扰动条件下极区电离层NmF2预测研究

Prediction of NmF2 in the polar ionosphere under disturbance conditions

徐盛 ¹牛月娟 ¹李培豪¹

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作者信息

1. 郑州轻工业大学电子信息学院,河南郑州 450000
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摘要

利用南极中山站和北极特罗姆瑟站的电离层长期观测数据,结合时间序列预测模型 Prophet、LSTM和多项式回归算法,构建了一种新型机器学习组合模型——PLPR,用于预测地磁扰动条件下极区台站电离层未来 1 h的F2 层峰值电子密度(NmF2).结果表明,地磁扰动条件下,PLPR组合模型的预测结果能够较好地反映两个台站NmF2 的日变化趋势,其预测精度在极隙区纬度的中山站要优于极光带纬度的特罗姆瑟站.与国际参考电离层IRI-2016 模型和单一时间序列预测模型Prophet和LSTM相比,PLPR模型具有更好的预测效果.

Abstract

A new prediction model of the ionospheric F2 layer peak electron density(NmF2)during geomagnetic disturbances for 1 hour is developed,using long-term ionospheric observation data from Zhongshan Station in Antarctica and Tromsø Station in Arctic.The machine learning model named PLPR is based on the time series prediction model Prophet,Long Short-Term Memory(LSTM)and polynomial regression.The predic-tions produced by PLPR can better reflect the daily variations of NmF2 at both stations than can those from other models,and the prediction accuracy at Zhongshan Station is better than that at Tromsø Station.Com-pared with the international reference ionosphere IRI-2016 model,as well as the single time series prediction model Prophet and LSTM,the PLPR model demonstrates superior performance.

关键词

极区电离层/地磁扰动/F2层峰值电子密度(NmF2)/电离层参数预报

Key words

polar region ionosphere/geomagnetic disturbances/F2 layer peak electron density(NmF2)/ionospheric parameter prediction

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出版年

2024

极地研究

国家海洋局极地考察办公室中国极地研究中心

极地研究

CSTPCD北大核心

影响因子：0.638

ISSN：1007-7073

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