Yan Dong

The United States geostationary operational environmental satellites GOES-16 and GOES-17 provide cloud-top height (CTH) data. A fusion model combining their CTH is vital for global high-resolution weather forecasting and climate analysis. However, ensuring a high degree of consistency between CTH from GOES-16 and GOES-17 is essential for building an effective CTH fusion model. In this paper, a consistency analysis was conducted on the CTH in regions where GOES-16/ABI and GOES-17/ABI observations overlap. Results show that the CTH of GOES-16 and GOES-17 exhibits a small mean error (ME) and high correlation, but between 9:30 and 15:00 (UTC), the ME, standard deviation (Std), and root mean square error (RMSE) are significantly larger than those during other periods, with the maximum values occurring between approximately 12:00 and 14:00. The difference in Satellite viewing zenith angle (ΔVZA) at the same observation pixel had an impact on the CTH consistency. The larger ΔVZA is, the larger ME, Std, and RMSE are; The increase in ME of between the two CTH from 9:30 to 15:00 was related to the difference in radiance of the 12.3μm and 13.3μm channel, while the increase in Std was related to the difference in radiance of the 13.3μm. It is due to anomalies in the channel radiance observations caused by the degradation of the imager's performance by solar radiation exposure during this period. The fusion of GOES-16 and GOES-17 CTH data needs to take into consideration the differences in VZA and the data quality control between 9:30 and 15:00.

Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari Imagers) on the Himawari-8 satellite are a new generation of visible and infrared imaging spectrometers characterized by a wide observation range and a high temporal resolution. In this paper, a cloud-top height retrieval algorithm based on XGBoost is proposed. The algorithm comprehensively utilizes AHI L1 multi-channel radiance data and calculates the input parameters of the generated model according to the characteristics of the cloud phase, texture, and the local brightness temperature change of the cloud. In addition, the latitude, longitude, solar zenith angle and satellite zenith angle are input into the model to further constrain the influence of the geographical and spatial factors such as the sea and land location, on CTH. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud-top height data (CTH_CAL), the results show that: the algorithm retrieved the cloud-top height (CTH_XGB) with a mean error (ME) of 0.3 km, a standard deviation (Std) of 1.72 km, and a root mean square error (RMSE) of 1.74 km. Additionally, it improves the problem of the large systematic deviation in the cloud-top height products released by the Japan Meteorological Agency (CTH_JMA), especially for ice clouds and multi-layer clouds with ice clouds on the top layer. For water clouds below 2 km and multi-layer clouds with water clouds at the top, the algorithm solves the systematically serious CTH_JMA problem. XGBoost can effectively distinguish between different cloud scenarios within the model, which is robust and suitable for CTH retrieval.