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INDONESIA
Jurnal Meteorologi dan Geofisika
Published by Pusat Penelitian dan Pengembangan, Badan Meteorologi Klimatologi dan Geofisika
ISSN : 14113082     EISSN : 25275372     DOI : https://doi.org/10.31172/jmg
Core Subject : Science,
Earth & Planetary Sciences Energy Physics
Jurnal Meteorologi dan Geofisika (JMG) is a scientific research journal published by the Research and Development Center of the Meteorology, Climatology, and Geophysics Agency (BMKG) as a means to publish research and development achievements in Meteorology, Climatology, Air Quality and Geophysics.
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 171 Documents
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Simulation of Volcanic Ash Dispersion From Mount Ruang Using the Puff Model (April 29–30, 2024): English Hidayanti, Arifatul; Hardyanto, Wahyu; Munandar, Arif; Wiujianna, Atri; Meinofelia, Erika
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1082

Abstract

This study aims to simulate and predict volcanic ash dispersion from the 29 April 2024 eruption of Mount Ruang by coupling the PUFF model with RGB analysis of Himawari-8 imagery. Driven by meteorological fields from the NOAA Global Forecast System, the PUFF model provides 24-hour forecasts of ash transport by integrating Lagrangian and Eulerian representations of particle motion. For validation, Himawari-8 satellite imagery was processed using the RGB method with IR1, IR2, and IR4 channels to visually detect the spatial distribution of ash clouds, enabling effective differentiation between volcanic ash and meteorological clouds and improving detection accuracy. The model forecasts closely match the timing and distribution patterns observed in the satellite imagery, indicating strong agreement between numerical simulation and remote sensing analysis. Overall, the results demonstrate that the PUFF model delivers reliable short-term guidance on ash dispersion, and the integration of numerical modeling and satellite-based analysis confirms its effectiveness in supporting early-warning capabilities, aviation safety, and volcanic hazard risk mitigation.
PERBANDINGAN METODE ASIMILASI DATA RADIANS SATELIT HIMAWARI-8 UNTUK PREDIKSI HUJAN DI KALIMANTAN TIMUR (STUDI KASUS HUJAN SANGAT LEBAT 2 – 4 JUNI 2019) Huda Abshor Mukhsinin; Trilaksono, Nurjanna Joko
Jurnal Meteorologi dan Geofisika Vol. 25 No. 2 (2024)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v25i2.1097

Abstract

The performance of assimilating Himawari-8 satellite radiance data on a convection-permitting model (CPM) depends on the methods of assimilation. Hence, in this study, we compared the impact of different assimilation methods of Himawari-8 satellite radiance data on a CPM's prediction skills for the case of extreme rainfall in East Kalimantan, June 2nd – 4th, 2019. The study tested four schemes on the Weather Research and Forecasting (WRF) model at 3 km resolution, comparing a scheme without assimilation (NODA) and three schemes with different assimilation methods: 3DVAR, and Hybrid 3DEnVar (HYBRID, and DUALRES). Results showed that assimilation with hybrid 3DEnVar and 3DVAR techniques significantly improved the prediction skill of extreme rain, for instance, a 25% improvement of the true positive rate. The DUALRES scheme excelled in reducing biases in rainfall distribution. It was found that assimilation with the 3DEnVar method, particularly the DUALRES scheme, improved the prediction sensitivity to complex atmospheric dynamics, produced more accurate rainfall distribution and intensity, and improved the diurnal pattern of rainfall in East Kalimantan.
Identifikasi Aktivitas Konveksi Menggunakan Model WRF-ARW dan Indeks Stabilitas Atmosfer di Cekungan Bandung: Identifikasi Aktivitas Konveksi Menggunakan Model WRF-ARW dan Indeks Stabilitas Atmosfer di Cekungan Bandung Al Habib, Abdul Hamid; Hutauruk, Reinhart C. H.; Trilaksono, Nurjanna Joko; Wicaksana, Haryas Subyantara; Choir, Arini Amalia
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1126

