- 1Department of Atmospheric and Oceanic Sciences & Institutes of Atmospheric Sciences, Fudan University, Shanghai, China
- 2College of Future Information Technology, Fudan University, Shanghai, China
- 3Engineering Research Center of Optical Instrument and System, the Ministry of Education, University of Shanghai for Science and Technology, Shanghai, China
- 4School of Atmospheric Sciences, Nanjing University, Nanjing, China
- 5Shanghai Meteorological Service, Shanghai, China
- 6Weather Modification Center, China Meteorological Administration, Beijing, China
Clouds play a crucial role in the global water cycle and the balance of the energy budget. The unique topographic and thermal conditions of the Tibetan Plateau (TP) have a profound impact on the formation of regional extreme weather and global climate change. However, existing official cloud products from geostationary satellites suffer from the spatiotemporal discontinuity over the TP.
Therefore, this study develops an all‑day retrieval algorithm of cloud physical properties (CPP) from geostationary satellite measurements using a deep learning model, achieving high-precision retrieval of cloud phase (CLP), cloud top height (CTH), cloud effective radius (CER), and cloud optical thickness (COT). This algorithm not only leverages the spatial structural information of clouds to compensate for the limitations of retrieving thick clouds from thermal infrared channels caused by their weak penetration ability, but also effectively combines the observed advantages of geostationary satellites with a wide coverage and polar-orbiting satellites with high precision.
Based on the retrieved CPP products with spatiotemporal continuity, we further adopted a Tracking Of Organized Convection Algorithm through a three-dimensional segmentatioN (TOOCAN-CPP) method to automatically identify and track the deep convection system (DCS) over the TP and its surrounding areas. The results show that, influenced by the South Asian Summer Monsoon and topographic conditions, DCSs are primarily concentrated in the Southern TP, the Southern Himalayas Front, and the Ganges Plain. The diurnal variation of DCS number follows a unimodal pattern, with a phase difference of approximately 2 hours between the two areas. Additionally, diurnal variation in cloud properties of DCSs and their internal regions is revealed for the first time. Quantitative analysis of the DCS properties with different sizes and lifetimes indicates that these two areas are dominated by small-sized DCS with initial DCS lifetimes under 6 hours. These discoveries provide valuable insights into understanding the development and evolution of DCSs and their climatic effects.
How to cite: Zhao, Z., Zhang, F., Li, W., Yang, B., He, Q., and Cai, M.: Retrieval of All-Day Cloud Physical Properties from Geostationary Satellite Measurements and Its Application to the Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3669, https://doi.org/10.5194/egusphere-egu26-3669, 2026.
