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Adi Mulsandi
Sekolah Tinggi Meteorologi Klimatologi Dan Geofisika
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MODEL ESTIMASI DATA INTENSITAS RADIASI MATAHARI UNTUK WILAYAH BANTEN Munawar Munawar; Adi Mulsandi; Anistia Malinda Hidayat
Jurnal Sains & Teknologi Modifikasi Cuaca Vol. 21 No. 2 (2020): December 2020
Publisher : BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/jstmc.v21i2.4171

Abstract

Data intensitas radiasi matahari (Rs, MJ/m2/day) memiliki peran yang sangat penting dalam pemodelan cuaca dan iklim guna mengkuantifikasi panas yang dipertukarkan antara permukaan dan atmosfer. Namun, keterbatasan jumlah titik pengamatan intensitas radiasi matahari menjadikan pemodelan sebagai alternatif solusi yang relatif mudah dan murah untuk pengambilan data intensitas radiasi. Penelitian ini bertujuan untuk mengevaluasi performa model dalam mengestimasi nilai intensitas radiasi matahari di wilayah penelitian menggunakan dua pendekatan model yang berbeda, yaitu model empiris oleh Keiser, Arkansas (AR) dan model deterministik. Tiga variabel utama cuaca yang digunakan sebagai input data model adalah curah hujan (mm), suhu maksimum (°C), dan suhu minimum (°C). Kedua model tersebut dipilih karena dapat diterapkan dengan hanya melibatkan variabel utama atmosfer yang tersedia dalam waktu yang panjang di lokasi penelitian. Hasil prediksi yang dilakukan dengan model kemudian dibandingkan dengan data reanalisis National Centers for Environmental Prediction (NCEP) pada titik koordinat wilayah Stasiun Klimatologi Pondok Betung. Hasilnya menunjukkan performa model empirik lebih baik dalam menggambarkan variasi temporal dan prediksi variabel intensitas matahari dibandingkan model deterministik. Hal tersebut ditunjukkan dengan nilai korelasi yang cukup baik, yakni mencapai 0,72 (korelasi kuat) dan nilai Root Mean Square Error (RMSE) 2,0. Atas dasar hasil pemodelan yang cukup representatif di lokasi penelitian, analisis secara spasial kemudian diterapkan untuk skala wilayah yang lebih luas, yaitu Provinsi Banten. Berdasarkan tinjauan secara spasial di wilayah kajian, model empirik memiliki performa yang bervariasi di wilayah Provinsi Banten. Hasil prediksi intensitas radiasi matahari di wilayah bagian barat memiliki performa yang lebih baik dibandingkan wilayah bagian timur.  
VARIABILITAS INTERANNUAL HUJAN MONSUN INDONESIA: REVIEW ARTIKEL TENTANG PENGARUH GAYA EKSTERNALNYA Mulsandi, Adi; Koesmaryono, Yonny; Hidayat, Rahmat; Faqih, Akhmad; Sopaheluwakan, Ardhasena
Jurnal Meteorologi dan Geofisika Vol. 24 No. 2 (2023)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v24i2.1049

