Ionogram F-layer Parameters Autoscaling And FoF2 Short-term Forecast

As an important part of the near-Earth space environment,the ionosphere has a significant impact on human communication and satellite navigation;it has guiding significance for aerospace and disaster warning.However,due to numerous factors,the state of the ionosphere is complex and changeable.To meet the needs of different scenarios,it is necessary to monitor and forecast the ionosphere environment.Every year,humans obtain millions of ionograms through ionospheric sounding,in order to increase the efficiency of ionogram autoscaling,reduce manual measurement errors,and meet real-time requirements,this paper studies on automatic scaling of ionospheric vertical sounding ionogram based on image processing.At the same time considering thatf_o F₂ is a critical parameter to describe the state of ionosphere,this paper also explores the short-term forecast method off_o F₂ parameters.The main works of this paper as follows:1.The significance of the ionogram autoscaling and the short-term forecast of ionospheric parameters is briefly introduced,and the research objectives of the project are clarified.It summarizes and analyzes the inland and foreign research trends in related fields and establishes the main methods for this study.2.Introduced the basic theory of the ionosphere,including the layered structure of the ionosphere,control factors,detection principles and the specific meaning of the ionospheric parameters.According to the structural features of the trace of F-layer in the ionogram,the flow of the autoscaling algorithm is designed,including the ionogram preprocessing and parameter scaling.3.The initial denoising of the ionogram was performed using the median filter,and multiple echo traces were removed based on the vertical sounding mechanism.After using Canny's edge detection algorithm to further remove the noise interference,the contour of the echo trace of F-layer was extracted by the projection integration method.4.The O-wave trace was extracted by using the dilation and erosion mathematical morphology algorithm combined with the translation image to obtain the coincident area,and the restoration scheme was given for the extracting traces of discontinuities.After the O-wave trace is skeletonized by the ZS image refinement algorithm,the O-wave skeleton is stratified according to the features of the F1 layer and the F2 layer transition phase trace lengthening,and the parameters of each layer are separatelyh'F,f_o F₁,h'F₂,f_o F₂autoscaling,and statistics of the accuracy of the method of scaling.5.A brief analysis of the time-varying characteristics off_o F₂ in Xi'an and Haikou Station was conducted.On the basis of the empirical mode decomposition off_o F₂,a one-hour forecast off_o F₂ was advanced in combination with the wavelet neural network and an ideal forecasting result was obtained.6.This paper summarizes the research results that have been completed and analyzes the ionogram autoscaling andf_o F₂ short-term forecasting methods.Future work plans have been formulated,including:further improvement of ionogram denoising methods,extraction of complex ionogram trace methods,introduction of data assimilation methods for ionospheric prediction,and comparison of predictive results with international models.