• Journal of Astronomy and Space Sciences
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• v.33 no.4
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• pp.279-285
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• 2016

The space radiation dose over air routes including polar routes should be carefully considered, especially when space weather shows sudden disturbances such as coronal mass ejections (CMEs), flares, and accompanying solar energetic particle events. We recently established a heliocentric potential (HCP) prediction model for real-time operation of the CARI-6 and CARI-6M programs. Specifically, the HCP value is used as a critical input value in the CARI-6/6M programs, which estimate the aviation route dose based on the effective dose rate. The CARI-6/6M approach is the most widely used technique, and the programs can be obtained from the U.S. Federal Aviation Administration (FAA). However, HCP values are given at a one month delay on the FAA official webpage, which makes it difficult to obtain real-time information on the aviation route dose. In order to overcome this critical limitation regarding the time delay for space weather customers, we developed a HCP prediction model based on sunspot number variations (Hwang et al. 2015). In this paper, we focus on improvements to our HCP prediction model and update it with neutron monitoring data. We found that the most accurate method to derive the HCP value involves (1) real-time daily sunspot assessments, (2) predictions of the daily HCP by our prediction algorithm, and (3) calculations of the resultant daily effective dose rate. Additionally, we also derived the HCP prediction algorithm in this paper by using ground neutron counts. With the compensation stemming from the use of ground neutron count data, the newly developed HCP prediction model was improved.

• Journal of Astronomy and Space Sciences
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• v.26 no.4
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• pp.479-486
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• 2009

We studied the solar proton events (SPEs) associated with coronal mass ejections (CMEs) during the solar cycle 23 (1997-2006). Using 63 SPE dataset, we investigated the relationship among SPE, flare, and CME, and found that (1) SPE rise time and duration time depend on CME speed and the earthward direction parameter of the CME, and (2) the SPE peak intensity depends on CME speed and X-ray Flare intensity. While inspecting the relation between SPE peak intensity and the direction parameter, we found there are two groups: first group consists of large six SPEs (> 10,000 pfu at > 10 MeV proton channel of GOES satellite) and shows strong correlation (cc = 0.65) between SPE peak intensity and CME direction parameter. The second group has a weak intensity and shows poor correlation between SPE peak intensity and the direction parameter (cc = 0.01). By investigating characteristics of the first group, we found that all the SPEs are associated with very fast halo CME (> 1400km/s) and also they are mostly located at central region and within ${\pm}20^{\circ}$ latitude and ${\pm}30^{\circ}$ longitude strip.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.

• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.419-430
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• 2005

We have carried out a statistical analysis on solar wind dynamic pressure pulses during geomagnetic storms. The Dst index was used to identify 111 geomagnetic storms that occurred in the time interval from 1997 through 2001. We have selected only the events having the minimum Dst value less than -50 nT. In order to identify the pressure impact precisely, we have used the horizontal component data of the magnetic field H (northward) at low latitudes as well as the solar wind pressure data themselves. Our analysis leads to the following results: (1) The enhancement of H due to a pressure pulse tends to be proportional to the magnitude of minimum Dst value; (2) The occurrence frequency of pressure pulses also increases with storm intensity. (3) For about $30\%$ of our storms, the occurrence frequency of pressure pulses is greater than $0.4\#/hr$, implying that to. those storms the pressure pulses occur more frequently than do periodic substorms with an average substorm duration of 2.5 hrs. In order to understand the origin of these pressure pulses, we have first examined responsible storm drivers. It turns out that $65\%$ of the studied storms we driven by coronal mass ejections (CMEs) while others are associated with corotating interaction regions $(6.3\%)$ or Type II bursts $(7.2\%)$. Out of the storms that are driven by CMEs, over $70\%$ show that the main phase interval overlaps with the sheath, namely, the region between CME body and the shock, and with the leading region of a CME. This suggests that the origin of the frequent pressure pulses is often due to density fluctuations in the sheath region and the leading edge of the CME body.