Ground temperature observation data analysis method based on adaptive kernel density estimation algorithm

Abstract

The invention relates to a ground temperature observation data analysis method based on an adaptive kernel density estimation algorithm, and belongs to the field of ground temperature observation dataanalysis. According to the method, a self-adaptive algorithm is introduced on the basis of a traditional fixed window width kernel density estimation algorithm, namely, an adaptive coefficient is introduced into a window width parameter, the influence caused by the sparse degree of the sample observation value can be effectively reflected, then the adaptive algorithm is improved, the window widthparameter is replaced with the optimal window width, the result obtained under the ground air temperature observation data meets the requirement that all mean square errors are minimum, and the improved adaptive method is completely suitable for the ground air temperature observation data.

Description

technical field [0001] The invention relates to the field of surface air temperature observation data analysis, in particular an improved kernel density estimation algorithm is invented to analyze surface air temperature elements. Background technique [0002] In recent years, the trend of global warming has become more and more obvious, and temperature changes have brought serious impacts on society. Therefore, it has attracted extensive attention from scholars from all over the world, and many meaningful conclusions have been obtained for this research. As far as our country is concerned, there are many researches on areas with special geographical location such as plateaus and basins, and many achievements have been made, while the research on the southeast region where the temperature is relatively stable is insufficient. Most of the traditional temperature analysis methods are based on the time series to predict and analyze the future change trend. From the perspective ...

AI Technical Summary

Problems solved by technology

Most of the traditional temperature analysis methods are based on the time series to predict and analyze the future change trend. From the perspective of space, experts and scholars from various countries have also conducted a series of researches. However, the factors affecting the temperature change are very complex and have obvious regional characteristics. with seasonal differences

Existing studies have insufficient analysis of the causes of temperature changes

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