Study of ionospheric TEC short-term forecast model based on combination method

Niu, Ruizhao; Guo, Chengjun; Zhang, Yiran; He, Liang; Mao, Yanling

doi:10.1109/icosp.2014.7015430

Cited by 14 publications

(11 citation statements)

References 2 publications

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“…The literature provides several methods using time series and statistical methods to predict TEC with various forecasting horizons from a few minutes to several days based on the previous state of the ionosphere. Most of these methods [5,8,21,24,35] provide predictions above specific stations. Among these, a few works aim at reconstructing the TEC on a small area [29,32] with methods such as Bezier surface-fitting or Kriging.…”

Section: Previous Workmentioning

confidence: 99%

“…Finally [24] predicts the mean TEC level globally. For comparison, we apply the following global mean TEC RMS:…”

Section: Comparison With Literaturementioning

confidence: 99%

“…The reason is that as we use a regression loss, the model try to reduce the loss for the entire map, which somehow can be interpreted to reducing the global TEC error. Moreover compared to [24] we use much more training data as they train their ARMA model on only 180 values.…”

Section: Comparison With Literaturementioning

confidence: 99%

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Ionospheric activity prediction using convolutional recurrent neural networks

Boulch¹,

Cherrier²,

Castaings³

2018

Preprint

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The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications. Being able to forecast globally the Total Electron Content (TEC) would enable a better anticipation of potential performance degradations. A few studies have proposed models able to predict the TEC locally, but not worldwide for most of them. Thanks to a large record of past TEC maps publicly available, we propose a method based on Deep Neural Networks (DNN) to forecast a sequence of global TEC maps consecutive to an input sequence of TEC maps, without introducing any prior knowledge other than Earth rotation periodicity. By combining several state-of-the-art architectures, the proposed approach is competitive with previous works on TEC forecasting while predicting the TEC globally.

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Section: Previous Workmentioning

confidence: 99%

“…Finally [24] predicts the mean TEC level globally. For comparison, we apply the following global mean TEC RMS:…”

Section: Comparison With Literaturementioning

confidence: 99%

See 1 more Smart Citation

Ionospheric activity prediction using convolutional recurrent neural networks

Boulch¹,

Cherrier²,

Castaings³

2018

Preprint

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“…The ASHF TEC model performed well to capture northern EIA features and ionospheric TEC variability for quiet and disturbed day conditions [3]. These ionospheric TEC time series forecasting models are implemented at a specific location using time series and statistical methods [4,5,6]. The Machine Learning (ML) techniques successfully forecast ionospheric TEC variability.…”

Section: Introductionmentioning

confidence: 99%

A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps

Krishna

Ratnam

Sivavaraprasad

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The rapid evolutions in Artificial Intelligence (AI) and the Machine Learning era have significantly improved accuracy for ionospheric space weather forecasting models. The ionospheric Total Electron Content (TEC) forecasting is necessary to alert Global Navigation Satellite System (GNSS) users about ionospheric space weather influences on satellitereceiver radio communications. Precise modeling and forecasting of the ionospheric TEC are critical for reliable and accurate GNSS applications. In this paper, a deep learning-based Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm is implemented for 26 GPS (Global Positioning System) stations TEC data over the Indian region. The Bi-LSTM ionospheric TEC forecasting maps are generated and compared with ANN, and LSTM models during both geomagnetic quiet and disturbed periods. The potential of Bi-LSTM networks in time sequence processing is improved by having forward and backward connections. The Bi-LSTM results are demonstrated with and without solar and geomagnetic indices as input to the network. This work's outcome would help develop an ionospheric weather alert system for GNSS users.

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“…For example, the Klobuchar model corrects only 50-60% of the ionospheric delay in mid-latitudes during quiet solar activity (Pongracic et al, 2019;Tongkasem et al, 2019). In contrast, ionospheric forecast models based on GNSS measurements are more practical due to their high accuracy, estimated RMS error for 24 h TEC forecast is 2-5 TECU under geomagnetically quiet conditions (Gulyaeva et al, 2013;Niu et al, 2014;Badeke et al, 2018). However, because the ionosphere is in uenced by solar and geomagnetic activities, the spatial variability of the ionosphere is extremely complex, and small-scale irregularities or perturbations often occur in some regions, especially at some low latitudes.…”

Section: Introductionmentioning

confidence: 99%

LSTM-based short-term ionospheric TEC forecast model and position accuracy analysis

Xie

Dai

Zhu

et al. 2022

Preprint

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Ionospheric delay is one of the major error sources in global navigation satellite system (GNSS) single point positioning (SPP). Some empirical models have been proposed to correct ionospheric delay, which, however, are limited by the low accuracy. The single-point time-series-based ionospheric total electron content (TEC) forecast method theoretically introduces model error and the accumulation of forecast error increases during a day. Therefore, based on the regular variation characteristics of the ionosphere, to improve the forecast accuracy of the ionosphere, we propose a long short-term memory (LSTM) short-term forecast model using discrete GNSS data and ionospheric space environment data in the same period of multiple days. The LSTM ionospheric forecast model is constructed based on the 2014 single-site GNSS data from different regions (low, mid, and high latitude regions) of the Crustal Movement Observation Network of China (CMONOC). The results are compared with the Klobuchar model, CMONOC regional ionosphere maps (RIM) data, and GNSS derived TEC measurements. The performance of each model in the SPP is also compared and analyzed to further examine the feasibility of the LSTM ionospheric forecast model. The comprehensive statistical analysis shows that: i) LSTM forecast model is consistent with GNSS-TEC observations at high, mid, and low latitudes, and the forecast error is less than 3 TECu, which is much better than that of the Klobuchar model and RIM model, and is robust to anomalous values. The mean absolute error (MAE) and root mean square error (RMSE) of the LSTM forecast model decrease with increasing latitude. ii) When being used in SPP, the LSTM forecast model brings significant improvement to position accuracy, which is overall better than the RIM and Klobuchar models. The RMSE of 3D position error is 2–4 m for the LSTM forecast model, 2–5 m for the RIM model, and 4–5 m for the Klobuchar model. iii) In terms of geographic location, for 3D position accuracy, it is highest at mid-latitudes followed by high latitudes and worst at low latitudes. For the percentage of 3D position corrections relative to the reference, it is highest at high latitudes followed by mid-latitudes, and lowest at low latitudes. The percentage of LSTM forecast model is 85%-90% at low latitudes, which is better than Klobuchar's 40%-80% and RIM's 75%-85%. Such percentage is over 90% at mid-latitudes, which is comparable to the RIM and about 50% better than the Klobuchar model. It is noteworthy that the LSTM performs even better than the reference data at high latitudes.

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Study of ionospheric TEC short-term forecast model based on combination method

Cited by 14 publications

References 2 publications

Ionospheric activity prediction using convolutional recurrent neural networks

Ionospheric activity prediction using convolutional recurrent neural networks

A Bidirectional Deep-Learning Algorithm to Forecast Regional Ionospheric TEC Maps

LSTM-based short-term ionospheric TEC forecast model and position accuracy analysis

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