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María Paula Natali

Researcher at National University of La Plata

Publications -  21
Citations -  186

María Paula Natali is an academic researcher from National University of La Plata. The author has contributed to research in topics: Total electron content & Earth's magnetic field. The author has an hindex of 8, co-authored 21 publications receiving 158 citations. Previous affiliations of María Paula Natali include National Scientific and Technical Research Council.

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Multi-year GNSS monitoring of atmospheric IWV over Central and South America for climate studies

TL;DR: In this paper, the authors performed a regional, 7-year-long and homogeneous analysis, comprising 136 ground-based global navigation satellite system (GNSS) tracking stations, obtaining high-rate and continuous observations of column-integrated water vapour and troposphere zenith total delay.
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Annual and semiannual variations of vertical total electron content during high solar activity based on GPS observations

TL;DR: In this article, the authors used Global IGS VTEC maps and principal component analysis to study spatial and temporal ionospheric variability at two-hour periods, at noon and at night.
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Estimation of precipitable water vapour from GPS measurements in Argentina: Validation and qualitative analysis of results

TL;DR: In this paper, the authors presented PWV estimates from GPS data computed at four continuously operated GPS stations in Argentina established at Buenos Aires, Cordoba, Rosario and Salta over a 1 year period (2006-2007).
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Analysis of the winter and semiannual ionospheric anomalies in 1999–2009 based on GPS global International GNSS Service maps

TL;DR: In this article, the analysis of seasonal and semiannual ionospheric anomalies using vertical total electron content (VTEC) measurements obtained by the International GNSS Service (IGS) is focused on the analysis using principal component analysis (PCA) and wavelet transform (WT).
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Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting

TL;DR: A comparison between two different artificial neural networks: an adaptive neuro-fuzzy inference system and nonlinear autoregressive neural network applied to TEC shows NAR-NN shows a slightly better performance, being the higher difference during the greater perturbations.