Algorithm For The Calibration Of Elevation From SuperDARN Ground Backscatter Echoes And Ionospheric Parameter Inversion

Elevation information is an important parameter in SuperDARN radar detection.A reliable elevation angle helps estimate the propagation paths of high-frequency radio signals between scattering spots and radars,which is crucial for determining high-frequency radar target geolocation.The SuperDARN radar uses interferometry to estimate the elevation of the returned signal.However,elevation data are still underutilized owing to the difficulties of phase difference calibration induced by the propagation time delay between two arrays.The data used in this paper are detected in the northern and southern hemispheres by SuperDARN radar network.The ground backscatter echoes are distinguished from others by using the conditions of power,velocity and spectral width.This paper statistically analyzes the distribution features of the group range-elevation angle and group range-virtual height before and after calibration using elevation data of ground backscatter echoes,proposes a numerical analysis method which uses the minimum root mean square error（RMSE）to judge the calibration effect.The daily calibration factors result show that nearly all of the calibrated RMSE decreased to 73%of the original and most of the RMSE ratio can be reduced to 54%.The calibrated elevation angle is more in line with its theoretical distribution.This study proposes a band-new idea for the future research on automation of elevation calibration and its high accuracy which improve the previous study that estimates calibration factors by visual analysis and firstly introduces the numerical analysis method of RMSE.Besides,this paper also calculates the virtual height of the ground backscatter echoes by calibrated elevation angle,combined with the phase data,estimating a second calibration factor by phase deviation analysis method.And then compared with the RMSE method to verify the consistency of the algorithm.The simulations demonstrate the calibration factors are basically identical.Previous studies are often assumed to be under a fixed virtual height while this paper will be tuning with a time.Short-term data on Han radar shows that the versatility of proposed algorithm.Finally,this article also uses the deep learning model combined with elevation for the inversion of MUF andfo F₂.The result show that reliable elevation angle can significantly improve the performance of the inversion parameters and the temporal sequence model LSTM show better inversion effect of BP model.In conclusion,this study not only calibrates the elevation angle error of Zhongshan radar by numerical analysis method and verifies its reliability and versatility,but also proves the effectiveness of accurate elevation and deep learning model in inversion of ionospheric parameters.