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Calculation and Validation Method of Cloud Amount by High Spatial Resolution Satellite Data
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摘要: 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。Abstract: 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 (ISCCP) 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, ISCCP 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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Key words:
- satellite data;
- cloud amount;
- high spatial resolution;
- long time series
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图 1 自行计算的1998年卫星云检测一致率 (a)、有云像元检测一致率 (b)、晴空像元检测一致率 (c) 和有云像元检测评分 (d) 的时间分布
Fig. 1 The temperal distribution of calculated cloud detection accuracy (a), cloud detection hit rate (b), clear detection hit rate (c), Threat Score (d) in 1998
图 2 自行计算的2007年卫星云检测一致率 (a)、有云像元检测一致率 (b)、晴空像元检测一致率 (c) 和有云像元检测评分 (d) 的时间分布
Fig. 2 The temporal distribution of calculated cloud detection accuracy (a), cloud detection hit rate (b), clear detection hit rate (c) and Threat Score (d) in 2007
图 3 自行计算的1998年卫星云检测结果与ISCCP DX云检测对比评估指标分析
Fig. 3 Evaluation parameters for calculated cloud detection and ISCCP DX data in 1998
图 4 自行计算的2007年卫星云检测结果与ISCCP DX云检测对比评估指标分析
Fig. 4 Evaluation parameters for calculated cloud detection and ISCCP DX data in 2007
图 5 NOAA反演的总云量与地面观测总云量间的CCA相关系数随时间变化
Fig. 5 The temporal distrubution of CCA relation coefficients of retrieval cloud amount and ground observation
图 6 2007年7月12日可见光云图 (a)、地面观测总云量 (b) 与利用NOAA/卫星数据计算总云量 (c) 的对比
Fig. 6 Comparison of visible image (a), ground observation (b) and retrieval cloud amount of NOAA (c) on 12 July 2007
图 7 完全晴空 (a)、完全云盖 (b) 亮温变化引起的估计云量变化
Fig. 7 The relationship of the brightness temperature bias about clear (a) and cloudy (b) to retrieval cloud amount
表 1 NOAA数据与ISCCP数据云检测联立表
Table 1 Contingency table for NOAA and ISCCP cloud detection
NOAA云像元 NOAA晴空像元 ISCCP云像元 A B ISCCP晴空像元 C D 
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表 2 地面观测与卫星计算总云量全国范围内逐日平均总云量的偏差分析
Table 2 Difference analysis between retrieval cloud amount and ground observation
月份 有效数据天数/d 平均偏差值/% 偏差的标准方差 1 31 4.46 0.06 2 28 3.64 0.04 3 28 6.91 0.04 4 30 5.54 0.04 5 27 7.47 0.05 6 30 15.13 0.03 7 31 16.03 0.02 8 31 14.92 0.03 9 30 11.13 0.05 10 29 7.17 0.06 11 29 0.80 0.03 12 31 3.33 0.06
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