Improved nuclear regression ground temperature observation data quality control method

Abstract

The invention discloses an improved nuclear regression ground temperature observation data quality control method. The method comprises the following steps: S1, screening experimental data; S2, introducing a kernel regression method and improving ground air temperature observation data needing to be applied; S3, improving the applicability of the multi-site kernel regression method on the basis ofS2; and S4, introducing an adaptive algorithm and a particle swarm algorithm to improve a window width coefficient in a kernel regression method, and performing regression prediction and corresponding quality control according to the principle. According to the invention, on the aspect of the method, a kernel regression method is introduced, ground temperature observation data is improved, meanwhile, the window width coefficient in the kernel regression method is further improved through the self-adaption and particle swarm optimization algorithm, and the direction of a traditional quality control method is expanded; in the aspect of quality control effect, the method has good effects in the aspects of prediction precision, universality, error detection rate and the like in the field of quality control, and is more beneficial to research and application of quality control of ground air temperature observation data.

Description

technical field [0001] The invention relates to the field of quality control of surface temperature observation data, in particular to an improved kernel regression quality control method of surface temperature observation data. Background technique [0002] The purpose of quality control (QC) of surface air temperature observation data is to review the collected air temperature data, find missing data and doubtful data, and supplement and correct them, so as to ensure the maximum extent of archived data. complete and accurate. Surface meteorological observation data is the basic data in meteorological research, and has very important decision-making significance for data assimilation technology and numerical weather prediction technology. Numerical weather prediction (NWP), as a key weather forecast technology in the context of today's information age, its accuracy is largely restricted by data assimilation technology, and the quality control of surface air temperature obs...

AI Technical Summary

Problems solved by technology

[0004] The current quality control methods can achieve corresponding quality control effects in their research, but they are not very universal in the face of surface air temperature observation data in different regions or at different time scales

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