Abstract 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

中文翻译：

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基于机器学习算法从先进的地球静止卫星成像仪测量中检索云顶属性

摘要 来自无源卫星仪器测量的云顶高度（CTH）产品通常用于制作气候数据记录（CDR）。CALIPSO（云气溶胶激光雷达和红外探路者卫星观测）提供高精度的 CTH 参数，但时空分辨率有限。最近，日本 Himawari-8/-9 上的 Advanced Himawari Imager (AHI) 提供了具有 16 个光谱带的高时间（每 10 分钟）和高空间（天底 2 公里）分辨率测量。本文报告了一项研究，该研究使用先进的机器学习 (ML) 算法从组合的 AHI 和 CALIPSO 中推导出 CTH，其精度比传统物理 (TRA) 算法的精度更高。我们发现显着的 CTH 改进（平均绝对误差为 1.54-2.72 公里，MAE）来自四种不同的机器学习算法（TRA 方法的原始 MAE 基于 CALIPSO 数据验证约为 3.24 公里），特别是在高云和光学薄云中。此外，我们还开发了一种联合算法，将最优机器学习和 CTH 的传统物理 (TRA) 算法相结合，以进一步将 MAE 降低到 1.53 km，并提高分层精度 (CTH