中央氣象局全球預報模式對不同BSISO相位下臺灣梅雨季降雨特徵之預報能力評析：以2016－2017年為例

Evaluation of CWB/GFS in Forecasting the Characteristics of Mei-yu Season Rainfall over Taiwan at Different Phases of Boreal Summer Intraseasonal Oscillations: Using 2016-2017 as Examples

劉品誼(Pin-Yi Liu) ； 黃婉如(Wan-Ru Huang) ； 張雅惠(Ya-Hui Chang) ； 黃柏翰(Po-Han Huang) ； 陳建河(Jen-Her Chen)

《大氣科學》 46卷4期 (2018/12) Pp. 372-405

https://doi.org/10.3966/025400022018124604002

摘要

本研究針對2016－2017年臺灣梅雨季（5、6月）降雨在北半球夏季季內振盪（Boreal Summer Intraseasonal Oscillation；簡稱BSISO）不同相位下的特徵進行討論。主要希望透過觀測資料與模式模擬結果的比對，了解中央氣象局全球預報模式（Central Weather Bureau Global Forecast System；簡稱CWB／GFS）對於兩種不同週期之BSISO（分別為振盪週期30～60天的BSISO1，以及振盪週期10～30天的BSISO2）現象下的東亞地區降雨、環流場變化以及對臺灣地區降雨特徵的掌握能力。有關模式方面，主要著重在預報領先時間（lead time）第1～15天（簡稱LT1～15）的資料分析。有關觀測資料方面，主要採用測站降雨、GPM（Global Precipitation Measurement）IMERG（Integrated Multi-satellite Retrieval for GPM）衛星降雨，以及NCEP（National Centers for Environmental Prediction）第二版再分析資料（Reanalysis 2）進行分析。藉由觀測資料分析的研究結果顯示，2016－2017年臺灣梅雨季降雨存在季內振盪變化週期的訊號，且降雨強度的變化特徵受到BSISO現象下東亞地區環流場變化的影響。其中，當BSISO1在相位7～1以及BSISO2在相位4～6時，受到大尺度低壓帶傳播至臺灣、華南及琉球一帶的影響，臺灣地區會有較強的降雨事件發生。比對CWB／GFS與觀測資料後發現，CWB／GFS大多能掌握臺灣地區強降雨事件（＞30mm／day）發生時間和BSISO相位的對應關係（即強降雨事件多發生在BSISO1相位7～1及BSISO2相位4～6）。其中，就空間相關係數（spatial correlation；簡稱Scorr）和均方根誤差（root mean square error；簡稱RMSE）的統計分析來看，以LT1～5的Scorr為最大、RMSE為最小（即預報表現最好），LT11～15的Scorr為最小、RMSE為最大（即預報表現最差），LT6～10的預報表現則介於兩者之間。而在定量上，CWB／GFS對臺灣地區的降雨預報結果，不論是LT1～5、LT6～10或LT11～15，則大多「低估強降雨事件的降雨強度」並「高估弱降雨事件的降雨強度」。進一步探究CWB／GFS能掌握臺灣地區強降雨事件發生時間和BSISO相位對應關係的原因，我們發現主要跟CWB／GFS能有效掌握BSISO現象下，臺灣附近環流場與降雨場變化的移動特徵有關。這些研究結果有助於瞭解CWB／GFS在臺灣降雨預報上的應用價值。另需說明的是，本研究為使用2016－2017兩年預報資料進行的先期研究（pilot study），這些研究結果是否適用於其他年份，仍待未來有更多的CWB／GFS資料可提供分析時，再進行相關驗證。

關鍵字

梅雨季 ； 北半球夏季季內振盪 ； 中央氣象局全球預報模式

並列摘要

By comparing the rainfall and circulation forecasted by Central Weather Bureau Global Forecast System (i.e. CWB/GFS) with the observational data over East Asia, this study evaluates the capability of CWB/GFS in forecasting the impact of two different types of boreal summer intraseasonal oscillations (named as BSISO1 with a 30-to-60-day oscillation period and BSISO2 with a 10-to-30-day oscillation period) on the Mei-yu season (May and June) rainfall over Taiwan during 2016-2017. For the model forecast, we focus on the analysis of lead times from day-1 to day-15 (denoted as LT1~15). For the observational data, we use rain gauge observation, GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellite Retrieval for GPM) precipitation data, and NCEP (National Centers for Environmental Prediction) Reanalysis version 2. Our analyses of observational data show that the characteristics of Mei-yu season rainfall in Taiwan during 2016-2017 are under the modulation of atmospheric circulation changes over East Asia related to BSISOs. In general, heavy rainfall events tend to occur over Taiwan at phase 7-1 of BSISO1 and at phase 4-6 of BSISO2, when the signal of large-scale convective zone propagates into the areas nearby Taiwan, Southeast China and Okinawa. By comparing these observational features with the features forecasted by CWB/GFS, it is noted that CWB/GFS can depict the relationship between the occurrence timing of heavy rainfall (＞30mm/day) in Taiwan and the phases of BSISOs (i.e. heavy rainfall mostly occurred at phase 7-1 of BSISO1 and at phase 4-6 of BSISO2). Also, based on the analysis of spatial correlation (Scorr) and root-mean-square-error (RMSE) between CWB/GFS and observation, we note that LT1~5 has the largest Scorr and smallest RMSE (i.e. the best forecasting performance), LT11~15 has the smallest Scorr and largest RMSE (i.e. the worst forecasting performance), whereas the forecasting performance of LT6~10 is between them. However, quantitatively, the CWB/GFS rainfall forecasts tend to ＂underestimate the heavy Mei-yu rainfall events＂ and ＂overestimate the weak Mei-yu rainfall events＂ in Taiwan; this feature is found in all of the forecast results of LT1~5, LT6~10 and LT11~15. Further examination on ＂Why CWB/GFS is able to capture the relationship between the occurrence timing of heavy rainfall in Taiwan and the phases of BSISOs?＂, our results show that is related to the good skill of CWB/GFS in forecasting the propagating features of rainfall and circulation over the areas nearby Taiwan, under the modulation of BSISOs. These results help a better understanding of the values of applying CWB/GFS in rainfall forecast over Taiwan. Notably, this is a pilot study, which uses 2016-2017 CWB/GFS forecast data as an example, to examine the capability of CWB/GFS in forecasting the rainfall changes in Taiwan under the modulation of BSISOs. Further research work is proposed to examine whether above results are suitable for other years when more CWB/GFS forecast data are available in the future.

並列關鍵字

Mei-yu season ； BSISO ； CWB/GFS