Method for restoring missing air temperature records on basis of spatial-temporal information fusion

Abstract

The invention discloses a method for restoring missing air temperature records on the basis of spatial-temporal information fusion. The method includes steps of preprocessing air temperature sequence data and indicating missing air temperatures by specific symbols; analyzing spatial-temporal characteristics of air temperatures to construct an air temperature distribution model, and decomposing air temperature variables into air temperature trend terms and air temperature residual terms; analyzing time sequences of air temperature and time sequences to estimate air temperature trend terms of the missing records and estimating residual terms of the missing records by a Kriging interpolation process; restoring the missing air temperature records. The method has the advantages that the spatial-temporal distribution characteristics of the air temperatures are comprehensively considered and are fused into the unified model, so that the missing air temperature record restoring precision can be greatly improved, the computational complexity is low, the method is easy to implement and has a broad application prospect, and the like.

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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