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All Journal JTIK (Jurnal Teknik Informatika Kaputama) Newton-Maxwell Journal of Physics
Syahid, Wisnu
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Astronomy Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Mechanical Engineering Physics

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PEMANFAATAN NUMERICAL WEATHER PREDICTION DAN CITRA SATELIT HIMAWARI-9 DALAM ANALISIS KONDISI ATMOSFER SAAT HUJAN LEBAT: (Studi Kasus 14 Maret 2024) Rafi, Rayhan; Syahid, Wisnu; Kaizzi Larasati, Kanaya; Aydin Umardani, Syarif Abdillah; Abigael, Febby Debora; Kristianto, Aries
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 9 No. 1 (2025): Volume 9, Nomor 1, Januari 2025
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v9i1.910

Abstract

Heavy rainfall occurred in the Special Region of Yogyakarta on March 14, 2024. This rainfall event was categorized as extreme weather, as data from the Regional Disaster Management Agency (BPBD) reported damage in 496 affected locations. Heavy rainfall can occur due to atmospheric instability caused by the growth of convective clouds (cumulonimbus). The phenomenon of heavy rainfall was monitored using remote sensing systems in the form of satellites to observe and analyze the event. Yogyakarta's topography explains the use of ECMWF ERA-5 model data to identify wind distribution patterns (streamlines) influenced by westerly winds. The Convective Cloud Overlay (CCO), red-green-blue (RGB), and High-resolution Cloud Analysis Information (HCAI) methods were applied to interpret cumulonimbus cloud development, observed from the formation phase (08:00 UTC) to the dissipation phase (18:00 UTC). Observations indicated a decrease in cloud-top temperature to -80°C at 09:00 UTC, followed by dissipation with a temperature of -20°C at 18:00 UTC. Atmospheric instability indices were analyzed using numerical weather prediction (NWP) methods to obtain quantitative values for indices contributing to heavy rainfall, such as SSI, LI, KI, TT, SWEAT, and CAPE. This study concluded that a "moderate" increase in instability index values explained why convective cloud development occurred.
Prediksi Trajektori dan Intensitas Siklon Tropis Menggunakan Pendekatan Multi-Task Learning Berbasis Recurrent Neural Network Syahid, Wisnu; Putu Aldi Tusan Pratama; Muhammad Nur Rizqi; Yosik Norman
Newton-Maxwell Journal of Physics Vol. 7 No. 1: April 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/nmj.v7i1.47955

Abstract

The limited ability of Numerical Weather Prediction (NWP) models to capture nonlinear dynamics and atmospheric uncertainty remains a major challenge in improving tropical cyclone forecasts, particularly over the eastern Indian Ocean. This study evaluates a Multi-Task Learning approach based on several Recurrent Neural Network (RNN) variants, namely LSTM, BiLSTM, GRU, and BiGRU, to simultaneously predict three key cyclone components: position (latitude and longitude), wind intensity, and cyclone category. Historical IBTrACS data from 2000 to 2025 with a 3-hour temporal resolution are used as model input, employing 48-hour sequences to forecast cyclone conditions at lead times of 12, 24, 48, and 72 hours. The results show that all models achieve stable convergence during training. At a 12-hour lead time, the BiLSTM model delivers the best performance, with a mean position error of 83.53 km and a Hit Rate of 0.966, outperforming the other models. For longer lead times (24–72 hours), the BiGRU model demonstrates the most stable positional accuracy, exhibiting the lowest error degradation as the forecast horizon increases. In addition, wind intensity predictions remain robust, with a Mean Absolute Error (MAE) below 4.6 knots up to 72 hours. These findings highlight the potential of multi-output RNN-based models to support more adaptive and efficient tropical cyclone forecasting systems.
Co-Authors Abigael, Febby Debora Aries Kristianto Aydin Umardani, Syarif Abdillah Kaizzi Larasati, Kanaya Muhammad Nur Rizqi Putu Aldi Tusan Pratama Rafi, Rayhan Yosik Norman