The size of snow grains is an important parameter in cryosphere studies. It is the main parameter affecting snow albedo and can have a feedback effect on regional climate change, the water cycle and ecological security. Larger snow grains increase the likelihood of light absorption and are important for passive microwave remote sensing, snow physics and hydrological modelling. Snow models would benefit from more observations of surface grain size. This paper uses an asymptotic radiative transfer model (ART model) based on MOD09GA ground reflectance data. A simulation of snow grain size (SGS) in northeast China from 2001 to 2019 was carried out using a two-channel algorithm. We verified the accuracy of the inversion results by using ground-based observations to obtain stratified snow grain sizes at 48 collection sites in northeastern China. Furthermore, we analysed the spatial and temporal trends of snow grain size in Northeastern China. The results show that the ART model has good accuracy in inverting snow grain size, with an RMSD of 65 mu m, which showed a non-significant increasing trend from 2001 to 2019 in northeast China. The annual average SGS distribution ranged from 430.83 to 452.38 mu m in northeast China, 2001-2019. The mean value was 441.78 mu m, with an annual increase of 0.26 mu m/a, showing a non-significant increasing trend and a coefficient of variation of 0.014. The simulations show that there is also intermonth variation in SGS, with December having the largest snow grain size with a mean value of 453.92 mu m, followed by January and February with 450.77 mu m and 417.78 mu m, respectively. The overall spatial distribution of SGS in the northeastern region shows the characteristics of being high in the north and low in the south, with values ranging from 380.248 mu m to 497.141 mu m. Overall, we clarified the size and distribution of snow grains over a long time series in the northeast. The results are key to an accurate evaluation of their effect on snow-ice albedo and their radiative forcing effect.

Atmospheric precipitation is an important part of the water circle in an inland basin. Based on the analytical results of 149 precipitation samples and corresponding surface meteorological data collected at four sampling sites (Lenglong, Ningchang, Huajian and Xiying) at different elevations in the Xiying river basin on the north slope of Qilian Mountains from May to September 2017, the sub-cloud evaporation in precipitation and its controlling factors are analyzed by the Stewart model. The results show that sub-cloud evaporation led to d-excess value in precipitation decrease and d-excess variation from cloud-base to near surface (Delta d) increase with decreasing altitude. The remaining evaporation fraction of raindrop (f) decreases with decreasing altitude. The difference of underlying surface led to a difference change of f and Delta d in the Xiying sampling site. For every 1% increase in raindrop evaporation, d-excess value in precipitation decreased by about 0.99 parts per thousand. In an environment of high relative humidity and low temperature, the slope of the linear relationship between f and Delta d is less than 0.99. In contrast, in the environment of low relative humidity and high temperature, the slope is higher than 0.99. In this study, set constant raindrop diameter may affect the calculation accuracy. The Stewart model could have different parameter requirements in different study areas. This research is helpful to understand water cycle and land-atmosphere interactions in Qilian Mountains.