Abstract

The IMR variability is notorious for its hydrometeorological disasters. This paper examines recent studies on IMR and the main factors controlling its variability. The focus of this study is to investigate the impact of the atmosphere-ocean interaction that acts as the external forcing of IMR in the tropical Indian and Pacific Oceans. Specifically, the study will examine the influence of two climate phenomena, namely the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) and their interdecadal changes associated Pacific Decadal Oscillation (PDO), on the IMR. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. Furthermore, data sets (such as rainfall, wind field, and SST) spanning 1990-2020 were used to verify the key findings. In general, this study concludes that the majority of the authors coincided with the following conclusion: ENSO and IOD events impact IMR by changing its amplitude, duration, intensity, and frequency of mean and extreme rainfall. Additionally, it has been shown that their impacts on IMR are most substantial during the dry seasons, specifically in June, July, and August (JJA), and not as strong as during the wet seasons, specifically in December, January, and February (DJF). Spatially, the effects of ENSO and IOD on IMR variability are clearly found more eastward and westward of the region, respectively. The expansions towards the east and west directions were facilitated by the displacement of the ascending and descending of Walker circulation patterns in the Indonesian region, respectively. Given the interannual fluctuations in IMR, caused mainly by ocean-atmosphere interactions, the knowledge gap of atmospheric factors like the Quasi-Biennial Oscillation (QBO) must be investigated in the future, as suggested by previous research and our preliminary study.
A station-scale modeling framework for heavy rainfall classification in tropical weather using representative machine learning approaches MULSANDI, ADI; MIFTAHUDDIN, MIFTAHUDDIN
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Extreme daily rainfall in rapidly urbanizing tropical cities frequently overwhelms drainage and disrupts critical services, yet station-scale forecasting remains limited by convective variability and sparse observations. This motivates lightweight, interpretable machine-learning tools that operate on routine station data. We propose and evaluate a station-scale framework to classify heavy-rainfall days (50 mm) in a humid tropical setting. Using 1,796 daily observations from the Soekarno-Hatta Meteorological Station (20182022), we engineered lag-informed predictors (e.g., previous-day rainfall, 3-day sums/means) and compared three representative classifiers, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Class imbalance was addressed with class-weighted training, and models were assessed on a held-out test set using precision, recall, F1, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC). LR achieved the highest recall (0.429), indicating moderate sensitivity to rare heavy-rainfall events, whereas RF yielded the best probabilistic discrimination (AUC = 0.619) but failed to flag positives at the default threshold; SVM displayed near-random behavior. Feature analyses highlighted humidity, temperature, and recent rainfall accumulation as the most influential predictors, consistent with tropical convective processes. Despite severe class imbalance, simple, station-based classifiers can extract actionable signals for rare-event screening in data-limited tropical regions. Operational value is likely to improve through probability calibration and threshold tuning, ensemble integration, and spatial generalization to multi-station settings.
Spatio-Temporal Dynamics of Extreme PM2.5 in Indonesia: A Weather-Based Hybrid Modeling Approach Musa, Dwi Indriyati; M. Aryono Adhi; Adi Mulsandi
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1163

Abstract

Air pollution due to fine particulate matter (PM2.5) has emerged as a critical public health concern in tropical megacities, where rapid urbanization often outpaces environmental regulation. Indonesia, characterized by limited air quality monitoring networks and complex meteorological conditions, remains particularly vulnerable to extreme pollution episodes. This study investigates the spatio-temporal dynamics of extreme PM2.5 concentrations in three Indonesian cities—Kemayoran, Semarang, and Malang—selected for their contrasting urban morphologies, topographic settings, and meteorological regimes. Using hourly PM2.5 observations from 2022 to 2024 combined with ERA5 reanalysis data, this study examines the role of atmospheric conditions in governing pollution variability across space and time. A hybrid modeling framework was implemented by integrating multiple linear regression with nonlinear machine learning algorithms, namely Random Forest and XGBoost, to predict hourly PM2.5 concentrations based on meteorological variables including temperature, dew point, wind speed, precipitation, and surface pressure. The results indicate that machine learning models outperform linear methods, with Random Forest providing a strong balance between predictive accuracy and interpretability. Wind speed emerged as the most consistent predictor, followed by dew point and precipitation, exhibiting notable spatial and seasonal variability. Extreme PM2.5 episodes, defined as hourly concentrations exceeding the 95th percentile, were most frequent during dry and transitional seasons under stagnant, humid, and low-rainfall conditions. Kemayoran recorded the highest concentrations, while Malang, despite lower emission levels, demonstrated vulnerability due to weak atmospheric ventilation. Overall, the findings highlight the dominant role of meteorological stagnation in driving PM2.5 extremes and demonstrate the potential of hybrid modeling approaches for developing localized early-warning systems in tropical urban environments.
Spatiotemporal Modeling of Acid Rain Chemistry in Tropical Java Using Mixed-Effects Models: Deposition Patterns and Threshold Exceedance Ahmad Romli, Rita Hidayati; Mochamad Aryono Adhi; Adi Mulsandi
Jurnal Meteorologi dan Geofisika Vol. 26 No. 1 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i1.1170