Abstract

The Bandung Basin, characterized by complex topography, experiences some of the highest occurrences of heavy rainfall and hail in Indonesia. This study focuses on forecasting convective activity associated with a thunderstorm and heavy rainfall event that triggered flooding in the Pagarsih area, West Java, on 4 October 2022. The spatiotemporal forecast characteristics of convection are assessed using atmospheric stability indices (CAPE, K Index, Total Totals Index, and Lifted Index), together with low-level convergence and updraft fields from the WRF-ARW model, to identify the development of convective cells influenced by the Bandung Basin’s topography. The results reveal a distinct spatiotemporal evolution of convection across mountainous and valley regions. In the early phase, convection first emerged over mountainous areas, driven by a gradual increase in atmospheric instability and low-level convergence, before developing over the valley. The forecasted stability indices show a rising trend 2–5 hours before the onset of thunderstorms and heavy rainfall in the Pagarsih area. At the mature stage, mountain convection was mainly initiated by solar radiation heating, while valley convection was predominantly triggered by mechanical forcing, characterized by a sudden surge in low-level convergence, instability indices, and updraft, leading to more explosive convective development. During dissipation, convection weakened in both regions; however, mountainous areas exhibited stronger convective recovery, indicating higher sensitivity to surface reheating.
Analysis of Volcanic Ash Dispersion from The Mount Agung Eruption Using Himawari-8 Satellite Data: Case Studies from 25 November 2017; 28 June 2018, and 4 July 2018 Pratiwi, Ire; Kharisma, Sulton; Carine P.D.V, Maria
Jurnal Meteorologi dan Geofisika Vol. 25 No. 2 (2024)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v25i2.1143

Abstract

The eruption of Mt. Agung in Bali province over the last two years caused Ngurah Rai International Airport in Bali province, Lombok International Airport in West Nusa Tenggara province, and Notohadinegoro Airport as well as Blimbingsari Airport, both in East Java province, close. The eruptions of volcanoes have a major impact on human activities as airplanes are the fastest and most efficient transportation. Volcanic ash can ruin the jet engine and lead to flameout. Accurate information on the movement and dispersion of volcanic ash was required, considering the location of Mt. Agung is far enough from the affected airports. One of the identifications of volcanic ash was processed using Himawari-8 satellite data with several channels. The satellite data was processed using TVAP (Three Band Volcanic Ash Product), Split Window, and RGB (Red Green Blue) techniques to get the result of the trajectory of volcanic ash dispersion. The result can be used as a reference in airport operations. It showed the movement and dispersion of volcanic ash from Mt. Agung’s eruption to the affected airport area, which resulted in the closure of the airports. The volcanic ash was dispersed in a west-southwest direction, impacting the central and southern regions of Bali Island.
Subsurface Characterization Using Multichannel Analysis of Surface Waves (MASW) for Coastal Abrasion Mitigation and Geotourism Planning at Nangai Beach, North Bengkulu Insani, Redha Radiatul; Hadi, Arif Ismul; Farid, Muchammad; Raihana, Hana; Al Ansory, Andre Rahmat; Muammar, Zaky
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1152

Abstract

Subsurface rock weakness is considered a contributing factor to the high abrasion rate along Nangai Beach, North Bengkulu. This study aims to characterize the subsurface structure using shear-wave velocity (Vs) data derived from Multichannel Analysis of Surface Waves (MASW) at 21 measurement points along the coastline. The data were processed using WinMASW 5.0 Professional software, beginning with dispersion curve picking in the fundamental mode and followed by inversion to generate one-dimensional Vs profiles along with corresponding density and layer thickness. Interpretation of the 1D and 2D Vs profiles indicates that most of the study area is dominated by soft rock formations with generally low Vs30 values, highlighting the area’s vulnerability to coastal abrasion. Recommended mitigation strategies include natural restoration through coastal vegetation, construction of protective structures, and implementation of regional zoning to safeguard tourism and residential areas. Furthermore, active community participation in tourism management is essential to achieve a sustainable balance between environmental preservation and economic development. Overall, the findings provide valuable input for local governments in designing effective abrasion mitigation strategies and sustainable geotourism development plans.
Investigating the Impact of Tropical Cyclones Cempaka and Dahlia on Atmospheric Conditions in Southern Indonesia Vita, Tika Ayunda; Anggoro, Mochammad Donny; Aminuddin, Jamrud
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1161

