Publications

Zhao, ZJ; Zhang, F; Li, WW; Li, JW (2024). Image-Based Retrieval of All-Day Cloud Physical Parameters for FY4A/AGRI and Its Application Over the Tibetan Plateau. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 129(18), e2024JD041032.

Abstract
Satellite remote sensing serves as a crucial means to acquire cloud physical parameters. However, existing official cloud products from the advanced geostationary radiation imager (AGRI) onboard the Fengyun-4A geostationary satellite lack spatiotemporal continuity and important micro-physical properties. In this study, an image-based transfer learning ResUnet (TL-ResUnet) model was applied to realize all-day and high-precision retrieval of cloud physical parameters from AGRI thermal infrared measurements. Combining the observation advantages of geostationary and polar-orbiting satellites, the TL-ResUnet model was pre-trained with official cloud products from advanced Himawari imager (AHI) and transfer-trained with official cloud products from moderate resolution imaging spectroradiometer (MODIS), respectively. For comparison, a pixel-based transfer learning random forest (TL-RF) model was trained using the equally distributed data sets. Taking MODIS official products as the benchmarks, the TL-ResUnet model achieved an overall accuracy of 79.82% for identifying cloud phase and root mean squared errors of 1.99 km, 7.11 mu m, and 12.87 for estimating cloud top height, cloud effective radius, and cloud optical thickness, outperforming the precision of AGRI and AHI official products. Compared to the TL-RF model, the TL-ResUnet model utilized the spatial information of clouds to significantly improve the retrieval performance and achieve more than a 6-fold increase in speed for single full-disk retrieval. Moreover, AGRI TL-ResUnet products with spatiotemporal continuity and high precision were used to accurately describe the spatial distribution characteristics of cloud fractions and cloud properties over the Tibetan Plateau, and provide the diurnal variation of cloud cover and cloud properties across different seasons for the first time. Accurately acquiring cloud physical parameters is crucial for estimating the shortwave and longwave radiation and advancing climate change research. However, existing official cloud products from geostationary satellites have limitations in spatiotemporal continuity. This study used an image-based transfer learning ResUnet model to retrieve all-day and high-precision cloud physical parameters for the advanced geostationary radiation imager (AGRI) onboard the Fengyun-4A geostationary satellite. This model fully combines the observation advantages of geostationary and polar-orbiting satellites, and shows stable performance in all-day and seasonal cycles. Additionally, the retrieved cloud products for AGRI accurately described the spatiotemporal distribution characteristics of cloud physical parameters over the Tibetan Plateau for the first time. An image-based transfer learning ResUnet model was used to retrieve all-day cloud physical parameters for FY4A/AGRI The model has stable performance in diurnal and seasonal cycles, with higher precision of cloud products over AHI and AGRI official products The spatial distribution and diurnal variation of cloud physical parameters over the Tibetan Plateau is described for the first time

DOI:
10.1029/2024JD041032

ISSN:
2169-8996