| dc.description.abstract | 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. | en_US |