Abstract

Acid deposition in tropical regions remains under-characterized despite rapid urbanization and increasing atmospheric emissions. This study examines the spatiotemporal variability of rainwater acidity across Java Island, Indonesia, based on five years (2019–2023) of event-based observations from thirteen monitoring stations. Weekly measurements of pH, SO₄²⁻, NO₃⁻, NH₄⁺, Cl⁻, and rainfall volume were analyzed using spatial mapping, seasonal stratification, and linear mixed-effects modeling. The results show that 47% of rain events exhibited pH values below 5.6, while sulfate and nitrate concentrations exceeded critical ecological thresholds in up to 34% of cases, particularly during monsoon transition periods. Although ammonium buffering was observed, it was often insufficient in urban areas. Rainfall volume was significantly associated with ion concentrations; however, episodic acidic deposition remained substantial even during periods of high precipitation. These findings highlight the dual role of tropical rainfall as both a cleansing mechanism and a vector for atmospheric pollutants and provide a scientific basis for incorporating acid deposition into Indonesia’s environmental monitoring and management programs.
Strategi Mitigasi Non-Struktural Bencana Hidrometeorologi Berbasis Edukasi dan Teknologi Informasi bagi Masyarakat Pesisir Sanur I Dewa Gede Loka Maheswara; Intan Bryliana Putri; I Made Prabawa Sandhi Gotama; Putu Aldi Tusan Pratama; Muhammad Jouhar Syah; I Wayan Jyesta Jaya Taruna; Shintia Dwi Cahya; John Pieter S. A; Azra Haiza Zaman; Gede Galang Temuju; Delfiana Yoventa Buti; Kadek Valerina Kitana Sanjaya; Adi Mulsandi; Abraham Frederik Mustamu; Dodo Gunawan
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 2 (2026): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/4ekjje90

Abstract

Kegiatan pengabdian kepada masyarakat merupakan salah satu sarana penerapan ilmu pengetahuan secara langsung untuk memberikan manfaat nyata bagi masyarakat. Dalam rangka Kuliah Kerja Nyata (KKN) STMKG Unit 13, dilaksanakan tiga kegiatan pengabdian di kawasan Pantai Kelurahan Sanur sebagai respons terhadap tingginya kerentanan wilayah pesisir Denpasar Selatan di Provinsi Bali terhadap bencana hidrometeorologi. Kegiatan yang dilaksanakan meliputi sosialisasi hidrometeorologi, pelatihan penggunaan aplikasi SICUPEL, serta pemasangan plang informasi prakiraan cuaca maritim dan tinggi gelombang di wilayah Pantai Semawang. Metode yang diterapkan mengutamakan diskusi interaktif dua arah antara tim penyuluh dan nelayan, yang memungkinkan konfirmasi silang antara informasi prakiraan resmi BMKG dengan pengetahuan empiris nelayan di lapangan. Pendekatan ini terbukti efektif dalam membangun penerimaan peserta terhadap materi yang disampaikan, sekaligus memvalidasi fenomena hidrometeorologi lokal seperti angin kencang dari awan cumulonimbus yang selama ini hanya dipahami secara empiris oleh nelayan. Ketiga kegiatan yang dirancang berkontribusi dalam memperkuat literasi kebencanaan dan kesiapsiagaan masyarakat nelayan pesisir pantai Kelurahan Sanur terhadap potensi bencana hidrometeorologi.
EVALUASI PENGARUH ASIMILASI DATA SATELIT TERHADAP SIMULASI HUJAN ES MENGGUNAKAN WRF-HAILCAST (STUDI KASUS HUJAN ES YOGYAKARTA, 11 MARET 2025) Oktabrian, Krisna Dwi; Mulsandi, Adi; Fadlan, Ahmad; Mulya, Aditya
JOURNAL ONLINE OF PHYSICS Vol. 11 No. 3 (2026): JOP (Journal Online of Physics) Vol 11 No 3
Publisher : Prodi Fisika FST UNJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jop.v11i3.58583

