95% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Phys.
Sec. Space Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1547350
This article is part of the Research Topic Predicting Near-Earth Space Environment: New Perspective and Capabilities in the AI Age View all articles
Prediction of thermospheric temperature over the South Pole based on two-layer LSTM network
Provisionally accepted- Institute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
This study presents a new two-layer LSTM network-based model, which improves the accuracy of thermospheric temperature over the South Pole simulated by MSIS2.0 model. A dataset is constructed using temperature data measured by the South Pole FPI from 2000 to 2011 along with corresponding temperature derived from MSIS2.0 model, F10.7 and Ap indices, which are the input parameters of the first LSTM network layer. The first LSTM layer combines these inputs into a one-dimensional time series, while the second LSTM layer extracts temporal features from the output of the first layer. The proposed LSTM-based model shows better performance in predicting FPI observations compared to the empirical MSIS2.0 model during both geomagnetically quiet and disturbed periods. For the year 2011, the mean absolute error between the MSIS2.0 model and FPI data is 53.460 K, whereas the LSTM model reduces it to 34.024 K. The euclidean distance analysis also demonstrates better performance of the LSTM model. This study illustrates the potential of applying a two-layer LSTM network to optimize model simulations in upper atmosphere research.
Keywords: Thermospheric temperature, South pole, FPI, LSTM, deep learning
Received: 18 Dec 2024; Accepted: 28 Mar 2025.
Copyright: © 2025 Yang, Huang, Zuo, Zhang, Shao and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yanshi Huang, Institute of Space Science and Applied Technology, Harbin Institute of Technology, Shenzhen, 518000, Guangdong, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.