大气科学2025，Vol.49Issue(5)：1271-1283,13.DOI:10.3878/j.issn.1006-9895.2407.24057
基于CALIPSO数据的FY-3D/HIRAS云检测方法评估
Evaluation of Cloud Detection Method for FY-3D/HIRAS Based on CALIPSO Data
摘要
Abstract
Cloud detection is critical for applications of infrared high-spectral radiance observations as it directly impacts the effectiveness of satellite data utilization.McNally proposed a method in 2003 based on observed and simulated brightness temperature differences for channel cloud detection,widely applied in satellite data quality control for numerical weather forecasting.Building upon this method,this study utilized Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations(CALIPSO)cloud classification data products to quantitatively assess the cloud detection performance of FengYun 3D(FY-3D)High Spectral Infrared Atmospheric Sounder(HIRAS),using precision and recall as validation metrics.Through this assessment,the assimilated data volume of FY-3D HIRAS products was enhanced.Results revealed the following:(1)The precision of FY-3D/HIRAS channel cloud detection is 97.19%,with a recall of 93.74%.The root mean square error of the difference between the observed brightness temperature and background brightness temperature caused by false clear-sky channels(i.e.,cloud channels detected as clear sky)was 0.984 K,which was within observational error variance in numerical forecasting.Thus,the results confirm that the method does not compromise data quality and can be effectively applied to numerical weather forecasting.(2)The CALIPSO-based analysis of different cloud types showed that high precision but lower recall was achieved for stratus,stratocumulus,and fractured cumulus.In contrast,high precision and recall were achieved for altocumulus,altostratus,and deep convective clouds,and lower precision but higher recall was achieved for cirrus.关键词
红外高光谱大气探测仪(HIRAS)/云检测/精确度/召回率Key words
HIRAS(High Spectral Infrared Atmospheric Sounder)/Cloud detection/Precision/Recall分类
大气科学引用本文复制引用
田少龙,肖贤俊,徐忠燕,李玉鹏,平凡..基于CALIPSO数据的FY-3D/HIRAS云检测方法评估[J].大气科学,2025,49(5):1271-1283,13.基金项目
国家重点研发计划项目2023YFC3007700,浙江省自然科学基金合作项目LZJMD24D050001,浙江省科技计划项目2025C02028 National Key Research and Development Program(Grant 2023YFC3007700),Zhejiang Provincial Natural Science Foundation Cooperative Project(Grant LZJMD24D050001),Zhejiang Provincial Science and Technology Project(Grant 2025C02028) （Grant 2023YFC3007700）
