地面降水乃是水文循環中的重要一項環，其乃肇因於舉凡降水強度、降水位置及延續時間等時序變化，若於發生於災害潛勢區域，不同的降水型態則會造成程度不一的災害。而分析地面降水除了可透過陸地上的雨量站、雨滴譜儀等單點式觀測，及整合資料後成為二維平面資料外，亦可依靠地基雷達的觀測反演而推估得到陸地及海上的降水情形。然水文循環中，降水的形成及過程，雲扮演不可或缺的一項因子，雲微物理性質的演變及大氣熱力、動力型態，均會左右降水強度、降水位置等。本本子計畫的研究將是基於過往已完成的衛星觀測反演基礎研究中，透過分析地球同步衛星所得之多項雲微物理性質，除了將進行雲頂垂直運動估算、雲區逸出及逸入率估算，也將使用地面雨滴譜儀及雨量站的連續觀測資料，分析成雲致雨的過程及其演變，釐清降水雲與非降水雲的條件，對流性降水與層狀降水的雲微物理特徵。於計畫第三年，規劃使用資料同化方式，將地球同步衛星所觀測之多頻道亮溫（以向日葵八號為例，每10分鐘可有16個可見光至紅外線頻道之2公里解析度觀測）及其雲微物理參數，透過同化方式與數值模式結合，進行地面降水估算驗證與改進預報。 ;Precipitation is one of the important processes within the hydrological cycle. The evolution of precipitation intensity, location and duration will lead the various impacts when it falls on the high-risk potential region. The traditional study was based on the rain gauge and/or disdrometer observation, and ground-based radar to obtain the precipitation information over land and ocean. However, the cloud microphysical parameters and associated atmospheric thermodynamic state affects the cloud formation and leading to the precipitation afterward. The formation and evolution of both cloud and precipitation are lack of investigation from the space-borne platform in the past. In this project, we propose to use the continuous observation from state-of-the-art satellite in the geostationary orbit, Himawari-8 Advanced Himawari Imager (AHI), and those retrieved cloud microphysical parameters to investigate the cloud-precipitation processes. AHI has the full disk observation at the spatiotemporal resolutions of 2 km and 10 minutes. We expect to estimate, but not limited to, the cloud-top vertical velocity, entrainment rate, and will collocate with ground-based observation to identify the mechanism between precipitating and non-precipitating clouds, convective verses stratiform precipitation characteristics. In the third year of project, we also propose to use data assimilation technique to investigate the use of multi-channel brightness temperatures from AHI and cloud parameters in NWP model so that precipitation estimation will be evaluated and anticipate the improved forecast skills.
