Forecasting the local ionospheric foF2 parameter 1 hour ahead during disturbed geomagnetic conditions

Chen, C.; Wu, Zhensen; Xu, Zhengwen; Sun, Shuo; Zhang, Ding; Ban, Panpan

doi:10.1029/2010ja015529

Cited by 18 publications

(7 citation statements)

References 37 publications

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“…For instance, one can employ a time history of a given quantity, recorded with a certain time frequency. Examples of supervised regression in Space Weather are the forecast of a geomagnetic index, as function of solar wind parameters observed at L1 (Gleisner et al, ; Lundstedt & Wintoft, ; Macpherson et al, ; Uwamahoro & Habarulema, ; Valach et al, ; Weigel et al, ), the prediction of solar energetic particles (SEPs) (Fernandes, ; Gong et al, ; Li et al, ), of the F10.7 index for radio emissions (Ban et al, ; Huang et al, ), of ionospheric parameters (Chen et al, ), of sunspot numbers or, more in general, of the solar cycle (Ashmall & Moore, ; Calvo et al, ; Conway et al, ; Fessant et al, ; Lantos & Richard, ; Pesnell, ; Uwamahoro et al, ), of the arrival time of interplanetary shocks (Vandegriff et al, ), and of CMEs (Choi et al, ; Sudar et al, ).…”

Section: Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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The numerous recent breakthroughs in machine learning make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in Space Weather. The purpose is twofold. On one hand, we will discuss previous works that use machine learning for Space Weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the Space Weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray box. Plain Language SummaryIn the last decade, machine learning has achieved unforeseen results in industrial applications. In particular, the combination of massive data sets and computing with specialized processors (graphics processing units, or GPUs) can perform as well or better than humans in tasks like image classification and game playing. Space weather is a discipline that lives between academia and industry, given the relevant physical effects on satellites and power grids in a variety of applications, and the field therefore stands to benefit from the advances made in industrial applications. Today, machine learning poses both a challenge and an opportunity for the space weather community. The challenge is that the current data science revolution has not been fully embraced, possibly because space physicists remain skeptical of the gains achievable with machine learning. If the community can master the relevant technical skills, they should be able to appreciate what is possible within a few years time and what is possible within a decade. The clearest opportunity lies in creating space weather forecasting models that can respond in real time and that are built on both physics predictions and on observed data.

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Section: Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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“…Machine learning can automatically make a model from data, even in case that they are not clearly understood. Therefore machine learning technology has been employed for space weather applications in the following two aspects: space weather prediction (Al-Omari et al 2010;Chen et al 2010;Colak et al 2009;Gavrishchaka et al 2001;He et al 2008;Li et al 2007;Liu Corresponding Author : Y.-J. Moon et al 2011;Olmedo et al 2005;Qahwaji et al 2007Qahwaji et al , 2008Yuan et al 2011) and solar feature identification (Henwood et al 2010;Labrosse et al 2010;Quaalude et al 2003Quaalude et al , 2005Martens et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Application of Support Vector Machine to the Prediction of Geo-Effective Halo Cmes

Choi¹,

Moon²,

Vien³

et al. 2012

Journal of The Korean Astronomical Society

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In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

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“…The prediction of monthly median f0F2 values by Lamming, the short-time forecasting by Cander and modeling of day variations of f0F2 by Francis all have been in relation with artificial-neural network-based methods [4] [5] [6]. With the development of machine learning, Chen introduced a method for forecasting the f0F2 using the support vector machine (SVM) approach [7] [8], and Sai Gowtam presented a method combined artificial neural networks and global GPS dataset [9]. Feynman and Gabriel proposed that variations in the solar, magneto spheric and ionospheric characteristics could affect a variety of ground-based and space-borne technological systems [10].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Ionospheric f0F2 Parameter One Hour in Advance Using Recurrent Neural Network

Lv¹,

Yu²,

Liu³

2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

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It is difficult to forecast the state of ionosphere because the time-varying characteristics. Using recurrent neural network (RNN) one hour ahead prediction of the critical parameter of ionospheric F2 layer (f0F2) is realized. The prediction model is developed based on 11 years (from 2005 to 2016) of data measured from ionospheric vertical stations in China. By analyzing time series correlation of f0F2 and solar-terrestrial and geomagnetic activities, several parameters are selected as inputs. Though training the RNN model, the forecasting values one hour ahead can be obtained. For this time-series problem, the predicted ability of RNN is better than Artificial Neural Network (ANN) and the autocorrelation method by comparing the results of three different algorithms.

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Forecasting the local ionospheric f_oF₂ parameter 1 hour ahead during disturbed geomagnetic conditions

Cited by 18 publications

References 37 publications

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

Application of Support Vector Machine to the Prediction of Geo-Effective Halo Cmes

Forecasting the Ionospheric f0F2 Parameter One Hour in Advance Using Recurrent Neural Network

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