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\begin{document}

\title{Potential of regional ionosphere prediction using a long short-term
memory (LSTM) deep-learning algorithm specialized for geomagnetic storm
period}



\author[1]{JEONGHEON Kim}%
\author[2]{Young-Sil Kwak}%
\author[1]{Yong Ha Kim}%
\author[3]{Su-In Moon}%
\author[1]{Se-Heon Jeong}%
\author[4]{Jongyeon Yun}%
\affil[1]{Chungnam National University}%
\affil[2]{Korea Astronomy and Space Science Institute}%
\affil[3]{Chungnam National Universiry}%
\affil[4]{SELab}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
In our previous study (Moon et al., 2020), we developed a Long Short
Term Memory (LSTM) deep-learning model for geomagnetic quiet days to
perform effective long-term predictions for the regional ionosphere.
However, their model could not predict geomagnetic storm days
effectively at all. This study developed an LSTM model suitable for
geomagnetic storms using the new training data set and re-designing
input parameters and hyper-parameters. We collected 131 days of
geomagnetic storm cases from 1 January 2009 to 31 December 2019, and
obtained the IMF Bz, Dst, Kp, and AE indices related to the geomagnetic
storm corresponding to each storm date. These indices and F2 parameters
of Jeju ionosonde (33.43@\selectlanguage{english}N, 126.30@\selectlanguage{english}E) were used as input parameters for
the LSTM model. To test and verify the predictive performance and the
usability of the LSTM model for geomagnetic storms developed in this
manner, we created and diagnosed the 0.5, 1, 2, 3, 6, 12, and 24-hour
predictive LSTM models. According to the results of this study, the LSTM
storm model for 24-hour developed in this study achieved a predictive
performance during the geomagnetic storms about 32\% (10\%), 34\%
(17\%), and 37\% (5\%) better in RMSE of foF2 (hmF2) than the LSTM quiet
model (Moon et al., 2020), SAMI2, and IRI-2016 models. We propose that
the short-term predictions of less than 3 hours are sufficiently
competitive compared to other traditional ionospheric models. Thus, this
study suggests that our model can be used for short-term prediction and
monitoring of the regional mid-latitude ionosphere.%
\end{abstract}%



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\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

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\emph{Space Weather}

\emph{Supporting Information for}

\textbf{Potential of regional ionosphere prediction using a lont
short-term memory (LSTM) deep-learning algorithm specialized for
geomagnetic storm period}

\textbf{Jeong-Heon Kim\textsuperscript{1}, Young-Sil
Kwak\textsuperscript{1, 2*}, YongHa Kim\textsuperscript{3}, Su-In
Moon\textsuperscript{3}, Se-heon Jeong\textsuperscript{1, 3},}

\textbf{JongYeon Yun\textsuperscript{4}}

\textsuperscript{1} Korea Astronomy and Space Science Institute (KASI),
776 Daedeok-daero, Yuseong-gu Daejeon, South
Korea,\textsuperscript{2}Department of Astronomy and Space Science,
Korea University of Science and Technology (UST), 776 Daedeok-daero,
Yuseong-gu, Daejeon, Korea, \textsuperscript{3} Department of Astronomy,
Space Science and Geology, Chungnam National University (CNU), 99
Daehak-ro, Yuseong-gu, Daejeon, South Korea, \textsuperscript{4} Korea
Space Weather Center (KSWC), 198-6 Gwideok-ro Hallim-eup Jeju, South
Korea

\textbf{Contents of this file}

Figure S1-S8

\textbf{Introduction}

This supporting information provides the characteristics of the
geomagnetic storm events applied to the new LSTM algorithm. The figures
(Figure S1-S8) below show detailed information of 61 trained storm
events as explained in the text. In particular, you can see what type of
ionosphere storm by P/N ratio was in each event.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure S1.} Ionospheric storm types for foF2 of training events
\#1 to \#15. The solid black line is the observed data at the Jeju
location during each event. The symbol lines indicate the 10-day average
reference lines during quiet periods. The red and blue areas indicated a
positive and negative storm. If the P/N ratio is greater than 1 in a
specific event, the positive storm is the dominant one, and if it is
less than 1, the negative storm is the dominant event.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure S2.} The ionospheric storm types for training events \#16
to \#30. Same formats in Figure S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure S3.} The ionospheric storm types for training events \#31
to \#45. Same formats in Figure S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure S4.} The ionospheric storm types for training events \#46
to \#61. Same formats in Figure S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure S5.} Ionospheric storm types for hmF2 of training events
\#1 to \#15. Same formats in Figure S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure S6.} Ionospheric storm types for hmF2 of training events
\#16 to \#30. Same formats in Figure S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure S7.} Ionospheric storm types for hmF2 of training events
\#31 to \#45. Same formats in Figure S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure S8.} Ionospheric storm types for hmF2 of training events
\#46 to \#61. Same formats in Figure S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure-6-traning-test1-plot-index/figure-6-traning-test1-plot-index}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure-2/figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure-8-test-2x3-fof2-hmf2/figure-8-test-2x3-fof2-hmf2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure-5-prediction-results-for-24hrs/figure-5-prediction-results-for-24hrs}

\end{center}
\end{figure}

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