A Method for Reconstructing Missing Air Temperature Records by Fusion of Spatiotemporal Information

Abstract

A method for reconstructing missing air temperature records by fusing spatiotemporal information. The method includes the following steps: firstly, preprocessing the temperature sequence data, and using specific symbols to represent the missing air temperature; It is decomposed into a temperature trend item and a temperature residual item; then time series analysis is performed on the temperature time series to estimate the temperature trend item of missing records, and Kriging interpolation method is used to estimate the residual items of missing records; finally, the missing temperature records reconstruction. This method comprehensively considers the temporal and spatial distribution characteristics of temperature and integrates them into a unified model, which can not only greatly improve the reconstruction accuracy of missing temperature records, but also has the characteristics of small amount of calculation and easy implementation. It has broad application prospects .

Description

technical field [0001] The invention relates to a method for processing meteorological data, in particular to a method for reconstructing missing air temperature records by fusing spatiotemporal information. Background technique [0002] Climate change has extremely far-reaching impacts on human survival, economic and social development, etc., and has become an issue of widespread concern and a research hotspot at home and abroad. The lack of meteorological records is a common problem in climate change research. Incorrect measurements, instrument failures, human and natural factors will lead to discontinuous meteorological records. On the one hand, many types of meteorological analysis in reality require a complete sequence of meteorological records, and the lack of records becomes the main constraint factor for these analyses; efficiency. Therefore, how to accurately reconstruct the missing meteorological records is of great significance. [0003] The study of reconstruc...

AI Technical Summary

Problems solved by technology

However, such methods often use classical statistics to determine the weights of surrounding meteorological observation stations, without fully considering the spatial distribution characteristics of meteorological data, such as NR and neighborhood feature methods; in addition, some methods are computationally complex and difficult to implement, such as Determination of Spatiotemporal Variation Model in Spacetime Kriging

In short, the existing temperature record reconstruction methods have the disadvantage that they cannot or are difficult to consider the spatial and temporal distribution characteristics at the same time, resulting in the final reconstruction accuracy is often not high

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