forecast results in LT11~45 obviously underestimated the rainfall intensity over Taiwan, and the model bias is increased with the forecast lead times. On the other hand, by comparing the skill of CFSv2 in predicting the changes of rainfall under the modulations of BSISO1 and BSISO2, it is noted that the skill of CFSv2 is better in predicting the related BSISO1 features than in predicting the related BSISO2 features. For issue (2), our analyses results show that at LT1~15, CFSv2 can depict the 10-20-day rainfall oscillation in 2017 Taiwan Meiyu season. The CFSv2 also has good skill at LT1~10 in predicting the relationship between the occurrence timing of heavy rainfall events and the phase of BSISOs during the 2017 Meiyu season. For the forecast skill of CFSv2 in predicting the changes of East Asia rainfall in 2017 Meiyu season that related to BSISOs, the forecast skill is better in predicting BSISO2 than predicting BSISO1. Overall, no matter the analyses are made for the 1999~2016 Meiyu seasons or the 2017 Meiyu season, the results all show that CFSv2 can have a better forecast skill in predicting "the relationship between the rainfall intensity, rainfall frequency over Taiwan and the occurrence phases of BSISO" at LT1~10. While for the rainfall prediction over Taiwan at LT11~45, the skill of CFSv2 is still needed to be improved. These findings provide useful information for understanding the capability of CFSv2 in predicting the Meiyu rainfall formation in Taiwan.
|Effective start/end date||2018/08/01 → 2019/10/31|
- Meiyu season；Climate；CFSv2；Forecast
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