Deep Learning Models for Estimation of the SuperDARN Cross Polar Cap Potential

Erxiao Liu,Hongqiao Hu,Jianjun Liu,Lei Qiao

doi:10.1029/2020ea001219

Abstract

AbstractWe present deep learning models for cross polar cap potential (CPCP) by applying multilayer perceptron (MLP) and long short‐term memory (LSTM) networks to estimate CPCP based on Super Dual Auroral Radar Network (SuperDARN) measurements. Three statistical parameters are proposed, which are root‐mean‐square error (RMSE), mean absolute error and linear correlation coefficient (LC), to validate and test the models by measuring their performance on an independent data set that was withheld from the training data set. In addition, we compare the models with previous work. The results show that the deep learning models can successfully reproduce the CPCP values with much lower RMSE (8.41 kV for MLP and 7.20 kV for LSTM) and mean absolute error (7.22 kV for MLP and 6.28 kV for LSTM) and higher LC (0.84 for MLP and 0.90 for LSTM) than do the other models, which have RMSE larger than 10 kV and LC lower than 0.75. The deep learning models can also express the CPCP nonlinear properties (saturation effect) accurately. This study demonstrates that deep learning techniques can enhance the ability to predict CPCP.

Highlights

It is generally believed that there are two types of mechanisms whereby energy and momentum are input into the magnetosphere‐ionosphere system from the solar wind
We present deep learning models for cross polar cap potential (CPCP) by applying multilayer perceptron (MLP) and long short‐term memory (LSTM) networks to estimate CPCP based on Super Dual Auroral Radar Network (SuperDARN) measurements
The results show that the deep learning models can successfully reproduce the CPCP values with much lower root‐mean‐square error (RMSE) (8.41 kV for MLP and 7.20 kV for LSTM) and mean absolute error (7.22 kV for MLP and 6.28 kV for LSTM) and higher linear correlation coefficient (LC) (0.84 for MLP and 0.90 for LSTM) than do the other models, which have RMSE larger than 10 kV and LC lower than 0.75

Summary

Introduction

It is generally believed that there are two types of mechanisms whereby energy and momentum are input into the magnetosphere‐ionosphere system from the solar wind. A new era in ionospheric convection and CPCP studies began owing to the introduction of the Super Dual Auroral Radar Network (SuperDARN) coherent radar network from the end of the twentieth century for measurements of the CPCP directly (Gillies et al, 2011; Grocott et al, 2017; Kustov et al, 1997; Liu et al, 2019; Ruohoniemi & Baker, 1998; Ruohoniemi & Greenwald, 1996, 2005; Shepherd, 2007; Thomas & Shepherd, 2018; Wilder et al, 2011) In these previous studies, the interplanetary electric field (IEF), the Kan‐Lee merging electric field (Ekl) proposed by Kan and Lee (1979), the Alfven Mach number (Ma), the polar cap index (PCN in the Northern Hemisphere), and the AE and Kp indices have all been proposed to study the relationships between the solar wind, magnetosphere, and ionosphere. A more focused study of the CPCP dependence on IMF

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Discussion

Conclusion