Abstract

Hujan es merupakan fenomena cuaca ekstrem yang dipengaruhi oleh proses konvektif dan mikrofisika awan yang kompleks. Penelitian ini bertujuan mengevaluasi pengaruh asimilasi data satelit menggunakan WRF-DA 3DVAR terhadap simulasi hujan es dengan WRF-HAILCAST pada kejadian hujan es Yogyakarta tanggal 11 Maret 2025. Dua eksperimen dilakukan, yaitu tanpa asimilasi (NODA) dan dengan asimilasi data satelit (DAST). Hasil menunjukkan bahwa asimilasi data satelit meningkatkan pembentukan hidrometeor fase es, ditandai dengan konsentrasi cloud water lebih dari 0,06 g/kg, kristal es lebih dari 0,014 g/kg, dan graupel mencapai 0,175 g/kg. Kedua eksperimen menghasilkan estimasi diameter hujan es 10–30 mm dengan diameter maksimum sekitar 25–30 mm. Meskipun tidak meningkatkan diameter maksimum, DAST mampu merepresentasikan lokasi pembentukan hujan es lebih mendekati distribusi ZHAIL radar. Selain itu, nilai koefisien korelasi reflektivitas meningkat dari 0,48 menjadi 0,56, menunjukkan bahwa asimilasi data satelit meningkatkan representasi struktur badai dan lokasi kejadian hujan es.  
ANALISIS AKURASI ESTIMASI CURAH HUJAN MENGGUNAKAN DATA SATELIT HIMAWARI-9 DENGAN METODE KONVENSIONAL PADA PUNCAK MUSIM HUJAN DI YOGYAKARTA Aslam, Dhaifullah Rafif; Mulya, Aditya; Mulsandi, Adi; Avrionesti , Avrionesti
JOURNAL ONLINE OF PHYSICS Vol. 11 No. 3 (2026): JOP (Journal Online of Physics) Vol 11 No 3
Publisher : Prodi Fisika FST UNJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jop.v11i3.59207

Abstract

Pengukuran curah hujan di Indonesia masih mengalami keterbatasan sebaran alat observasi dikarenakan variabilitas spasial-temporal. Penelitian ini bertujuan mengevaluasi performa metode Convective Stratiform Technique (CST) dan Modified Convective Stratiform Technique (mCST) memanfaatkan data Satelit Himawari-9 untuk estimasi curah hujan di wilayah Daerah Istimewa Yogyakarta selama periode puncak musim hujan yaitu 1 Desember 2025 – 28 Februari 2026. Metode mCST merupakan pengembangan dari algoritma CST konvensional yang dilakukan melalui penyesuaian nilai ambang batas intensitas curah hujan, dan luasan rata-rata area piksel agar lebih fleksibel digunakan di wilayah tropis. Metode estimasi berdasarkan suhu puncak awan yang diverifikasi terhadap data 20 titik observasi BMKG menggunakan Root Mean Square Error (RMSE) dan koefisien korelasi. Hasil yang diperoleh menunjukkan kejadian hujan di DIY mengikuti pola monsunal dengan frekuensi jam hujan tertinggi terjadi pada bulan Februari. Secara statistik, metode mCST terbukti lebih andal dibandingkan CST karena mampu mendeteksi kondisi Tidak Hujan 60,53% serta menekan tingkat overestimate yang signifikan pada kategori hujan ringan. Meskipun kedua metode memiliki kendala akurasi spasial karena kompleksitas topografi lokal, mCST berhasil mengeliminasi korelasi negatif yang dihasilkan metode CST di wilayah pesisir.Sehingga metode mCST menjadi alternatif estimasi curah hujan yang lebih akurat dan sesuai untuk diaplikasikan di wilayah DIY dibandingkan metode CST.
KARAKTERISTIK TURBULENSI ATMOSFER DI WILAYAH BANDARA MINANGKABAU BERDASARKAN DATA RADIOSONDE Elindra, Hani; Fadlan, Ahmad; Mulsandi, Adi; Darmawan, Yahya
JOURNAL ONLINE OF PHYSICS Vol. 11 No. 3 (2026): JOP (Journal Online of Physics) Vol 11 No 3
Publisher : Prodi Fisika FST UNJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/ws46kh95

