Jamjareegulgarn, Punyawi

Preferred name

Alternative Name

Jamjareegulgarn, P.

Main Affiliation

KMITL Prince of Chumphon Campus

punyawi.ja@kmitl.ac.th

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Deep Machine Learning Based Possible Atmospheric and Ionospheric Precursors of the 2021 Mw 7.1 Japan Earthquake
(2023-04-01)
Draz, Muhammad Umar
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Shah, Munawar
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Jamjareegulgarn, Punyawi
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Shahzad, Rasim
;
Hasan, Ahmad M.
Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (M<inf>w</inf> 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.
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Satellite-derived Spatio-temporal Dynamics of Sea Surface Temperature in the Indonesian and Halmahera Seas During ENSO Events
(2025-03-01)
Lubis, Muhammad Zainuddin
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Purwanto, Budi
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Sobaruddin, Dyan Primana
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Adrianto, Dian
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Dwinovantyo, Angga
Our study investigates the satellite-derived spatio-temporal dynamics of sea surface temperature (SST) in the Indonesian and Halmahera Seas from 2019 to 2021, highlighting its implications for climate change and marine resource management. SST values range from 21.95ºC to 33.50ºC, with pronounced peaks during the East Season (June to October) and lower temperatures in the West Season (January to March). These variations are closely associated with the El Niño-Southern Oscillation (ENSO) and seasonal wind and rainfall patterns. During the East Season, we observed notable upwelling events that indicate significant ecological impacts on fish distribution and fisheries productivity. Our study employed Conductivity-Temperature-Depth (CTD) data to validate satellite observations from the Copernicus Marine Environment Monitoring Service (CMEMS). The results revealed a strong correlation between satellite and observation data (coefficients of 0.91 and 0.93), confirming the reliability of satellite data for monitoring SST in remote marine areas. Our findings underscore the critical importance of continuous SST monitoring for sustainable marine resource management and the integration of satellite data in oceanographic studies. Our study is vital for developing adaptive strategies to address climate variability, particularly El Niño and La Niña events, which significantly influence regional weather patterns and ocean dynamics and ultimately impact global climate systems. Future research should explore the long-term trends of SST toward ongoing climate change and the resilience of marine ecosystems in the face of such variability.
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Improvement of Real-Time Kinematic Positioning Using Kalman Filter-Based Singular Spectrum Analysis During Geomagnetic Storm for Thailand Sector
(2023-01-01)
Srisamoodkham, Worachai
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Ansari, Kutubuddin
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Jamjareegulgarn, Punyawi
The ionospheric error is the largest error source of positioning, especially when electron density becomes high during geomagnetic storms. Real-time kinematic (RTK) positioning during the storm time often has higher fluctuation and noise errors in positioning. Therefore, in this work, a technique based on Kalman filter with implementation of singular spectrum analysis (namely KF-SSA) is anticipated for RTK positioning. The RTK drone data are collected around 50 min of interval (7.37 AM to 8.28 AM) on May 12, 2021, with respect to a base station located at 13.84° N, 100.29° E. The RTK positioning tests have been done to determine the positioning accuracy of the proposed algorithm. The simulated results reveal that SSA implication with KF showed very high-precision estimates and improves the positioning accuracy.
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Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology
(2022-12-01)
Ansari, Kutubuddin
;
Seok, Hong Woo
;
Jamjareegulgarn, Punyawi
The tide gauge measurements from global navigation satellite system reflectometry (GNSS-R) observables are considered to be a promising alternative to the traditional tide gauges in the present days. In the present paper, we deliver a comparative analysis of tide-gauge (TG) measurements retrieved by quasi-zenith satellite system-reflectometry (QZSS-R) and the legacy TG recordings with additional observables from other constellations viz. GPS-R and GLONASS-R. The signal-to-noise ratio data of QZSS (L1, L2, and L5 signals) retrieved at the P109 site of GNSS Earth Observation Network in Japan (37.815° N; 138.281° E; 44.70 m elevation in ellipsoidal height) during 01 October 2019 to 31 December 2019. The results from QZSS observations at L1, L2, and L5 signals show respective correlation coefficients of 0.8712, 0.6998, and 0.8763 with observed TG measurements whereas the corresponding root means square errors were 4.84 cm, 4.26 cm, and 4.24 cm. The QZSS-R signals revealed almost equivalent precise results to that of GPS-R (L1, L2, and L5 signals) and GLONASS-R (L1 and L2 signals). To reconstruct the tidal variability for QZSS-R measurements, a machine learning technique, i.e., kernel extreme learning machine (KELM) is implemented that is based on variational mode decomposition of the parameters. These KELM reconstructed outcomes from QZSS-R L1, L2, and L5 observables provide the respective correlation coefficients of 0.9252, 0.7895, and 0.9146 with TG measurements. The mean errors between the KELM reconstructed outcomes and observed TG measurements for QZSS-R, GPS-R, and GLONASS-R very often lies close to the zero line, confirming that the KELM-based estimates from GNSS-R observations can provide alternative unbiased estimations to the traditional TG measurement. The proposed method seems to be effective, foreseeing a dense tide gauge estimations with the available QZSS-R along with other GNSS-R observables.
