A Kriging proxy model construction method for global ionospheric electron content

Abstract

The invention provides a Kriging proxy model algorithm of the global ionospheric electron content. The algorithm utilizes the current ionospheric space physics research results and combines the long-term stable ionospheric electronic content data obtained by the observation of the global satellite navigation system. The gold agent model is used for model construction and ionospheric prediction to improve the accuracy of ionospheric prediction. At the same time, the parallel computing method is used for the calculation of the large amount of global grid data. Through the above method, the electronic content of the global ionosphere in the prior art is compensated. Many shortcomings of modeling and forecasting methods.

Description

technical field [0001] The invention relates to the technical field of ionospheric detection and communication, in particular to a kriging proxy model algorithm for establishing global ionospheric electron content based on space weather index. Background technique [0002] As one of the important components of atmospheric space weather, the ionosphere has an extremely important influence, especially in the field of radio signal transmission, because when electromagnetic wave signals propagate in the ionosphere, they will be affected by the complex electromagnetic environment in the ionosphere, causing their propagation The speed and propagation path change, and the degree of influence of the ionosphere on electromagnetic waves has an extremely important relationship with the degree of ionization of the ionosphere, the electron density in the ionosphere, and the signal frequency of electromagnetic waves. Therefore, with the deepening of radio research, ionosphere research is a...

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Problems solved by technology

The former has better prediction results when the space weather is stable, but it cannot make full use of the historical observation data of the ionospheric electron content, resulting in a waste of data resources. At the same time, it cannot respond when the weather changes drastically, resulting in poor prediction results; the advantages of deep learning methods It can make full use of historical observation data for trend analysis and extraction, but the deep learning method has high requirements for hardware equipment and the data training cycle is long

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