Abstract

At the end of 2017, the Jakarta Tropical Cyclone Warning Center (TCWC) observed the formation of two tropical cyclones in the southern waters of Indonesia, namely Cempaka and Dahlia, which triggered extreme weather events and caused damage and casualties in several regions. This study aims to identify the developmental stages of tropical cyclones Cempaka and Dahlia, from formation to dissipation, and to examine atmospheric conditions before, during, and after the cyclones. The analysis employs the Dvorak technique based on Himawari-8 satellite infrared imagery to monitor cyclone intensity, supported by ECMWF reanalysis data to evaluate atmospheric parameters in the cyclone growth region. The results indicate that Cempaka and Dahlia reached the Tropical Storm (TS) category on 27 November and 1 December 2017, respectively. During the mature stage, atmospheric conditions were characterized by high relative humidity ranging from 90% to 100%, strong cyclonic circulation with negative vorticity values between −10 × 10⁻⁵ s⁻¹ and −50 × 10⁻⁵ s⁻¹, and lower-level convergence indicated by negative divergence values ranging from −10 × 10⁻⁵ s⁻¹ to −20 × 10⁻⁵ s⁻¹. These conditions support the development of convective clouds and the intensification of the cyclonic systems, providing insight into the role of atmospheric dynamics in the growth of tropical cyclones in the vicinity of Indonesia.
Spatio-Temporal Dynamics of Extreme PM2.5 in Indonesia: A Weather-Based Hybrid Modeling Approach Musa, Dwi Indriyati; M. Aryono Adhi; Adi Mulsandi
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1163

Abstract

Air pollution due to fine particulate matter (PM2.5) has emerged as a critical public health concern in tropical megacities, where rapid urbanization often outpaces environmental regulation. Indonesia, characterized by limited air quality monitoring networks and complex meteorological conditions, remains particularly vulnerable to extreme pollution episodes. This study investigates the spatio-temporal dynamics of extreme PM2.5 concentrations in three Indonesian cities—Kemayoran, Semarang, and Malang—selected for their contrasting urban morphologies, topographic settings, and meteorological regimes. Using hourly PM2.5 observations from 2022 to 2024 combined with ERA5 reanalysis data, this study examines the role of atmospheric conditions in governing pollution variability across space and time. A hybrid modeling framework was implemented by integrating multiple linear regression with nonlinear machine learning algorithms, namely Random Forest and XGBoost, to predict hourly PM2.5 concentrations based on meteorological variables including temperature, dew point, wind speed, precipitation, and surface pressure. The results indicate that machine learning models outperform linear methods, with Random Forest providing a strong balance between predictive accuracy and interpretability. Wind speed emerged as the most consistent predictor, followed by dew point and precipitation, exhibiting notable spatial and seasonal variability. Extreme PM2.5 episodes, defined as hourly concentrations exceeding the 95th percentile, were most frequent during dry and transitional seasons under stagnant, humid, and low-rainfall conditions. Kemayoran recorded the highest concentrations, while Malang, despite lower emission levels, demonstrated vulnerability due to weak atmospheric ventilation. Overall, the findings highlight the dominant role of meteorological stagnation in driving PM2.5 extremes and demonstrate the potential of hybrid modeling approaches for developing localized early-warning systems in tropical urban environments.
Spatiotemporal Modeling of Acid Rain Chemistry in Tropical Java Using Mixed-Effects Models: Deposition Patterns and Threshold Exceedance Ahmad Romli, Rita Hidayati; Mochamad Aryono Adhi; Adi Mulsandi
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1170

