Research On The Quality Control Method Of Surface Air Temperature Observation Data Based On Spatial Dimension Analysis

The assimilation technology of ground meteorological observation data is an important method to improve the accuracy of numerical weather prediction,and the premise of assimilating ground meteorological observation data is to carry out effective quality control of ground meteorological observation data.Due to my country's complex topography,climate change,and uneven distribution of automatic weather stations,only a small part of the ground meteorological observation data has entered the assimilation system,and conventional quality control methods cannot meet the needs of meteorological services.In view of this,based on the analysis of the temporal and spatial distribution characteristics of surface temperature observation data,this article constructs a surface temperature observation data quality control method based on spatial dimension analysis for different situations.The specific content is as follows:(1)Aiming at areas with flat terrain and densely distributed stations,considering the obvious correlation of the temperature observation data of space automatic weather stations,a mobile surface fitting-based surface temperature quality control method(Mobile Surface Fitting,MSF)is proposed.First,calculate the Euclidean distance between each reference station and the target station as the reference station weight,then use the moving surface fitting algorithm to fit the correlation surface,calculate the temperature data of the target station,and compare the real data for quality control.In order to verify the validity and accuracy of this method,this method is used to carry out quality control analysis on the 2011 average daily temperature data of 25 automatic weather stations in my country,and compare it with the traditional spatial quality control methods IDW and SRT.The experimental results show that the MSF method has better predictability and error detection than IDW and SRT,and can be effectively used for the interpolation and detection of temperature data at observatories.(2)Aiming at areas with complex topography and geomorphology,considering the stability and accuracy of the MSF algorithm,a surface temperature quality control method(Cosine and Mobile Surface Fitting,COS?MSF)based on cosine similarity and moving surface fitting is proposed.Use the cosine value and the root mean square error to analyze the correlation between neighboring sites and the target site,and select the neighboring sites with higher correlation as the reference site,The selection of the threshold is optimized and analyzed by genetic algorithm,and the Euclidean distance weight method in the MSF method is improved,and the cosine value and the root mean square error value are used to weight the reference station.The experimental results show that the COS?MSF algorithm has a significant improvement in accuracy and stability compared to the MSF algorithm.(3)Aiming at the western and northeastern regions with sparse stations,due to the lack of observation data and relatively low spatial correlation,a surface temperature quality control method(Characteristic Polynomial and Ridge Regression,CP?RR)based on characteristic polynomial and ridge regression is proposed.The temperature data of the original reference station is upscaled by the characteristic polynomial,the data samples are added,and the dimensionless processing is performed,and finally the ridge regression model is used to fit the temperature data of the target station.The experimental results show that the CP?RR algorithm has obvious advantages over other methods in error detection performance in sparse sites,and it also has a better quality control effect in areas such as the eastern coast.