Ionospheric index of solar activity based on the data of measurements of the spacecraft signals characteristics

Ortikov, M. Yu.; Shemelov, V.A.; Shishigin, I.V.; Troitsky, B.V.

doi:10.1016/j.jastp.2003.09.005

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…Besides various attempts to retrieve solar proxies directly from solar data, several ionospheric indices have been successively introduced since the 1950s [18,[24][25][26][27]. These indices are deduced from ionospheric data measured at single stations or over regions.…”

Section: Solar Radiation Observations and Solar Proxiesmentioning

confidence: 99%

Solar activity effects of the ionosphere: A brief review

et al. 2011

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Solar radiation, which varies over multiple temporal scales, modulates remarkably the evolution of the ionosphere. The solar activity dependence of the ionosphere is a key and fundamental issue in ionospheric physics, providing information essential to understanding the variations in the ionosphere and its processes. Selected recent studies on solar activity effects of the ionosphere are briefly reviewed in this report. This report focuses on (1) observations of solar irradiance at X-ray and extreme ultraviolet wavelengths and the outstanding problems of solar proxies, in the view of ionospheric studies, (2) new findings and improved representations of the features of the solar activity dependence of ionospheric key parameters and the corresponding physical processes, (3) possible phenomena in the ionosphere under extremely high and low solar activity conditions that are unique, as indicated by historical solar datasets and the deep solar minimum of solar cycle 23/24, and (4) statistical studies and model simulations of the ionosphere response to solar flares. The above-mentioned studies provide new clues for comprehensively explaining basic processes in the ionosphere and improving the prediction capability of ionospheric models and related applications.

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Section: Solar Radiation Observations and Solar Proxiesmentioning

confidence: 99%

Solar activity effects of the ionosphere: A brief review

et al. 2011

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“…As a proxy for solar extreme ultraviolet (EUV) radiation, F10.7 is always an input parameter for calculating the atmospheric density for orbit determination in aerospace and predicting the influence of the ionosphere on communication. For example, F10.7 is used as a control parameter of an ionospheric model in calculating the variations of radio-signal characteristics (Ortikov et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Forecast daily indices of solar activity, F10.7, using support vector regression method

Huang

Liu

Wang

2009

Res. Astron. Astrophys.

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The 10.7 cm solar radio flux (F10.7), the value of the solar radio emission flux density at a wavelength of 10.7 cm, is a useful index of solar activity as a proxy for solar extreme ultraviolet radiation. It is meaningful and important to predict F10.7 values accurately for both long-term (months-years) and short-term (days) forecasting, which are often used as inputs in space weather models. This study applies a novel neural network technique, support vector regression (SVR), to forecasting daily values of F10.7. The aim of this study is to examine the feasibility of SVR in short-term F10.7 forecasting. The approach, based on SVR, reduces the dimension of feature space in the training process by using a kernel-based learning algorithm. Thus, the complexity of the calculation becomes lower and a small amount of training data will be sufficient. The time series of F10.7 from 2002 to 2006 are employed as the data sets. The performance of the approach is estimated by calculating the norm mean square error and mean absolute percentage error. It is shown that our approach can perform well by using fewer training data points than the traditional neural network.

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“…The F10.7 index is an important parameter in atmospheric density calculations for spacecraft orbit forecasting and in ionospheric forecasts affecting communications. For example, F10.7 is used for a control parameter in ionospheric models to calculate the variation of radio signal properties (Ortikov et al,2003).F10.7 is also widely used for satellite, navigation, communication and terrestrial climate (Huang et al, 2009;Yaya et al, 2017). Therefore, accurate forecasting of F10.7 is not only of great value for the conduct of applicaion, but is also of comparative importance in the scientific study of space weather forecasting.…”

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confidence: 99%

Deep Temporal Convolutional Networks for F10.7 Radiation Flux Short-Term Forecasting

Wang

Zhang

et al. 2023

Preprint

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Abstract. F10.7, the solar radiation flux at a wavelength of 10.7 cm (F10.7), is often used as an important parameter input in various space weather models and is also a key parameter for measuring the strength of solar activity levels. Therefore, it is valuable to study and forecast F10.7. In this paper, the temporal convolutional network (TCN) approach in deep learning is used to predict the daily value of F10.7. The F10.7 series from 1957 to 2019 are used, which the datasets from 1957 to 2008 are used for training and the datasets from 2009 to 2019 are used for testing. The results show that the TCN model of prediction F10.7 with a root mean square error (RMSE) from 5.03 to 5.44sfu and correlation coefficients (R) as high as 0.98 during solar cycle 24. The overall accuracy of the TCN forecasts is better than those of the widely used autoregressive (AR) models and the results of the US Space Weather Prediction Center (SWPC) forecasts especially for 2 and 3 days ahead. In addition ,the TCN model is slightly better than other neural network models like backward propagation network (BP) and long short-term memory network (LSTM) in terms of the solar radiation flux F10.7 forecast. The TCN model predicted F10.7 with a lower root mean square error, a higher correlation coefficient and the better overall model prediction.

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Ionospheric index of solar activity based on the data of measurements of the spacecraft signals characteristics

Cited by 11 publications

References 6 publications

Solar activity effects of the ionosphere: A brief review

Solar activity effects of the ionosphere: A brief review

Forecast daily indices of solar activity, F10.7, using support vector regression method

Deep Temporal Convolutional Networks for F10.7 Radiation Flux Short-Term Forecasting

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