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Assessment on Main Kinds of Satellite Cloud Climate Datasets
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摘要: 自20世纪70年代气象卫星进入业务化观测以来，气象卫星已提供了40余年的观测数据。长时间序列的卫星数据为云气候研究提供了可能。基于长时间序列的卫星数据，构建云气候数据集会涉及诸如定标、反演算法、反演数据精度验证等方面。目前国际上也已生成了一系列的云气候数据集，如ISCCP，Patmos-x，CLARA和MODIS-ST等，这些数据集所选用的探测数据、反演算法不尽一致，数据集产品的时空属性各异。如何发挥极轨和静止气象卫星各自优势，融合两类卫星数据，形成高时间分辨率、质量稳定的长时间序列云气候数据集是未来需要解决的问题。Abstract: Cloud is not only a key parameter that affects the radiation balance between earth and atmosphere system, but also is an important index to research atmosphere cycle and climate change. Cloud information can be achieved by surface observation, but it is limited by the spatial and temporal distribution of stations. Only satellite observations can provide a continuous synoptic survey of the state of the atmosphere over the entire globe, and satellite remote sensing also has advantages in observation area and time frequency. The operational weather satellite sensors can supply data records as long as more than 40 years, provide major support for cloud climate research. Whereas polar-orbiting cross-track scanning sensors generally only provide daily global coverage at particular local times, geostationary satellites are placed at particular longitudes along the equator and permit higher-frequency temporal sampling.Building the cloud climate dataset is related with some factors, such as recalibration, stable retrieval algorithm and validation. Based on long term satellite data, several cloud climate datasets, such as ISCCP, Patmos-x, CLARA, MODIS-ST, HIRS and so on are built selecting different instruments and different retrieval algorithm. Spatial and temporal resolutions of these cloud climate datasets are also different. Focusing on different properties of these cloud climate datasets including instruments and retrieval algorithm, references are cited to show the accuracy of these cloud climate datasets. Applications of these cloud climate data in weather and climate analysis are also introduced. As an example, the Tibetan Plateau is selected to analyze the difference between Patmos-x and CLARA-A2 that has the same satellite data source and high similar cloud detection algorithm. In long term, the changing trend is similar. The difference between these two cloud datasets is the spatial resolution:For Patmos-x, it's 0.1°, while for CLARA-A2, it's 0.25°. Compared with CLARA-A2, the Patmos-x has smaller cloud amount at day-time and has larger cloud amount at night-time. Based on NOAA-18 and Aqua data, Patmos-x, CLARA-A2 and Aqua/MODIS cloud amount during 2005 and 2015 are compared. Results show that the difference between three kinds of cloud amount is smaller in day-time, especially in summer. The difference increases in night-time, especially in winter and spring. The main cause may come from different observation ability, retrieval algorithm and observation time of different satellites.
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Key words:
- satellite;
- cloud;
- climate data
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图 2 1982—2015年20°~40°N，73°~105°E区域内NOAA-07，NOAA-09，NOAA-11，NOAA-14，NOAA-16和NOAA-18卫星的CLARA-A1，CLARA-A2及Patmos-x月平均白天云量时序图
Fig. 2 Monthly mean cloud fraction at day-time from CLARA-A1, CLARA-A2 and Patmos-x by NOAA-07, NOAA-09, NOAA-11, NOAA-14, NOAA-16 and NOAA-18 over 20°-40°N, 73°-105°E during 1982-2015
图 3 1982—2015年20°~40°N，73°~105°E区域内NOAA-07，NOAA-09，NOAA-11，NOAA-14，NOAA-16和NOAA-18卫星的CLARA-A1，CLARA-A2及Patmos-x月平均夜间云量时序图
Fig. 3 Monthly mean cloud fraction at night-time from CLARA-A1, CLARA-A2 and Patmos-x by NOAA-07, NOAA-09, NOAA-11, NOAA-14, NOAA-16 and NOAA-18 over 20°-40°N, 73°-105°E during 1982-2015
图 4 2005—2015年20°~40°N，73°~105°E区域内CLARA-A2, Patmos-x的NOAA-18和Aqua/MODIS月平均白天云量时序图
Fig. 4 Monthly mean cloud fraction at day-time from CLARA-A2, Patmos-x by NOAA-18 and Aqua/MODIS over 20°-40°N, 73°-105°E during 1982-2015
图 5 2005—2015年20°~40°N，73°~105°E区域内CLARA-A2, Patmos-x的NOAA-18和Aqua/MODIS月平均夜间云量时序图
Fig. 5 Monthly mean cloud fraction at night-time from CLARA-A2, Patmos-x by NOAA-18 and Aqua/MODIS over 20°-40°N, 73°-105°E during 1982-2015
表 1 主要云数据集信息
Table 1 Some kinds of cloud climate dataset information
数据集名称 空间分辨率 时间分辨率 时间范围 主要数据源 ISCCP 2.5°×2.5°(C和D系列)
30 km×30 km(DX数据)3 h, 日, 月 1983—2009年 NOAA，GMS，GOES，METEOSAT Patmos-x 0.1°×0.1° 每日2次，月 1979年至今 NOAA，Metop CLARA-A1 0.25°×0.25° 日，月 1982—2009年 NOAA CLARA-A2 0.25°×0.25° 日，月 1982—2015年 NOAA，Metop MODIS-ST 1 km×1 km，5 km×5 km 每日2次，月 2000年至今
2003年至今EOS/Terra
EOS/AquaHIRS 月 1980—2015年 NOAA，Metop 
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