Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction  

作　　者：BingKun Yu PengHao Tian XiangHui Xue Christopher JScott HaiLun Ye JianFei Wu Wen Yi TingDi Chen XianKang Dou 

机构地区：[1]Institute of Deep Space Sciences,Deep Space Exploration Laboratory,Hefei 230088,China [2]CAS Key Laboratory of Geospace Environment,School of Earth and Space Sciences,University of Science and Technology of China,Hefei 230026,China [3]Hefei National Laboratory,University of Science and Technology of China,Hefei 230088,China [4]Department of Meteorology,University of Reading,Reading RG66BB,UK

出　　处：《Earth and Planetary Physics》2025年第1期10-19,共10页地球与行星物理（英文版）

基　　金：supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(grant No.YSBR-018);the National Natural Science Foundation of China(grant Nos.42188101,42130204);the B-type Strategic Priority Program of CAS(grant no.XDB41000000);the National Natural Science Foundation of China(NSFC)Distinguished Overseas Young Talents Program,Innovation Program for Quantum Science and Technology(2021ZD0300301);the Open Research Project of Large Research Infrastructures of CAS-“Study on the interaction between low/mid-latitude atmosphere and ionosphere based on the Chinese Meridian Project”.The project was supported also by the National Key Laboratory of Deep Space Exploration(Grant No.NKLDSE2023A002);the Open Fund of Anhui Provincial Key Laboratory of Intelligent Underground Detection(Grant No.APKLIUD23KF01);the China National Space Administration(CNSA)pre-research Project on Civil Aerospace Technologies No.D010305,D010301.

摘　　要：Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

关 键 词：ionospheric sporadic E layer radio occultation ionosondes numerical model deep learning model artificial intelligence 

分 类 号：P352[天文地球—空间物理学]