Estimation of dusk time F-region electron density vertical profiles using LSTM neural networks:A preliminary investigation  

作　　者：Lucas Alves Salles Paulo Renato Pereira Silva Guilherme Schwinn Fagundes Jonas Sousasantos Alison Moraes 

机构地区：[1]Instituto Tecnol´ogico de Aeron´autica-ITA,Sao Jos´e Dos Campos,Brazil [2]William B.Hanson Center for Space Sciences,University of Texas at Dallas-UT Dallas,Richardson,TX,USA [3]Instituto de Aeron´autica e Espaço-IAE,S˜ao Jos´e Dos Campos,SP,12228-904,Brazil

出　　处：《Artificial Intelligence in Geosciences》2023年第1期209-219,共11页地学人工智能（英文）

基　　金：CAPES scholarships 88887.570088/2020-00 and 88887.634447/2021-00 and worked on this research in collaboration to the framework CNPq 465648/2014-2 and FAPESP 2017/01150-0.GSF;AOM are supported by CNPq awards 165561/2023-8 and 309389/2021-6 respectively;PRPS and JS were supported by CAPES awards 850937/2023-00 and 88887.901203/2023-00 respectively;JS also acknowledges FAPESP 2018/06158-9.

摘　　要：The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles(EPBs),that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System(GNSS).Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services.The objective of this study is to propose a neural network,trained with radio occultation data from the COSMIC-1 mission,that generates average ionospheric electron density profiles during dusk,focusing on the pre-reversal enhancement of the zonal electric field.Results show that the estimated profiles exhibit a clear seasonal pattern,and reproduce adequately the climatological behavior of the ionosphere,thus presenting strong appeal on ionospheric error attenuation.

关 键 词：Ionosphere density Equatorial plasma bubbles(EPBs) Ionospheric scintillation Global navigation satellite system(GNSS) Neural network modeling 

分 类 号：P35[天文地球—空间物理学]