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Integrated analysis of atmospheric and ionospheric precursors using SARIMAX, NARX, and LSTM approaches for the 2024 Mw 7.4 Taiwan earthquake
(2026-08-01)
Tahreem, Azka
;
Shah, Munawar
;
Jamjareegulgarn, Punyawi
Earthquakes are among the most damaging natural hazards, highlighting the need for improved monitoring frameworks and rigorous analysis of potential precursory signals. The Mw 7.4 Taiwan earthquake provides a relevant case for evaluating methodologies to identify and interpret atmospheric and ionospheric anomalies in seismically vulnerable regions. In this study, satellite-based Remote Sensing (RS) products and Global Navigation Satellite System (GNSS) observations are integrated to examine candidate precursors, including Outgoing Longwave Radiation (OLR), Relative Humidity (RH), Air Temperature (AT), Air Pressure (AP), and Total Electron Content (TEC). Using statistical approaches, including the standard deviation (STDEV) method and the Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, together with machine-learning frameworks such as the Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Long Short-Term Memory (LSTM) networks, this study identified synchronized anomalies approximately 5–6 days prior to the event. In addition, geomagnetic perturbations were observed approximately nine days before the event, coinciding with a pronounced geomagnetic storm (Kp > 8; Dst < −120 nT; ap > 225 nT). To limit the influence of background variability and potential false alarms, a historical comparative analysis was performed using atmospheric parameters from the same region and comparable time window across the preceding five years, which further supported the robustness of the observed anomalies. By integrating statistical detection, spatial screening, and time-series forecasting models, this work contributes to a more detailed understanding of atmospheric–ionospheric signals associated with seismic activity and highlights the value of multi-parameter monitoring for seismic hazard assessment and risk-reduction planning.
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Multi-instrument observations of unseasonal post-sunset equatorial plasma bubbles during two moderate geomagnetic storms in May and June 2024 over East/Southeast Asia
(2025-12-01)
Panda, Sampad Kumar
;
Rajana, Siva Sai Kumar
;
Vivek, Chiranjeevi G.
;
Vankadara, Ramkumar
;
Jamjareegulgarn, Punyawi
This study investigated an unseasonal development of post-sunset EPBs in summer solstice period over the East/Southeast Asian longitude region by using multi-instrument observations during two consecutive moderate geomagnetic storm events (16–17 May 2024 and 28–29 June 2024). The results indicate, formation of EPBs during non-climatological plasma bubble season is primarily driven by sustained southward oriented IMF-Bz in the storm main phase, which facilitated the penetration of eastward electric fields into the equatorial ionosphere. These electric fields uplifted the F-region plasma to altitudes favorable for irregularities growth. Also, noteworthy hemispheric asymmetry is noticed in the formation of EPBs, manifesting more intense occurrence in Southern Hemisphere during the geomagnetic storm of 16–17 May 2024 and extended up to ∼20°S magnetic latitude. During the 28–29 June 2024 geomagnetic storm, EPBs are more prominent in the Northern Hemisphere and reached beyond EIA region up to ∼22°N magnetic latitude. In brief, the EPBs developed initially over the 100°E longitude sector, exhibited eastward drift and thereafter extended to 120°E longitude region during the 16–17 May 2024 geomagnetic storm. During the 28–29 June 2024 geomagnetic storm, EPBs formed over the 120°E longitude region and later drifted to 140°E longitude sector. These findings highlight the dominant role of storm-time electrodynamics in triggering EPBs and emphasize the need for continuous regional monitoring of EPBs to mitigate space weather impacts on satellite-based communication and GNSS systems.
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Ionospheric-Thermospheric Responses in South America to the August 2018 Geomagnetic Storm Based on Multiple Observations
(2022-01-01)
Shah, Munawar
;
Abbas, Ayesha
;
Ehsan, Muhsan
;
Aiber, Andres Calabia
;
Adhikari, Binod
The ionospheric storm time responses during August 2018 are investigated over South American region using multiple observables, for example, Global Navigation Satellite System (GNSS) derived vertical total electron content (VTEC) from International GNSS Service, magnetic field data, geomagnetic indices, global ionospheric maps, thermospheric mass density (TMD), and [O/N2] ratio measurement. Strong-ionospheric and upper-atmospheric disturbances affected the ionospheric variables with long duration during the storm recovery phase and following after. First, daytime VTEC (9:00-20:00 UT) presented variations of >15 TECU during days 25 to 30 of August 2018 in low and middle latitudes of South America, this after sudden storm commencement (SSC). Furthermore, nighttime (21:00-24:00 and 00:00-05:00 UT) VTEC presented low values (5<TECU<7) in mid-latitude region after SSC event during the main phase, followed by high values (>8 TECU) in the recovery phase. Second, the ionospheric values during the storm main phase and following after, at low-and mid-latitudes, caused the equatorial ionization anomaly to expand due to prompt penetration electric field. Furthermore, VTEC enhancements are likely to occur few hours after the SSC of 25 August 2018, while enhancements of TMD and [O/N2] ratio started to appear later on 26 and 27 of August 2018.
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Total electron content prediction using singular spectrum analysis and autoregressive moving average approach
(2022-01-01)
Dabbakuti, J. R.K.Kumar
;
Yarrakula, Mallika
;
Panda, Sampad Kumar
;
Jamjareegulgarn, Punyawi
;
Haq, Mohd Anul
Continuous monitoring of ionospheric behavior and subsequent development or improvement of models for the prediction of its parameters with consistent accuracy remains an ongoing challenge. In this sense, an integrated approach by combining the signal extraction technique Singular Spectrum Analysis (SSA) with Autoregressive Moving Average (ARMA) is presented in this work to predict the ionospheric Total Electron Content (TEC) values that are responsible for causing ionospheric delays in the trans-ionospheric signal propagation associated with satellite-based communication, navigation, and timing applications. In general, SSA is a nonparametric spectral estimation procedure that decomposes the signals into interpretable and physically significant components. The observed TEC from two Global Positioning System (GPS) stations across the low latitude Saudi Arabian region are considered during the year 2017 that falls in the descending phase of solar cycle-24. The performance of the proposed hybrid model is evaluated by comparing with the sole estimation from the ARMA model and the observed GPS–TEC dataset for two different geomagnetic conditions: a) the regular geomagnetically quiet period of 15 to 29 December, 2017 (Ap < 24 and Dst > − 30 nT) and b) the geomagnetic storm period from 7 to 9 September, 2017 (Dst min = − 142 nT). The corresponding average Precision, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed SSA–ARMA model predictions are 1.79 TECU, 1.23 TECU, and 13.02%. In contrast, the respective values in the exclusive ARMA model are 2.01 TECU, 1.37 TECU, 14.42% at Oman station. The corresponding values for Magna station are 0.92 TECU, 0.61 TECU, and 10.76% (SSA–ARMA) and 1.01 TECU, 0.75 TECU, and 11.33% (ARMA). The results show an improved computational efficiency with minor improvement in the TEC predictions with the proposed SSA–ARMA method compared to the sole employment of the ARMA model by disregarding the extraneous components.

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