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Urban rainfall in the Capitals of Brazil: Variability, trend, and wavelet analysis
(2022-04-01)
Oliveira-Júnior, José Francisco de
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Correia Filho, Washington Luiz Félix
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Monteiro, Lua da Silva
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Shah, Munawar
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Hafeez, Amna
The patterns of urban rainfall in Brazil's capitals are critical, due to population growth and extreme weather. Therefore, the objectives are: i) to identify homogeneous rainfall groups and meteorological systems, ii) to evaluate the trend of the monthly rainfall time series and iii) to apply wavelet analysis to estimate the variance at different frequencies in the rainfall series in the capitals of the Brazil. Monthly rainfall data during 1960–2020 for 27 stations located in the capitals of Brazil were used. The data were flawed, and data imputation (mtsdi package) was applied via Fully Conditional Specification (FCS). Rainfall data were submitted to descriptive, exploratory statistics (boxplot), multivariate analysis (Cluster Analysis - CA) and the Mann-Kendall (MK) test. Seven CA methods (Ward, Single, Complete, Average, McQuity, Median and Centroid) were tested using the cophenetic correlation coefficient (CCC) with a significance level of 5%, the Average method obtained CCC > 0.81 (S). The CA identified three homogeneous regions (G1, G2 and G3) in the capitals of Brazil. The G1 group is formed by the capitals of the Northeast of Brazil (NEB), except for Boa Vista, (North of Brazil - NB). The G2 group is the largest group formed by the capitals of the Midwest (MWB), Southeast (SEB) and South (SB) of Brazil. The G3 group is the smallest group, with the capitals of the NB and some of the NEB. The capitals with the category of significant growth trend were only Porto Alegre and Florianópolis (SB), Vitória (SEB) and Belém (NB). The category of non-significant increase trend prevailed in most capitals of Brazil, with emphasis on the corridor formed between the NB and the Center-South, except for Natal (NEB). The without trend category prevailed in the North, Northeast and Midwest regions of Brazil. Monthly precipitation analyzes for trend detection purposes via Wavelet Analysis showed that ENSO phases are significant in rainfall variability in Brazilian capitals.
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Roles of thermospheric neutral wind and equatorial electrojet in pre-reversal enhancement, deduced from observations in Southeast Asia
(2021-09-01)
Abadi, P.
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Otsuka, Y.
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Liu, Hui Xin
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Hozumi, K.
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Martinigrum, D. R.
Previous studies have proposed that both the thermospheric neutral wind and the equatorial electrojet (EEJ) near sunset play important roles in the pre-reversal enhancement (PRE) mechanism. In this study, we have used observations made in the equatorial region of Southeast Asia during March–April and September–October in 2010–2013 to investigate influences of the eastward neutral wind and the EEJ on the PRE’s strength. Our analysis employs data collected by the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) satellite to determine the zonal (east-west direction) neutral wind at an altitude of ~250 km (bottomside F region) at longitudes of 90°–130°E in the dusk sector. Three ionosondes, at Chumphon (dip lat.: 3.0°N) in Thailand, at Bac Lieu (dip lat.: 1.7°N) in Vietnam, and at Cebu (dip lat.: 3.0°N) in Philippines, provided the data we have used to derive the PRE strength. Data from two magnetometers — at Phuket (dip lat.: 0.1°S) in Thailand and at Kototabang (dip lat.: 10.3°S) in Indonesia — were used to estimate the EEJ strength. Our study is focused particularly on days with magnetically quiet conditions. We have found that the eastward neutral wind and the EEJ are both closely correlated with the PRE; their cross-correlation coefficients with it are, respectively, 0.42 and 0.47. Their relationship with each other is weaker: the cross-correlation coefficient between the eastward neutral wind and the EEJ is just 0.26. Our findings suggest that both the eastward neutral wind and the EEJ near sunset are involved in the PRE mechanism. Based on the weak relationship between these two parameters, however, they appear to be significantly independent of each other. Thus, the wind and the EEJ are likely to be influencing the PRE magnitude independently, their effects balancing each other.
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Singular spectrum analysis of GPS derived ionospheric TEC variations over Nepal during the low solar activity period
(2020-04-01)
Ansari, Kutubuddin
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Panda, Sampad Kumar
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Jamjareegulgarn, Punyawi
