A neural network based error correction method for radio occultation electron density retrieval

Viet Cuong Pham, Jyh Ching Juang

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Abel inversion techniques have been widely employed to retrieve electron density profiles (EDPs) from radio occultation (RO) measurements, which are available by observing Global Navigation Satellite System (GNSS) satellites from low-earth-orbit (LEO) satellites. It is well known that the ordinary Abel inversion might introduce errors in the retrieval of EDPs when the spherical symmetry assumption is violated. The error, however, is case-dependent; therefore it is desirable to associate an error index or correction coefficient with respect to each retrieved EDP. Several error indices have been proposed but they only deal with electron density at the F2 peak and suffer from some drawbacks. In this paper we propose an artificial neural network (ANN) based error correction method for EDPs obtained by the ordinary Abel inversion. The ANN is first trained to learn the relationship between vertical total electron content (TEC) measurements and retrieval errors at the F2 peak, 220. km and 110. km altitudes; correction coefficients are then estimated to correct the retrieved EDPs at these three altitudes. Experiments using the NeQuick2 model and real FORMOSAT-3/COSMIC RO geometry show that the proposed method outperforms existing ones. Real incoherent scatter radar (ISR) measurements at the Jicamarca Radio Observatory and the global TEC map provided by the International GNSS Service (IGS) are also used to valid the proposed method.

原文English
頁(從 - 到)77-84
頁數8
期刊Journal of Atmospheric and Solar-Terrestrial Physics
135
DOIs
出版狀態Published - 2015 十二月 1

All Science Journal Classification (ASJC) codes

  • 地球物理
  • 大氣科學
  • 空間與行星科學

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