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Abstract. In order to understand the differences of raindrop size distribution (DSD) in complex mountainous terrain, the characteristics of DSD were analyzed by using the six-months observation data at the southern slopes, northern slopes and inside in Qilian Mountains. For all rainfall events, the number concentration of small and large raindrops on the inside and south slope are greater than that on the north slope, but midsize raindrops are less. The DSD spectrum of inside mountains are more variable and significantly differ from the north slopes. The differences in normalized intercept parameters of DSD for stratiform and convective rainfall are 8.3 % and 10.4 %, respectively, and mass-weighted diameters are 10.0 % and 23.4 %, respectively, which the standard deviation of DSD parameters on inside stations are larger. The differences in coefficient and exponent of Z-R relationship are 2.5 % and 10.7 %, respectively, with an increasing value of coefficient from the south slope to the north slope in stratiform rainfall but opposite to convective rainfall. In addition, the DSD characteristics and Z-R relationships are more similar at the ipsilateral stations and have smaller differences between the south slope and inside mountains.

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With the development of artificial intelligence (AI) in recent years, meteorological departments have also begun to improve algorithms and revise short-term forecasts via AI, expecting to timely capture meteorological clues in massive weather data, to "prevent meteorological disasters", and "calculate precipitation faster and more accurately". At present, AI has been initially applied to the meteorological field, especially to the analysis of massive meteorological data. For instance, the AI-based data analysis technology can rapidly judge the cloud type and the meteorological prototype in satellite images. The AI-based data fusion technology contributes to more three-dimensional and refined atmosphere data, which improves the temporal and spatial resolutions of precipitation data. If the big data in AI are used to analyze typhoons and identify the typhoon track and source, the errors resulting from the naked-eye observation of images by meteorologists can be avoided, thus considerably improving the scientificity and accuracy of weather forecasts. During data fusion, the severe convective weather characteristics reflected by massive historical precipitation data can be learned through machine learning methods to predict the evolution trend of disastrous weather within the future 1 to 2 h. Furthermore, precipitation data errors are corrected through AI data analysis, and a daily precipitation fusion dataset with a spatial resolution of 1 km is obtained.