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From August 4th to 30th, 2020 and from November 27th to December 25th, 2020, a self-developed radiosonde balloon system was used to observe high-altitude atmospheric optical turbulence at three sites in northwestern China, and an improved model based on the observational data was established. Through comparative analysis of the observational data and the improved model, the distribution characteristics of atmospheric optical turbulence under the combined action of different meteorological parameters and different landform features in different seasons were obtained. The improved model can show the variation of the detailed characteristics of turbulence with the height distribution, and the degree of correlation with the measured values is above 0.82. The improved model can provide a theoretical basis and supporting data for turbulence estimation and forecasting in northwestern China. [ABSTRACT FROM AUTHOR]

With the globalization and rapid development every filed is taking an increasing interest in physical geography and human economics. There is a surging demand for small scale world map in large formats all over the world. Further study of automated mapping technology, especially the realization of small scale production on a large scale global map, is the key of the cartographic field need to solve. In light of this, this paper adopts the improved model (with the map and data separated) in the field of the mapmaking generalization, which can separate geographic data from mapping data from maps, mainly including cross-platform symbols and automatic map-making knowledge engine. With respect to the cross-platform symbol library, the symbol and the physical symbol in the geographic information are configured at all scale levels. With respect to automatic map-making knowledge engine consists 97 types, 1086 subtypes, 21845 basic algorithm and over 2500 relevant functional modules. In order to evaluate the accuracy and visual effect of our model towards topographic maps and thematic maps, we take the world map generalization in small scale as an example. After mapping generalization process, combining and simplifying the scattered islands make the map more explicit at 1:2.1 billion scale, and the map features more complete and accurate. Not only it enhance the map generalization of various scales significantly, but achieve the integration among map-makings of various scales, suggesting that this model provide a reference in cartographic generalization for various scales. [ABSTRACT FROM AUTHOR]