三种卫星云量数据在青藏高原地区的比对分析

青藏高原是卫星反演云参数的热点和难点区域.选取1982年—2015年0.1°空间分辨率的Patmos-x、0.25°空间分辨率的CLARA-A2的NOAA/AVHRR下午星数据和2003年—2015年0.05°空间分辨率的Aqua/MODIS C6等3种云量数据,针对青藏高原区域,从数据的反演算法和数据的空间属性出发,...

Performance of cloud fraction of three satellite cloud climate date records over the Tibetan Plateau

Tibetan Plateau (TP) plays an important role in adjusting the large-scale atmospheric circulation in the northern hemisphere and the atmosphere-sea interaction from the equator to the middle latitude in the North Pacific. Obtaining complete observation data based on ground observations over TP is difficult. Satellite provides good observational data over the Tibetan Plateau. Considering the complex underlying surface types and geographical elevations in the Tibetan Plateau region, three kinds of long-term cloud fraction data that came from PATMOS-x/AVHRR, CLARA-A2/AVHRR, and MODIS / Aqua were analyzed from the perspective of data retrieval methods and data spatial attributes.The relationship among the three kinds of satellite cloud fraction and the ground observation cloud fraction was analyzed at first. Correlation analysis, linear trend, and accumulate bias were used to analyze the data. The analysis data were selected from instantaneous orbital observations and monthly and annual mean value.The annual mean cloud fraction of the three kinds of data are similar, but seasonal cloud fraction is different. CLARA-A2 has the smallest cloud fraction in summer and the highest cloud fraction in winter. Patmos-x agreed well with the ground observation. The correlation relationship between CLARA-A2 and ground was weak. Aqua/MODIS had good relationship in autumn and less correlation in spring and summer.The three kinds of long-term cloud fraction data showed similar spatial and temporal distribution. During daytime, CLARA-A2 has larger cloud fraction than MODIS and PATMOS-x. At nighttime, MODIS has the maximum cloud fraction value, and PATMOS-x and CLARA-A2 have similar values. All three kinds of cloud data committed a mistake with snow along the ridge of a mountain. The linear regression and accumulate bias analysis showed that the annual mean cloud fraction of PATMOS-x and CLARA-A2 displayed a decreasing trend from 1982 to 2015. The trend of the night time cloud fraction was more obvious than that of daytime. CLARA-A2 displayed more obvious trend than PATMOS-x, especially at night. The year of 2000 is a turning point for the change in cloud cover over the plateau area from high to low. In January, April, and October, the decrease in cloud amount is the main change trend. Meanwhile, in July, the weak increase is the main change characteristic.Three kinds of satellite cloud data have good comparability. Three kinds of data obtained different correlations when compared with the ground observation. The reasons may come from matched data with different spatial and temporal characteristics, different payloads with various observation abilities and different data set with different cloud detection algorithms.The stability of satellite orbit and high quality of instrument calibration are the baselines of long-term climate data. MODIS has stable instrument orbit and calibration. Thus, its long term cloud data have good homogeneity. © 2021, Science Press. All right reserved.