Short-term forecasting method suitable for single-station ionized layer TEC

Abstract

The invention discloses a short-term forecasting method suitable for a single-station ionized layer TEC, and relates to the technical field of space weather and environment. The short-term forecastingmethod comprises the following steps: firstly, collecting a TEC data set of an observation point, and carrying out differential and standardized preprocessing on the data; secondly, marking a TEC training data set, and constructing and training a hybrid neural network TEC short-term forecasting system based on a univariate sequence; and finally, inputting a TEC data operation model, outputting asequence, restoring the data by adopting an anti-standardization and contrast technology, outputting a prediction TEC result and evaluating the model performance. According to the invention, a deep learning network ConvLSTM and a BiLSTM are combined; spatial-temporal features and periodic day-by-day change features of the sequence are effectively extracted; an accurate ionospheric refraction correction value can be provided for a space application system user taking radio as a propagation beacon, an effective space abnormal weather monitoring and early warning mechanism can be established, Meanwhile, data support is provided for morphological analysis of space weather and space environment.

Description

Technical field [0001] The present invention relates to the field of space weather and an environment, and more particularly to a short-term forecasting method suitable for single-station ionosphere TEC. Background technique [0002] The ionic layer from the surface of the Earth is contained in a large amount of free electron and ions, which causes the electromagnetic wave in which the electromagnetic wave is changed, and the reflection or refraction occurs, thereby generating additional delays on its spatial information transmission link, reducing remote sensing, Navigate the service performance of various types of space systems such as navigation positioning. In particular, there is a relatively serious consequence of space weather than the spatial weather such as magnetic storm or ionization layer. The ionospheric attach time delay can often be attributed to the total electron concentration total content TEC in the sight direction of the satellite to the sight of the receiver....

AI Technical Summary

Problems solved by technology

However, the impact of these feature quantities on the model output results is not clear, and there is a large correlation between some feature quantities, which will adversely affect the prediction speed and accuracy of the model.

In addition, various parameters that characterize solar activity and geomagnetic activity also need to be updated in real time, and the TEC prediction model thus established is difficult to promote and implement in practical applications

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