DOI: 10.1029/2018JA025559
Scopus记录号: 2-s2.0-85050872899
论文题名: The Improved Two-Dimensional Artificial Neural Network-Based Ionospheric Model (ANNIM)
作者: Tulasi Ram S. ; Sai Gowtam V. ; Mitra A. ; Reinisch B.
刊名: Journal of Geophysical Research: Space Physics
ISSN: 21699380
出版年: 2018
卷: 123, 期: 7 起始页码: 5807
结束页码: 5820
语种: 英语
英文关键词: Digisonde
; GPS-radio occultation
; ionosphere
; modeling
; neural networks
英文摘要: An artificial neural network-based two-dimensional ionospheric model (ANNIM) that can predict the ionospheric F2-layer peak density (NmF2) and altitude (hmF2) had recently been developed using long-term data of Formosat-3/COSMIC GPS radio occultation (RO) observations (Sai Gowtam & Tulasi Ram, 2017a, https://doi.org/10.1002/2017JA024795). In this current paper, we present an improved version of ANNIM that was developed by assimilating additional ionospheric data from CHAMP, GRACE RO, worldwide ground-based Digisonde observations, and by using a modified spatial gridding approach based on the magnetic dip latitudes. The improved ANNIM better reproduces the spatial and temporal variations of NmF2 and hmF2, including the postsunset enhancement in equatorial hmF2 associated with the prereversal enhancement in the zonal electric field. The ANNIM-predicted NmF2 and hmF2 exhibit excellent correlations with ground-based Digisonde observations over different solar activity periods. The ANNIM simulations under enhanced geomagnetic activity predict the depletion of NmF2 at auroral-high latitudes, and enhancement over low latitude to midlatitude with respect to quiet conditions, which is consistent with the storm time meridional wind circulation and the associated neutral composition changes. The improved ANNIM also predicts a significant enhancement in hmF2 around auroral latitudes due to increased plasma scale height associated with particle and Joule heating during storm periods. Further, the ANNIM successfully reproduces the coherent oscillations in NmF2 and hmF2 with recurrent cororating interaction region-driven geomagnetic activity during the extreme solar minimum year 2008 and can distinguish the roles of recurrent geomagnetic activity and solar irradiance through controlled simulations. ©2018. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113649
Appears in Collections: 气候减缓与适应
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作者单位: Indian Institute of Geomagnetism, Navi Mumbai, India; Department of Atmospheric Sciences, University of Illinois, Urbana, IL, United States; Lowell Digisonde International, Lowell, MA, United States; Center for Atmospheric Research, University of Massachusetts, Lowell, MA, United States
Recommended Citation:
Tulasi Ram S.,Sai Gowtam V.,Mitra A.,et al. The Improved Two-Dimensional Artificial Neural Network-Based Ionospheric Model (ANNIM)[J]. Journal of Geophysical Research: Space Physics,2018-01-01,123(7)