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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 7 Documents
 1 
Search results for , issue "Vol. 26 No. 2 (2025)" : 7 Documents clear
Tsunami Evacuation Route Mapping in the Tegalkamulyan Area, Cilacap Regency, Based on the Potential of a South Java Earthquake Saputri, Aliffia Retno Maya; Muqoddas, Muhamad Mahfud; Irayani, Zaroh
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.v26i2.917

Abstract

Cilacap Regency is one of Indonesia’s rapidly developing regions, particularly in the oil mining industry, but it is also highly vulnerable to tsunami hazards due to its location along the South Java subduction zone. A major tsunami generated by a large earthquake in this zone would severely impact coastal areas, including the Tegalkamulyan area. As part of disaster mitigation efforts, this study conducted tsunami evacuation route mapping using tsunami wave propagation modeling based on the Shallow Water Equations (SWE). The simulations employed three hypothetical South Java earthquake sources proposed by the National Earthquake Study Center in 2017, with magnitudes up to Mw 8.7. Among these, Scenario 5 represents the worst-case scenario, producing the largest vertical displacement, with a wave uplift of 11.418 m and a subsidence of −7.476 m. The results show that tsunami waves propagate in all directions, with the fastest arrival time reaching the coast 44 minutes and 1 second after the earthquake. The maximum inundation distance extends up to 12.7 km from the coastline, covering an area of 534.890 km², with a maximum run-up height of 30.847 m. Based on the evacuation route mapping, vertical evacuation directs residents to seek tall buildings or Temporary Evacuation Sites within approximately 6–23 minutes on foot, while horizontal evacuation routes guide evacuees from Temporary Evacuation Sites to Final Evacuation Sites within approximately 13–20 minutes using motorized vehicles at an average speed of 38 km/h.
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.
Network-Based Equity Evaluation of Tsunami Evacuation Access for a Megathrust Scenario in Palabuhanratu: English Sudibyo, Reno; Kurniadi, Anwar; Subiyanto, Adi; Ramadhan, Fajar Gilang
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.v26i2.1196

Abstract

We present a network-based equity evaluation of tsunami evacuation access for a megathrust scenario in Palabuhanratu, quantifying both individual safety attainment and the spatial distribution of access. By overlaying physics-based inundation data with a road graph, we compute multimodal time-to-safety and isochrones, summarizing village-level access through overall reachability (RR), Gini, and hazard-weighted Gini (Gini*) indices. Evacuation time allowances (ETAs) are set at 22, 18, and 15 minutes—validated against site-specific arrival modeling and real-world departure observations from the 2024 Noto event—revealing a critical temporal tipping point. While an ETA of 22 minutes ensures total reachability (RR=1.00) with low inequality, tightening the window to 18 and 15 minutes sharply reduces RR and increases Gini* scores. Furthermore, the addition of an alternative Tsunami Evacuation Area (TEA) at Smile Hill yields localized time savings and minor gains in specific clusters at 22 minutes, yet provides no systemwide benefit at shorter ETAs, indicating that time scarcity dominates access during tight windows. Methodologically, this study employs "beat-the-wave" logic and least-cost routing on OSMnx/NetworkX graphs, offering a reproducible screening tool that integrates access, fairness, and hazard emphasis for TEA design under time-critical evacuation constraints.
the CORRELATION BETWEEN SEA SURFACE TEMPERATURE AND CONVECTIVE CLOUDS IN AMBON ISLANDS Pasaribu, Puput Mustika; Tubalawony, Simon; Masrikat, Julius Anthon Nicolas
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.v26i2.1085

