Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms

• Published in: Remote sensing of environment Vol. 239; p. 111616
• Main Authors: Min, Min, Li, Jun, Wang, Fu, Liu, Zijing, Menzel, W. Paul
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 15.03.2020; Elsevier BV
• Subjects: Accuracy; Algorithms; artificial intelligence; CALIPSO; CALIPSO (Pathfinder satellite); climate; Climate and weather; Climatic data; Cloud top height; Clouds; Himawari-8; Learning algorithms; Lidar; Machine learning; Measuring instruments; meteorological data; Meteorological satellites; Passive satellites; remote sensing; Retrieval; Satellite imagery; Satellite instruments; Satellite observation; Satellites; Spatial discrimination; Spatial resolution; Spectral bands; Synchronous satellites; uncertainty; Weather; Cloud top height; Himawari-8; CALIPSO; Machine learning
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2019.111616

The cloud-top height (CTH) product derived from passive satellite instrument measurements is often used to make climate data records (CDR). CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) provides CTH parameters with high accuracy, but with limited temporal-spatial resolution. Recently, the Advanced Himawari Imager (AHI) onboard Japanese Himawari-8/-9, provides high temporal (every 10 min) and high spatial (2 km at nadir) resolution measurements with 16 spectral bands. This paper reports on a study to derive the CTH from combined AHI and CALIPSO using advanced machine learning (ML) algorithms with better accuracy than that from the traditional physical (TRA) algorithms. We find significant CTH improvements (1.54–2.72 km for mean absolute error, MAE) from four different machine learning algorithms (original MAE from TRA method is about 3.24 km based on CALIPSO data validation), particularly in high and optically thin clouds. In addition, we also develop a joint algorithm to combine optimal machine learning and traditional physical (TRA) algorithms of CTH to further reduce MAE to 1.53 km and enhance the layered accuracy (CTH < 18 km). While the ML-based algorithm improves CTH retrieval over the TRA algorithm, the lower or higher clouds still exhibit relatively large uncertainty. Combining both methods provides the better CTH than either alone. The combined approach could be used to process data from advanced geostationary imagers for climate and weather applications. •A novel machine learning algorithm to retrieve cloud top height using Himawari-8•Significant improvements in cloud top height product from machine learning algorithm•A joint algorithm further reduces uncertainty in cloud top height.