Estimating regional amount of low clouds over North China plain from multi-source remote sensing data

Low cloud is the main source of precipitation as well as an important modulator of radiative fluxes. Satellite and in situ observations provide valuable cloud information. In this study, we estimated the low cloud amount over North China Plain based on MODIS products, and validated the results with ground-based and CloudSat data. First, we classified low cloud into two types: pure low cloud and overlapped low cloud. Then we conducted detection algorithm for pure low cloud, and estimated the total low cloud fraction (LCF) under a random overlapping assumption. Monthly mean MODIS-derived and ground-based LCF showed a good agreement in seasonal variation. And the linear correlation coefficient for these two LCFs was 0.642. Finally, MODIS-derived low cloud scenes were collated with CloudSat cloud scenarios, both the pure and overlapped ones were validated.