俄罗斯南方联邦大学物理研究所Olga Maltseva小组，报道了赤道区电离层总电子含量预报精度的纵向依赖性。2024年3月30日出版的《大地测量与地球动力学》发表了这项成果。

据介绍，电离层参数行为的纵向依赖性已成为许多研究工作的热点并发现了显著的变化。这也适用于电离层总电子含量(TEC)的预测，神经网络方法最近已被广泛的使用。然而，这些结果主要是针对有限的一组经线提出的。

本文研究了赤道区TEC预报精度的纵向依赖性。在这种情况下，研究所使用层方法在三个子午线上提供最佳精度：欧洲(30°E)子午线，东南(110°E)子午线和美洲(75°W)子午线。研究人员使用喷气推进实验室2015年全球电离层地图(JPL GIM)，对所考虑站点的结果进行了经度函数分析。这些结果是提前2小时和24小时的预报。在这种情况下，根据度量值可以区分出三组体系结构。第一组包括长短期记忆(LSTM)、门控循环单元(GRU)和时间卷积网络(TCN)模型，作为单向深度学习模型的一部分；第二组以第一组的循环模型为基础，辅以双向算法，将TEC预测精度提高2-3倍。

第三组，包括双向TCN架构(BiTCN)，提供了最高的精度。根据9个赤道台站的数据，在以下指标(平均绝对误差MAE，均方根误差RMSE，平均绝对百分比误差MAPE)下，该体系的TEC预测精度与经度的实际独立性得到了体现：MAE(2h)约为0.2TECU；MAE(24h)约为0.4TECU；除纽埃站RMSE(2h)约为1 TECU外，RMSE(2h)均小于0.5TECU；RMSE(24h)在1.0-1.7TECU之间；除了达尔文站MAPE(2h)<1%外，MAPE(24h)<2%。另外5个站点的数据证实了这一结果，在三条子午线的赤道部分形成了纬向链。研究结果强调，2015年12月19-22日，包括下半年最强磁暴(最小Dst =155nT)在内的扰动条件下，几个台站的TEC观测值与预测值完全吻合。

附：英文原文

Title: Longitudinal dependence of the forecast accuracy of the ionospheric total electron content in the equatorial zone

Author: Olga Maltseva

Issue&Volume: 2024/03/30

Abstract: The longitudinal dependence of the behavior of ionospheric parameters has been the subject of a number of works where significant variations are discovered. This also applies to the prediction of the ionospheric total electron content (TEC), which neural network methods have recently been widely used. However, the results are mainly presented for a limited set of meridians. This paper examines the longitudinal dependence of the TEC forecast accuracy in the equatorial zone. In this case, the methods are used that provided the best accuracy on three meridians: European (30° E), Southeastern (110° E) and American (75° W). Results for the stations considered are analyzed as a function of longitude using the Jet Propulsion Laboratory Global Ionosphere Map (JPL GIM) for 2015. These results are for 2 h ahead and 24 h ahead forecast. It was found that in this case, based on the metric values, three groups of architectures can be distinguished. The first group included long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN) models as a part of unidirectional deep learning models; the second group is based on the recurrent models from the first group, which were supplemented with a bidirectional algorithm, increasing the TEC forecasting accuracy by 2–3 times. The third group, which includes the bidirectional TCN architecture (BiTCN), provided the highest accuracy. For this architecture, according to data obtained for 9 equatorial stations, practical independence of the TEC prediction accuracy from longitude was observed under the following metrics (Mean Absolute Error MAE, Root Mean Square Error RMSE, Mean Absolute Percentage Error MAPE): MAE (2 h) is 0.2 TECU approximately; MAE (24 h) is 0.4 TECU approximately; RMSE (2 h) is less than 0.5 TECU except Niue station (RMSE (2 h) is 1 TECU approximately); RMSE (24 h) is in the range of 1.0–1.7 TECU; MAPE (2 h) < 1% except Darwin station, MAPE (24 h) < 2%. This result was confirmed by data from additional 5 stations that formed latitudinal chains in the equatorial part of the three meridians. The complete correspondence of the observational and predicted TEC values is illustrated using several stations for disturbed conditions on December 19–22, 2015, which included the strongest magnetic storm in the second half of the year (min Dst = 155 nT).

DOI: 10.1016/j.geog.2024.02.001

Source: https://www.sciencedirect.com/science/article/pii/S1674984724000223