该文利用国家卫星气象中心1998-2008年NOAA卫星的存档数据,在再定标和精定位等数据再处理基础上,利用自行研发的云检测算法及云量计算方法,生成空间分辨率为0.01°×0.01°、时间尺度为10年的逐日云量数据,并利用ISCCP和地面观测数据对计算得到的云参数进行数据质量评估.评估结果显示:利用NOAA数据的抽样云检测结果与ISCCP-DX数据相比,晴空像元检测率具有0.70左右的一致性;有云像元检测率具有60%左右的一致性.卫星计算的总云量与地面观测总云量间的月平均相关系数大于0.70.%Cloud plays an important role in earth-atmosphere radiation balance system, atmospheric circulation and climate change. Surface observation is a regular method to obtain cloud amount data but it is limited by time and place. International Satellite Cloud Climatology Project (lSCCP) offers cloud parameters product with better quality, but the best spatial resolution is just 30 km. Based on re-calibration and accurate re-location to NOAA daily data during 1998-2008 , total cloud amount are calculated with improved cloud detection and radiation calculation method, and validated by ISCCP and surface regular observation data. The temporal and spatial resolution (daily and 0.01°× 0.01°) of this cloud amount data is much better than ISCCP product. The sub cloud pixel covered problem is also resolved. Compared with ISCCP DX cloud detection data, validation result shows that clear pixel detection consistence reaches 0.70, cloud pixel detection consistence reaches 0. 60, and total cloud detection consistence reaches 0. 57. For cloud amount, the coefficient between the calculated cloud amount and surface observation is higher than 0.70. The main differences between cloud amount of ISCCP and calculated data come from two aspects.First, lSCCP method doesn't consider sub-pixel problem reasonably. If one pixel is covered by cloud, ISCCP method regards its cloud amount as one while with the radiation calculation method, clear and completely cloudy cover radiation is calculated, and then every pixel cloud amount according to its radiation value is calculated. Second, different spatial resolution and targets influence the evaluation of the two sets of data. Limited by observation angles and time, ground and satellite observations are not the same. The validation shows that the calculated long time series cloud parameters with high temporal and spatial resolution have good quality, and could play important role in weather analysis and climate change research.
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