Abstract

Turbulensi atmosfer merupakan fenomena dinamika atmosfer berskala kecil yang berperan dalam pertukaran momentum dan panas, sekaligus berdampak bagi keselamatan penerbangan, terutama di wilayah tropis dengan topografi kompleks seperti pesisir barat Sumatera. Penelitian ini bertujuan untuk mengetahui profil angin vertikal dan karakteristik turbulensi atmosfer di wilayah Bandara Internasional Minangkabau menggunakan data radiosonde periode Desember 2022–November 2023 pada pukul 00 UTC dan 12 UTC. Karakteristik turbulensi dianalisis melalui frekuensi Brunt–Väisälä (N²), Bilangan Richardson (Ri), dan laju disipasi energi (ε) yang diturunkan dari Metode Analisis Thorpe, dilengkapi kontribusi geseran angin vertikal melalui closure Rmin, serta penyaringan Trend-to-Noise Ratio (TNR) untuk mengurangi deteksi overturning palsu akibat noise instrumen. Profil vertikal dibagi menjadi tiga lapisan: Lower Troposphere Layer (0–3 km), Middle Troposphere Layer (3–10 km), dan Upper Troposphere and Lower Stratosphere Layer (10–18 km). Pada periode penelitian, hasil menunjukkan bahwa kecepatan angin dan vertical wind shear meningkat tajam pada UTLS, dengan puncak kecepatan angin musim Desember-Januari-Februari (DJF) mencapai sekitar 29 m/s pada ketinggian 15–16 km. Lapisan UTLS teridentifikasi sebagai zona dengan kondisi instabilitas dinamis (Ri < 0,25) paling sering terjadi, yang mengindikasikan potensi shear-driven turbulence dengan frekuensi kejadian mencapai sekitar 40–48% terutama pada musim DJF, konsisten dengan nilai turbulensi aktual oleh εtotal serta kategori intensitas Severe yang juga memuncak pada lapisan yang sama. Mekanisme shear-driven teridentifikasi lebih dominan dibandingkan convective/overturning pada hampir seluruh lapisan dan musim, dengan kategori intensitas Severe mencapai sekitar 55–56% pada UTLS musim DJF.
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 A.A. Ketut Agung Cahyawan W Abraham Frederik Mustamu Achmad Zakir Ahmad Muhlis Ahmad Romli, Rita Hidayati Akhmad Faqih Anistia Malinda Hidayat Aries Kristianto Aslam, Dhaifullah Rafif Avrionesti , Avrionesti Azra Haiza Zaman Delfiana Yoventa Buti Dodo Gunawan Elindra, Hani Fadlan, Ahmad Gede Galang Temuju Haryanto, Yosafat Donni Hidayat, Anistia Malinda I Dewa Gede Loka Maheswara I Made Prabawa Sandhi Gotama I Wayan Jyesta Jaya Taruna Ichsan Setiawan Intan Bryliana Putri John Pieter S. A Kadek Valerina Kitana Sanjaya Latifah Nurul Qomariyatuzzamzami Lutfi Fitriano Lutfi Fitriano, Lutfi M. Aryono Adhi Mamenun Mamenun Mamenun, Mamenun Miftahuddin Miftahuddin Mochamad Aryono Adhi Muhammad Jouhar Syah Mulya, Aditya Musa, Dwi Indriyati Oktabrian, Krisna Dwi Perdana, Ilham Fajar Putra Prasetya, Ferdian Adhy Putu Aldi Tusan Pratama Rahmat Hidayat Rahmat Hidayat Rismana, Yosza Indra Saputro, Hari Setyowati, Pertiwi Risky Shintia Dwi Cahya Sopaheluwakan, Ardhasena Yahya Darmawan Yonny Koesmaryono Zakir, Achmad Zakir, Achmad