Abstract

Sea surface temperature (SST) plays a key role in modulating tropical convection, yet its influence on the vertical structure of convective clouds in island-based, anti-monsoonal regions remains poorly quantified. This study examines the relationship between SST variability and convective cloud characteristics over the coastal waters of Ambon Island during 2023. Daily SST data were obtained from ERA5 reanalysis, while convective parameters Convective Condensation Level (CCL) and Equilibrium Level (EL) were derived from twice-daily radiosonde observations. Owing to non-normal distributions and serial autocorrelation, Spearman rank correlation was applied with effective sample size (ESS) correction and bootstrap confidence intervals. Results show that SST exhibits a pronounced seasonal cycle primarily governed by monsoonal forcing. SST displays a moderate positive correlation with CCL (ρ = 0.532-0.580) and a consistently strong correlation with EL across all stations (ρ = 0.770-0.778; p_adj < 0.001), indicating a stronger SST control on convective depth than on cloud-base height. Although large-scale climate modes (ENSO, IOD, and MJO) contribute to short-term variability, seasonal monsoonal forcing remains the dominant modulator of SST-convection coupling. These findings represent robust statistical associations and highlight the importance of ocean-atmosphere coupling in regulating convective cloud structure in tropical maritime island environments.
Determining Monsoon Onset Dates in Makassar Using Rainfall Anomalies and Moisture Source Trajectory Analysis (1991–2020) Hutauruk, Rheinhart; Hadi, Tri Wahyu; Muharsyah, Robi; Yolanda, Selvy
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.v26i2.1162

Abstract

Makassar exhibits a typical monsoonal rainfall regime, characterized by a strong annual cycle with peak rainfall occurring in January–February. Understanding the onset of the rainy season in this region is crucial for water resource management and disaster preparedness, yet previous studies have generally relied only on rainfall-based criteria with coarse temporal resolution. This study aims to determine the onset date of the rainy season in Makassar by combining local rainfall anomalies with regional-scale moisture-source trajectories. Daily rainfall data for 1991–2020 were analyzed using harmonic reconstruction to identify the climatological peak of the monsoon season, which then guided the moisture trajectory analysis. The results show that most rainy-season onsets occur in November–December, with high interannual variability influenced by large-scale climate drivers such as ENSO. Moisture transport during the peak rainy months is predominantly derived from the Northern Maritime (58.8%) and Tropical Maritime (40.5%) sources, highlighting the essential role of cross-equatorial water-vapor advection. In addition, changes in zonal wind direction at 850 hPa consistently coincide with the onset, providing an independent dynamical indicator of the transition from dry to wet phase. By explicitly linking rainfall anomalies with the timing of dynamical shifts and dominant moisture pathways, this approach reduces ambiguities commonly found in rainfall-only methods and produces onset estimates that align more closely with regional atmospheric dynamics. Compared to previous rainfall-only approaches, this combined local–regional method provides a more representative onset estimate at daily resolution, offering new insight into the mechanisms of monsoon rainfall in coastal areas of eastern Indonesia.
Comparative Analysis of Weather Radar Signatures of Puting Beliung in Indonesia Kiki; Koesmaryono, Yonny; Hidayat, Rahmat; Sukma Permana, Donaldi; Perdinan
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.v26i2.1180

Abstract

This study presents a comprehensive radar-based analysis of Puting Beliung (PB), Indonesia’s localized tornado phenomenon, using multiple weather radar–derived products. A comparative analysis of ten PB cases was conducted to identify consistent meteorological signatures and variations in tropical storm behavior, motivated by recent observations indicating an increasing frequency of PB events in Indonesia and the need for improved detection methods. Analysis using Rainbow software reveals consistently high reflectivity values ranging from 35 to 60 dBZ, with diverse echo patterns, among which the hook echo is the most dominant. Physical parameters show horizontal wind speeds of 10–30 knots at an altitude of 4 km, horizontal shear of 5–10 m s⁻¹ km⁻¹, and vertical shear of 1–10 m s⁻¹ km⁻¹, while spectral width analysis indicates moderate turbulence with values around 3 m s⁻¹. The Tornadic Vortex Detection (TVD) product identifies potential vortex signatures at six locations, with detected heights ranging from 1.2 to 3.1 km. This study represents the first comprehensive application of multiple radar products for PB characterization in Indonesia and identifies CMAX, HWIND, HSHEAR, and TVD as the most effective products for PB detection and monitoring. These findings provide essential baseline criteria for the development of radar-based early warning systems tailored to Indonesia’s tropical environment, with the potential to reduce the socioeconomic impacts of PB events through improved detection and prediction capabilities.

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