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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 4 Documents
 1 
Search results for , issue "Vol. 25 No. 2 (2024)" : 4 Documents clear
Improving Short-Term Weather Forecasting using Support Vector Machine Method in North Barito Wulandari, Ayu Vista; Trilaksono, Nurjanna Joko; Ryan, Muhammad
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.v25i2.1096

Abstract

Flooding is a recurring issue in North Barito Regency due to the overflow of the Barito River. Weather forecasts in the region rely mainly on Numerical Weather Prediction (NWP) models, which often fail to capture local details due to their grid-based homogenization. To address this limitation, statistical techniques such as Model Output Statistics (MOS) can enhance NWP outputs by representing local conditions more accurately . MOS establishes statistical relationships between response variables (predictands) and predictor variables derived from NWP outputs, enabling operational applications without the need for advanced instruments. This study utilizes rainfall data from 2021-2022 from the Beringin Meteorological Station in North Barito as the response variable, while data from the Integrating Forecasting System (IFS) model serve as the predictor variables. The Support Vector Machine (SVM) method is employed to identify the relationship between predictor and response variables. By integrating the MOS technique with the SVM method, this research aims to improve the accuracy of weather forecasting, particularly for short-term predictions in North Barito. This approach demonstrates the potential to enhance localized weather predictions by addressing the limitations of conventional NWP models. The results indicate a consistent reduction in RMSE across all experiments conducted. Furthermore, the SVM model showed notable improvements in bias values and exhibited a stronger correlation compared to the original outputs from the IFS model. The percentage improvement (%IM) in rainfall forecasts, following correction using the SVM model, increased by 5.03%. The percentage improvement (%IM) in rainfall forecasts, following correction using the SVM model, increased by 5.03% for use as a predictor variable in the applied SVM method. In contrast, a combination of surface pressure, temperature across various layers, and rainfall proved to be the the most effective input variables for enhancing the accuracy of weather forecasting in North Barito using the SVM model.
Comparative Analysis of Diurnal and Seasonal Variations in Precipitation of Mesoscale Convective System and Non-Mesoscale Convective System over Borneo Island Azka, Mukhamad Adib; 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.v25i2.1101

Abstract

Convective storms, which play a critical role in producing severe weather events, are often associated with mesoscale convective systems (MCS). The most favorable tropical regions for MCS development include the Indonesian Maritime Continent (IMC), with Borneo Island being a prominent area. Borneo Island features unique topography and is influenced by the surrounding oceans, resulting in MCS with the largest average size and most extended lifespan compared to other islands within the IMC. Previous studies on MCS focused on occurrence statistics and case studies. However, analyses distinguishing characteristics of MCS and non-MCS precipitation remain limited over the IMC. This study examines the diurnal and seasonal variations and their respective contributions over Borneo Island. MCS identification and tracking were performed using the Flexible Object Tracker (FLEXTRKR) algorithm. The results indicate that MCS precipitation typically occurs from nighttime to early morning, while non-MCS precipitation primarily occurs during the daytime until the evening. Furthermore, MCS precipitation occurs more frequently over the ocean, while non-MCS precipitation is primarily observed over land. Seasonally, MCS precipitation is most prominent during the December–January–February (DJF) season, particularly over the South China Sea, parts of West Kalimantan, Sarawak, Central Kalimantan, and the Java Sea. Conversely, MCS precipitation is less dominant during the June–July–August (JJA) season. The contribution of precipitation produced by MCS exceeds 50% of the total precipitation, whereas non-MCS precipitation contributes approximately 20–40%. The differences in precipitation produced by MCS and non-MCS clouds will affect for soil water content, vulnerability to hydrometeorological disasters, and further understanding of climate and weather.
Simulasi Numerik Gelombang Tinggi di Sulawesi Utara Saat Terjadi Siklon Tropis Kimi Menggunakan Model Gelombang SWAN Andariwan, Yogi Muhammad; Ningsih, Nining Sari; Kartadikaria, Aditya Rakhmat
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.v25i2.1104

Abstract

This study investigates wind and wind wave conditions in North Sulawesi waters based on their climatological characteristics and a case study of when high waves occurred during Tropical Cyclone (TC) Kimi. Climatological characteristics are calculated by using ERA5 data and the case study is conducted by simulation using Simulating Waves Nearshore (SWAN) wave model. Model verification was performed by comparing the significant wave height (SWH) from SWAN with observation data from wave buoys in Albatross Bay, Townsville, and Emu Park. The statistical results provide biases of -0.11 m, 0.22 m, and 0.16 m, respectively. The Root Mean Square Error (RMSE) values are 0.14 m, 0.28 m, and 0.23 m, and the correlation coefficients are 0.54, 0.8, and 0.95. During the December- February (DJF) period, wind speed peaks in February (3.0-6.5 m/s), and the SWH reaches 0.5-0.8 m. On 17th of January 2021, Manado's coastline experienced high waves, coinciding with the active phase of TC Kimi near northeastern Australia from 15th to 19th of January 2021. As TC Kimi developed, wind speeds in North Sulawesi increased to 7.0–12 m/s, triggering waves reaching 1.0–1.8 meters with an anomaly of 1–1.5 meters. This wave activity experienced a time lag of +19 hours in response to the wind speeds generated by TC Kimi.
EVALUASI METODE KOREKSI BIAS UNTUK PREDIKSI CURAH HUJAN BULANAN ECMWF SEAS5 DI INDONESIA Hutauruk, Rheinhart C H; Rahmanto, Edi; Al Habib, Abdul Hamid; Yoku, Priskila Wilhelmina; Giriharta, I Wayan Gita; Trilaksono, Nurjanna Joko; Hadi, Tri Wahyu
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.v25i2.1124

Abstract

The seasonal rainfall forecast from ECMWF SEAS5 often suffers from biases that reduce its accuracy, limiting its use in applications like water resource management and agricultural planning. This study evaluates the effectiveness of bias correction methods in enhancing the skill of ECMWF SEAS5 seasonal precipitation forecasts in Indonesia. Observational data from 148 BMKG rain gauges and SEAS5 raw output from 2011 to 2020 are used. Three bias correction methods—linear scaling (LS), empirical distribution quantile mapping (EQM), and gamma distribution quantile mapping (GQM)—are applied to the raw model. Model performance is assessed using scatter plots, root mean square error (RMSE), correlation, and Taylor diagrams. The results show LS consistently outperforms EQM and GQM, reducing RMSE from 128 to 102 and improving correlation from 0.57 to 0.65. Additionally, Brier Score (BS) and Relative Operating Characteristic (ROC) analysis highlight significant improvements in probabilistic predictions, especially in areas with high rainfall variability. These findings indicate LS as a particularly effective approach for bias correction, enhancing accuracy and reliability. This study underscores the potential of applying bias correction methods like LS to improve ECMWF SEAS5 forecasts, supporting better decision-making for climate change adaptation and mitigation in Indonesia.

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