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All Journal Jurnal Meteorologi Klimatologi dan Geofisika GEOGRAPHIA: Jurnal Pendidikan dan Penelitian Geografi Buletin GAW Bariri ( BGB)
Saragih, Immanuel Jhonson Arizona
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Agriculture, Biological Sciences & Forestry Astronomy Computer Science & IT Earth & Planetary Sciences Education Environmental Science Mathematics Physics Social Sciences Other

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PREDIKSI CURAH HUJAN BULANAN DI DELI SERDANG MENGGUNAKAN PERSAMAAN REGRESI DENGAN PREDIKTOR DATA SUHU DAN KELEMBAPAN UDARA Saragih, Immanuel Jhonson Arizona; Inlim Rumahorbo; Ricko Yudistira; Dedi Sucahyono
Jurnal Meteorologi Klimatologi dan Geofisika Vol 7 No 2 (2020): 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 | DOI: 10.36754/jmkg.v7i2.192

Abstract

A simulation of monthly rainfall prediction (RR) using a regression equation with a predictor of air temperature (T) and humidity (RH) has been tried at Deli Serdang Climatology Station, North Sumatra. The RR, T and RH data for 30 years (1989-2018) were obtained from the Deli Serdang Climatology Station. This prediction simulation for total monthly rainfall uses simple linear regression and multiple linear regression. Evaluation is done by comparing and calculating the Pearson correlation value and the deviation of the predicted total monthly rainfall against the actual total rainfall. The results of data processing showed that the simulation of the total monthly rainfall forecast for 2019 in the Deli Serdang area obtained a correlation value of r = 0,72 and an average of RMSE = 77,42 mm / month using air temperature predictors, obtained correlation values r = 0,73 and RMSE = 77,13 mm / month using the air humidity predictor, and the correlation value r = 0,70 with RMSE = 80,53 mm / month using a predictor of air temperature and air humidity as well.
Identifikasi Potensi Turbelensi di Wilayah Penerbangan Indonesia Berdasarkan Analisis Richardson Number (Ri) Menggunakan Data ECMWF ERA5 (Studi Kasus Tahun 2022) Riswanto, Rafli Muhammad Haris; Mustofa, Musa Ali; Saragih, Immanuel Jhonson Arizona
Buletin GAW Bariri (BGB) Vol 5 No 2 (2024): BULETIN GAW BARIRI
Publisher : Stasiun Pemantau Atmosfer Global Lore Lindu Bariri - Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/bgb.v5i2.126

Abstract

Turbulence is an atmospheric phenomenom that that significanly impacts aviation safety, particularly in Indonesia, which features complex tropical weather conditions and dense air traffic. The identification and mapping of turbulence potential are crucial for improving aviation safety and operational efficiency. This study aims to map the turbulence potential in Indonesian flight regions using the Richardson Number (Ri) for the year 2022. The data utilized in this research are sourced from the European Centre for Medium - Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5) and radiosonde observation. ERA5 data were employed to calculated Ri, which can detect turbulence potential based on meteorological parameters such as wind speed and temperature. Ri calculations were aggregated monthly and annually for temporal and spatial analysis. Validation was conducted using radiosone data to verify turbulence events and meteorological parameters across various atmospheric levels. Spatial observations across Indonesia revealed significant turbulence events on January 18, July 2, and December 26, 2022, with notable daily fluctuations. Geographically, regions along the sothern coasts of Java and Sumatera, as well as certain areas in Kalimantan and Papua, exhibited higher frequencies of turbulence. These patterns are influenced by seasonal weather condition and complex local geopraphical features. Data validation demonstrated good agreement between ERA5 data and radiosonde measurements for temperature parameters, although significant differences were found in the zonal and meridional wind components. This indicates that the reialbility of the generated turbulence potential maps could be enhanced by integratting local data and further refining validation methods for wind components. These findings highlight the importance of ultilizing accurate local data to effectively predict and mitigate the impacts of turbulence on aviation in Indonesia
Daily rainfall forecast based on multi-station observation data in Medan City Sinaga, Nora Valencia; Aisjah, Aulia Siti; Arifin, Syamsul; Saragih, Immanuel Jhonson Arizona
GEOGRAPHIA : Jurnal Pendidikan dan Penelitian Geografi Vol. 6 No. 2 (2025): Desember
Publisher : Jurusan Pendidikan Geografi Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/82y51t67

Abstract

This study develops a multi-horizon daily rainfall forecasting model using the Long Short-Term Memory (LSTM) deep learning method, based on multi-station Automatic Weather Station (AWS) data in Medan City. Ten-minute AWS data from multiple stations (2021–2024) were merged and time-synchronized (UTC), followed by a quality control process including physical range checks, rate-of-change filtering, inter-variable consistency checks, and spike detection. Missing values were addressed using linear interpolation for short gaps and Multiple Imputation by Chained Equations (MICE) for longer gaps. Predictor features were constructed from weather parameters (temperature, humidity, pressure, wind, radiation), aggregated to an hourly scale, and reshaped into input time windows for LSTM. A two-layer LSTM model (128–64 units, 0.3 dropout, Adam optimizer) was trained to predict daily rainfall up to five days ahead. Evaluation metrics, including RMSE, MAE, POD, FAR, and CSI (with rainfall threshold ≥1 mm/day), indicated strong model performance: for instance, RMSE was below 10 mm/day for 1–3 day horizons, with POD above 0.80 and FAR below 0.20. The LSTM model outperformed conventional statistical models, yielding an accuracy improvement of approximately 30–40%. These findings highlight the potential of high-resolution AWS-based automatic forecasting systems to support hydrometeorological disaster mitigation in tropical urban areas.
Co-Authors Aulia Siti Aisjah Dedi Sucahyono Inlim Rumahorbo Mustofa, Musa Ali Ricko Yudistira Riswanto, Rafli Muhammad Haris Sinaga, Nora Valencia Syamsul Arifin