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All Journal Jurnal Meteorologi Klimatologi dan Geofisika Jurnal Meteorologi dan Geofisika
Wulandari, Ayu Vista
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Astronomy Earth & Planetary Sciences Energy Environmental Science Mathematics Physics

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KAJIAN ATMOSFER SAAT MCC (MESOSCALE CONVECTIVE COMPLEX) DI PAPUA BARAT (STUDI KASUS 14 AGUSTUS 2017) Wulandari, Ayu Vista; Swastiko, Wishnu Agum; Silitonga, Andreas Kurniawan; Hariadi, Hariadi
Jurnal Meteorologi Klimatologi dan Geofisika Vol 6 No 1 (2019): Jurnal Meteorologi Klimatologi dan Geofisika
Publisher : Unit Penelitian dan Pengabdian Masyarakat Sekolah Tinggi Meteorologi Klimatologi dan Geofisika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (622.22 KB) | DOI: 10.36754/jmkg.v6i1.112

Abstract

Mesoscale Convective Complex (MCC) merupakan salah satu jenis dari Mesoscale Convective System (MCS). MCC membentuk sistem awan badai yang luas akibat dari banyaknya sel tunggal awan Cumulunimbus yang berkumpul dan tumbuh sehingga disebut gugusan awan konvektif berskala meso. Pada 14 Agustus 2017 terbentuk MCC di wilayah Papua Barat dengan masa hidup dari pukul 14.00 hingga 19.00 UTC. Fenomena MCC tersebut menghasilkan hujan yang berlangsung cukup lama dan bersifat terus-menerus. Penelitian ini bertujuan untuk mengkaji kondisi atmosfer saat terjadinya MCC di Papua Barat pada 14 Agustus 2017. Kajian ini menggunakan data reanalysis dari ECMWF berupa parameter komponen angin meridional dan zonal, vortisitas, dan kelembaban udara. Selain itu, juga perlu dikaji dengan menggunakan citra satelit Himawari 8 dan data Radiosonde. Dari komponen angin meridional dan zonal pada pukul 06.00-24.00 UTC terdapat angin yang cukup kencang di Papua Barat dengan arah pergerakan ke barat laut hingga utara. Berdasarkan kajian sementara, nilai vortisitas lapisan 500 mb pada pukul 06.00-24.00 UTC bernilai negatif yang mengindikasikan adanya sirkulasi siklonik pada troposfer bagian tengah. Kondisi tersebut didukung dengan nilai kelembaban udara yang berkisar antara 70-100% yang menunjukkan kondisi lapisan pada saat kejadian relatif basah. Pada citra satelit Himawari menunjukkan adanya gugusan awan Cumulonimbus dengan suhu puncak -80 0C dan berdiameter sekitar 200 km, yang bercampur dengan awan jenis lain. Sehingga, MCC tersebut tergolong pada MCS kategori beta.
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.
Co-Authors Hariadi Hariadi Ryan, Muhammad Silitonga, Andreas Kurniawan Swastiko, Wishnu Agum Trilaksono, Nurjanna Joko