Abstract

Acid deposition in tropical regions remains under-characterized despite rapid urbanization and increasing atmospheric emissions. This study examines the spatiotemporal variability of rainwater acidity across Java Island, Indonesia, based on five years (2019–2023) of event-based observations from thirteen monitoring stations. Weekly measurements of pH, SO₄²⁻, NO₃⁻, NH₄⁺, Cl⁻, and rainfall volume were analyzed using spatial mapping, seasonal stratification, and linear mixed-effects modeling. The results show that 47% of rain events exhibited pH values below 5.6, while sulfate and nitrate concentrations exceeded critical ecological thresholds in up to 34% of cases, particularly during monsoon transition periods. Although ammonium buffering was observed, it was often insufficient in urban areas. Rainfall volume was significantly associated with ion concentrations; however, episodic acidic deposition remained substantial even during periods of high precipitation. These findings highlight the dual role of tropical rainfall as both a cleansing mechanism and a vector for atmospheric pollutants and provide a scientific basis for incorporating acid deposition into Indonesia’s environmental monitoring and management programs.
Performance Evaluation of Automated and Manual Seismic Phase Picking for Rapid Earthquake Parameter Determination in the Indonesian BMKG Network Hielmy, Rayhan Irfan; Pranata, Bayu; Wijayanto; Daryono
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1189

Abstract

Indonesia is situated at the intersection of three major tectonic plates, resulting in high seismic activity and significant earthquake vulnerability.1 Rapidly determining initial earthquake parameters—including origin time, epicenter location, depth, and magnitude—is critical for effective early warning systems. This study evaluates the reliability of automated versus fast manual picking (<3 minutes, S-wave-based) by comparing their performance against final validated results. Utilizing data from the BMKG SeisComP system for the period of May 18, 2024, to May 17, 2025, the study analyzed 2,790 seismic events across Indonesia, including low-seismicity regions such as Kalimantan. Performance was assessed across six key parameters (depth, origin time, RMS, azimuth gap, magnitude, and epicenter) using a numerical scoring system (0–100) based on deviation from validated data. The results indicate that while automated picking processed a significantly higher volume of events (1,857 events; 66.6%) compared to manual picking (327 events; 11.7%) within the target timeframe, manual picking achieved a superior 'good' quality rating (score 75–100) at 96.9%, compared to 88.5% for automated methods. Nevertheless, automated picking remains the preferred method for rapid dissemination (<3 minutes) due to its operational speed. Furthermore, the study establishes regional thresholds for the minimum seismic phases required for reliable automated picking, ranging from 8 to 16 phases depending on the region, with a national average of 15 phases.
Leveraging Sequential and Attention-Based Deep Learning Architectures for Accurate Daily Rainfall Prediction in Jakarta, Indonesia, Using Atmospheric Predictors Hardiano, Akhdan Fadhilah Yaskur; Setiawan, Sonni
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1194

Abstract

In this study, we developed and evaluated daily rainfall prediction models utilizing deep learning architectures, specifically comparing Long Short-Term Memory (LSTM) and Transformer models integrated with various atmospheric predictors. Our results indicate that the LSTM achieved superior accuracy at short-term lags—reaching an R² of 0.94 and an RMSE as low as 4.81 at lag-3—whereas the Transformer demonstrated higher consistency across all lags, maintaining stable R² values between 0.87 and 0.88. The application of a 5-day smoothing pre-processing step significantly enhanced prediction quality for both architectures by mitigating high-frequency noise, a benefit particularly pronounced in the LSTM due to its sensitivity to data fluctuations. Notably, the inclusion of tropical wave variables did not substantially improve model performance and, in some instances, reduced LSTM accuracy at longer lags by increasing input complexity; conversely, the Transformer remained robust to these additional variables. Among the predictors evaluated, Vertically Integrated Moisture Flux Divergence (VIMD) emerged as the most critical feature, underscoring its physical relevance to precipitation processes in convective and monsoonal regions. These findings suggest that while LSTMs excel at capturing immediate temporal dynamics, Transformers provide a more stable framework for longer-range rainfall forecasting in Jakarta.

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