Accurate modeling of ionospheric total electron content (TEC) is an important aspect for mitigating the threats of trans-ionospheric delay error in satellite communication, earth observation, space-based navigation, timing applications as well as space weather forecasting services. In recent years, singular spectrum analysis (SSA) has been proved to be a powerful technique giving a relatively accurate estimate in time-series analysis comparable to the contemporary methods. In the current study, the SSA has been implemented on the GPS-derived TEC during the low solar activity year of 2017 over Nepal region which locates itself almost in the vicinity of low-latitudes being sandwiched between India and Tibet, China. The country foresees an explicit investigation and modeling of ionospheric TEC variations and corresponding delay error to precisely accomplish the space-based trans-ionospheric applications. The semi-annual variability of TEC with higher magnitudes during equinoctial seasons and lower values during solstice seasons is clearly noticed in the diurnal plots which are further substantiated by the trajectory matrix of time-series. The decomposed modes in the principal component analysis (PCA) signifies diurnal (first), semidiurnal (second), semiannual (third), monthly (fourth) with higher orders representing associated noise errors in the signals. Correlation coefficients (CC) between the reconstructed and observed time-series demonstrates the SSA method could be a successful tool for forecasting the TEC series over the region. The results are compared with empirical global ionospheric maps (GIMs) and IRI-Plas 2017 models during different seasons, emphasizing the suitability of SSA technique for relatively better precise TEC forecasting over the region.
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Trailing Equatorial Plasma Bubble Occurrences at a Low-Latitude Location through Multi-GNSS Slant TEC Depletions during the Strong Geomagnetic Storms in the Ascending Phase of the 25th Solar Cycle
(2023-10-01)
Vankadara, Ram Kumar
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Jamjareegulgarn, Punyawi
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Seemala, Gopi Krishna
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Siddiqui, Md Irfanul Haque
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Panda, Sampad Kumar
The equatorial plasma bubbles (EPBs) are depleted plasma density regions in the ionosphere occurring during the post-sunset hours, associated with the signal fading and scintillation signatures in the trans-ionospheric radio signals. Severe scintillations may critically affect the performance of dynamic systems relying on global navigation satellite system (GNSS)-based services. Furthermore, the occurrence of scintillations in the equatorial and low latitudes can be triggered or inhibited during space weather events. In the present study, the possible presence of the EPBs during the geomagnetic storm periods under the 25th solar cycle is investigated using the GNSS-derived total electron content (TEC) depletion characteristics at a low-latitude equatorial ionization anomaly location, i.e., KL University, Guntur (Geographic 16°26′N, 80°37′E and dip 22°32′) in India. The detrended TEC with a specific window size is used to capture the characteristic depletion signatures, indicating the possible presence of the EPBs. Moreover, the TEC depletions, amplitude (S4) and phase scintillation (σ<inf>φ</inf>) indices from multi-constellation GNSS signals are probed to verify the vulnerability of the signals towards the scintillation effects over the region. Observations confirm that all GNSS constellations witness TEC depletions between 15:00 UT and 18:00 UT, which is in good agreement with the recorded scintillation indices. We report characteristic depletion depths (22 to 45 TECU) and depletion times (28 to 48 min) across different constellations confirming the triggering of EPBs during the geomagnetic storm event on 23 April 2023. Unlikely, but the other storm events evidently inhibited TEC depletion, confirming suppressed EPBs. The results suggest that TEC depletions from the traditional geodetic GNSS stations could be used to substantiate the EPB characteristics for developing regional as well as global scintillation mitigation strategies.
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Variations in Ionospheric Total Electron Content and Scintillation at GPS stations in Uzbekistan and China during the Annular Solar Eclipse on June 21, 2020
(2025-05-01)
Eshkuvatov, H. E.
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Jamjareegulgarn, Punyawi
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Ahmedov, B. J.
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Tillayev, Y. A.
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Ruziev, Z. J.
This study presents a novel investigation into the distinct ionospheric variations observed over China and Uzbekistan during the annular solar eclipse on June 21, 2020. For the first time, we demonstrate the influence of this celestial event on Total Electron Content (TEC) measurements obtained from GPS satellites. We analyzed fluctuations in TEC and the Ionospheric Scintillation Index (S4) across six strategically selected sites—three in Uzbekistan (MTAL, KIT3, MADK) and three in China (JFNG, LHAZ, BJFS) located near the eclipse path, with obscuration levels of 52%, 57%, 58%, in Uzbekistan and 92%, 94% and 95% in China. Our study involved continuous monitoring of ionospheric parameters over three days, from June 20 to June 22, 2020. Results indicated a significant TEC depletion ranging from 10% to 30% on the day of the eclipse. The analysis reveals that both TEC levels and the S4 scintillation index experienced notable reductions during the event, attributed to the decreased ionizing radiation. These findings enhance our understanding of ionospheric dynamics in response to solar eclipses and have important implications for satellite communication and navigation systems.
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Publisher Correction: Quasi zenith satellite system-reflectometry for sea-level measurement and implication of machine learning methodology (Scientific Reports, (2022), 12, 1, (21445), 10.1038/s41598-022-25994-6)
(2023-12-01)
Ansari, Kutubuddin
;
Seok, Hong Woo
;
Jamjareegulgarn, Punyawi
In the original version of this Article a previous rendition of Figure 6 was published. The original Figure 6 and accompanying legend appear below. The original Article has been corrected.

Jamjareegulgarn, Punyawi

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