The Deep-Learning-Based Fast Efficient Nighttime Retrieval of Thermodynamic Phase From Himawari-8 AHI Measurements

Retrieval of the cloud thermodynamic phase (CP) is essential for satellite remote sensing and downstream applications. However, there is still a lack of efficient nighttime CP data products. A transfer-learning-based deep learning model, transfer-learning-ResUnet, is proposed to retrieve the nighttime CP of Himawari-8 from thermal infrared channels. Cloud products of Himawari-8 and Moderate-resolution Imaging Spectroradiometers were selected as labels during training. A benchmark obtained by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations confirmed the accuracy of the CP retrieval. During three independent months, the daytime and nighttime retrieval accuracy of the CP was 0.867 and 0.816, respectively, which was superior to that of the Himawari-8 operational product in the daytime (0.788).

Automated summary

The authors propose a transfer-learning-based deep learning model, transfer-learning-ResUnet, to retrieve the nighttime thermodynamic phase (CP) of clouds from thermal infrared channels. Cloud products of Himawari-8 and Moderate-resolution Imaging Spectroradiometers were used as labels during training. The accuracy of the CP retrieval was confirmed by a benchmark obtained by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations. During three independent months, the daytime and nighttime retrieval accuracy of the CP was 0.867 and 0.816, respectively, which was superior to that of the Himawari-8 operational product in the daytime (0.788).