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

Author:

Tong Xuan12ORCID,Li Jingwei2,Zhang Feng12ORCID,Li Wenwen1,Pan BaoXiang23ORCID,Li Jun4ORCID,Letu Husi5ORCID

Affiliation:

1. Key Laboratory of Polar Atmosphere‐Ocean‐Ice System for Weather and Climate, Ministry of Education, Department of Atmospheric and Oceanic Sciences Fudan University Shanghai China

2. Shanghai Qi Zhi Institute Shanghai China

3. Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

4. National Satellite Meteorological Center China Meteorological Administration Beijing China

5. State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

Abstract

AbstractRetrieval 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).

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

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