Panpan Ban

Forecasting of low‐latitude storm‐time ionospheric foF2 using support vector machine

An empirical model for predicting low-latitude storm-time ionospheric foF2 is developed using the support vector machine technique. Considering that the ionospheric disturbances are mainly caused by interplanetary disturbances, the solar wind data are introduced as model input, as well as the ionospheric observations of Haikou (HK, with geographic coordinates of 110.3 degrees E and 20.0 degrees N, and geomagnetic latitudes of 8.6 degrees N) and Chongqing (CQ, 106.5 degrees E, 29.6 degrees N, and geomagnetic latitudes of 18.1 degrees N) in China. Data from 45 storms are selected as training samples to construct the model, and other 26 storms are used to validate and evaluate the model. The results indicate that the model proposed here can capture the low-latitude ionospheric disturbances most of the time. Compared with another empirical model, STORM, which has been included in International Reference Ionosphere (IRI) as storm time corrections, our model shows remarkable improvement at least for the given events.

Forecasting the ionospheric f o F 2 in Chinese region by neural network technique

By using artificial neural network (NN) and considering the effects of the solar and geomagnetic activities on the ionosphere, a method for forecasting the ionospheric critical frequency, f<inf>o</inf>F<inf>2</inf>, up to 5 hour ahead at any target geographic location in Chinese region has been proposed. The inputs of the NN are time, day of the year, geographical latitude, solar zenith angle, the twelve recent past observations of f<inf>o</inf>F<inf>2</inf> and the 30-day mean moving values of f<inf>o</inf>F<inf>2</inf> from the target location. The outputs of the NN are F<inf>+1</inf>, F<inf>+2</inf>, F<inf>+3</inf>, F<inf>+4</inf>, F<inf>+5</inf>, representing the values of f<inf>o</inf>F<inf>2</inf> up to 5h ahead. Data from Wulumqi, Changchun, Chongqing and Guangzhou stations spanning the period 1958–1968 are used for training the NN. Historical data at nine different stations in China are used to checkout the network respectively (Not including the training set). The performance of the NN is measured by calculating the root-mean-square error (RMS) difference between the NN outputs and measured station data. The results indicate that the prediction of NN has good agreement with measured data.

On the predictability of foF2 twenty-four hour ahead using a support vector machine technique

This paper proposes a method for forecasting the ionospheric critical frequency, f₀F₂, 24 hour in advance using the support vector machine (SVM) approach. The inputs to the SVM network are the time of day, seasonal information, a 2 month running mean sunspot number (R2), a 3 day running mean of the 3 hour planetary magnetic Ap index, the solar zenith angle, the present value f_oF₂(t), the observation of f₀F₂ at t-23 time, and the previous 30 day running mean of f₀F₂ at t-23 time f_mF₂ (t-23). The output is the predicted f₀F₂ one hour ahead. The network is trained to use the ionospheric sounding data at Guangzhou, Changchun, Manzhouli stations at high and low solar activity. In order to test the predictive ability, the SVM was verified with different data from the training data. The results indicate that the predicted f₀F₂ has good agreement with observed data.

Low-latitude ionospheric perturbations responding to zonal disturbance electric fields

The equatorial electric field plays an important role in low-latitude ionospheric plasma drifts and distributions at both quiet and disturbed time, which leads to complex ionospheric variability near the Equatorial Ionization Anomaly (EIA). In this study, ionospheric foF2 data from two low-latitude ionosondes and equatorial disturbance fields from an empirical model during fifty storms between 1978∼1995 are picked in search of the nonlinear responses of low-latitude ionospheric perturbations to zonal disturbance electric fields. The results show that the responses exhibit remarkable differences versus season and location. This indicates that the low-latitude ionospheric variabilities caused by the disturbance electric field show deep relation to their quiet-time evolutions.