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All Journal El-Jughrafiyah : Jurnal Geografi dan Terapannya BULETIN FISIKA
Setyowati, Pertiwi Risky
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Astronomy Earth & Planetary Sciences Education Energy Environmental Science Materials Science & Nanotechnology Medicine & Pharmacology Physics Social Sciences

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Identifikasi Karakteristik Mesoscale Convective Complex (MCC) serta Analisis Profil Vertikal Atmosfer di Wilayah Laut Banda dan Sekitarnya (Studi Kasus 9 dan 10 Maret 2024) Adzan, Muhizzadin Abdul; Setyowati, Pertiwi Risky; Sari, Nining Baidila; Nugraheni, Imma Redha
EL-JUGHRAFIYAH Vol 5, No 1 (2025): El-Jughrafiyah : February, 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jej.v5i1.35883

Abstract

Indonesia sebagai negara kepulauan di wilayah tropis memiliki dinamika atmosfer yang unik, termasuk pembentukan sistem mesoscale seperti Mesoscale Convective Complex (MCC). Penelitian ini bertujuan mengidentifikasi karakteristik MCC yang terjadi di perairan Laut Banda dan sekitarnya pada 9–10 Maret 2024 berdasarkan kriteria Maddox (1980). Penelitian ini menggunakan pendekatan multidata yang terdiri dari data citra satelit Himawari-9, Automatic Weather Station (AWS), dan model reanalysis ECMWF untuk mengidentifikasi suhu puncak awan, luasan awan, eksentrisitas, durasi kejadian, serta kondisi atmosfer pendukungnya. Hasil menunjukkan bahwa MCC fase matang terjadi selama 14 jam dengan suhu puncak awan ≤ -80°C serta rata-rata luasan awan ± 315.784 km² dan eksentrisitas sekitar 0.951307. Berdasarkan metode Cloud Convective Overlays (CCO), distribusi awan cumulonimbus (Cb) mencapai maksimum pada pukul 20–02 UTC. Analisis curah hujan di Kota Baubau menunjukkan intensitas maksimum 46,1 mm/jam selama kejadian MCC, sementara profil vertikal menunjukkan kondisi atmosfer unstable dengan nilai vertical velocity didominasi negatif sekitar -1.8 Pa/s hingga 0 Pa/s. Kelembaban relatif (RH) pada lapisan 1000–10 mb didominasi ≥ 80% yang mendukung pertumbuhan MCC dan presipitasi intensif. Penelitian ini diharapkan dapat memberikan wawasan yang komprehensif berbasis multidata dalam identifikasi karakteristik MCC dan analisis kondisi atmosfer di wilayah Indonesia.
Nowcasting of Tropical Cyclone Intensity and Trajectory over Southern Indonesia Using a Temporal Convolutional Network Setyowati, Pertiwi Risky; Haryanto, Yosafat Donni; Mulsandi, Adi; Qomariyatuzzamzami, Latifah Nurul
BULETIN FISIKA Vol. 28 No. 1 (2027): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2027.v28.i01.p02

Abstract

Tropical cyclones are extreme weather phenomena that generate strong winds, heavy rainfall, high waves, and infrastructure damage, necessitating rapid and accurate prediction methods to support early warning systems. This study aims to develop a Temporal Convolutional Network (TCN) model to forecast the short-term track and intensity of tropical cyclones at lead times of 6, 12, 18, and 24 hours within World Meteorological Organization (WMO) Regional Association V (RA V), covering waters south of Indonesia. Best-track data from the Australian Bureau of Meteorology (BOM) for 1973–2026 were used, comprising longitude, latitude, central pressure, and maximum wind speed. Cyclones Seroja, Cempaka, Dahlia, Anggrek, and Savanna served as independent test data, while the remaining data were chronologically split into 90% training (1973–2021) and 10% validation (2022–2026). At a 6-hour lead time, the RMSE for longitude, latitude, maximum wind speed, and central pressure were 1.87°, 0.33°, 2.50 knots, and 3.37 hPa, respectively, with R² values of 0.947–0.994. At 24 hours, RMSE increased to 2.70°, 1.25°, 6.76 knots, and 8.95 hPa; R² for longitude and latitude remained high (0.955 and 0.947), while R² for maximum wind speed and central pressure declined to 0.616 and 0.658. Spatially, the model captured the main trajectory pattern for most cyclones, though performance for Cempaka was relatively low. TCN proved more reliable for track than intensity prediction. Incorporating additional environmental variables is recommended to improve intensity prediction accuracy and strengthen Indonesia's tropical cyclone early warning system.
Co-Authors Adi Mulsandi Adzan, Muhizzadin Abdul Haryanto, Yosafat Donni Latifah Nurul Qomariyatuzzamzami Nugraheni, Imma Redha Sari, Nining Baidila