| 摘要: | 雲覆蓋全球表面約70%，其消長過程與微物理特性變化，對地球能量收支和水文循環有著重大影響。由於過往研究對雲的特性及其發展過程認知不足，迫使雲迄今為止皆為氣候變化預測模型中最大的不確定性因子。過往研究經常使用繞極軌道衛星作為觀測雲的儀器，然而其有限的時間解析度，無法提供連續性的觀測資料，並充分監測短時天氣系統。因此，本研究嘗試利用具有高時間解析度之地球同步衛星，作為分析工具。
本研究以台灣和南海為研究目標，並特別針對夏季時段，乃因該區域坐落於東印度洋暖池中心及亞洲季風之水氣通道，具有多樣性地貌及複雜的天氣氣候尺度。透過搭載於向日葵八號衛星之儀器-AHI於2017年至2019年夏季(6月至8月)白天(00UTC至08UTC)反演之雲產品，包含雲出現頻率(COF)、雲頂氣壓(CTP)、雲光學厚度(COF)和雲滴有效粒徑(RE)，以及EAR5再分析資料提供的相對濕度(RH)、垂直速度(VV)、空氣溫度(T)及對流可用未能(CAPE)之大氣變量估計值，探討兩者之間的時空分布狀態。研究結果顯示，台灣及南海的低雲、高雲和深對流雲出現頻率，在03UTC至05UTC達最大值，且同時伴隨著COT的增長、相對濕度增加，以及上升氣流的增強。此外，由於台灣地貌複雜，不同雲型出現頻率也有明顯的區域差異，而南海則沒有明顯的空間變化。;Clouds account for 70% of global coverage, which life cycle and the changes of microphysical properties affect the energy budget and hydrological cycle on Earth. Due to insufficient understanding of the characteristics of clouds and their development process in previous studies, clouds have been the largest uncertainty factor in climate change forecast models so far. Previous studies often used polar-orbiting satellites as the instruments to observe cloud information, but the limitation of temporal resolution cannot provide continuous observation dataset and adequately monitor short-term weather systems. Therefore, this study attempts to use geostationary satellites with high temporal resolution as an analysis instrument. The study areas are Taiwan and South China Sea, which is located in the warm pool center of East Indian Ocean and water vapor path of Asia monsoon, with the various landform and complicated weather and climate scale, especially during summer period. By using the Himawari-8 satellite data and the atmospheric variable estimates provided by ERA5 reanalysis data, to analyze the relationship between cloud occurrence frequency, cloud top pressure, cloud optical thickness, cloud effective radius, and the environmental factors including relative humidity, vertical velocity, air temperature, and convective available potential energy during 2017 to 2019 summer season (June to August), daytime (00UTC to 08UTC). The results show that the occurrence frequency of low cloud, high cloud, and deep convective cloud reached the maximum value during 03 to 05UTC, with increasing COT, RH, and updraft. In addition, due to the complex topography of TW, there are obvious regional differences in frequency and cloud types, while there is no obvious spatial change over SCS. |
