Pemanfaatan Metode Merged-IMSRA dalam Peningkatan Estimasi Curah Hujan Berbasis Satelit saat Kejadian Mesoscale Convective System (MCS) di Bali
-
DOI:
https://doi.org/10.29303/goescienceed.v7i3.2314Keywords:
Bali, Mesoscale Convective System, Merged-IMSRA, Banjir, Satelit.Abstract
Mesoscale Convective System (MCS) is one of the primary causes of extreme rainfall events in tropical regions, as occurred in Bali on 8–10 September 2025, which resulted in a significant hydrometeorological disaster. This study aims to evaluate the capability of the Merged Indian National Satellite System Multi-Spectral Rainfall Algorithm (M-IMSRA) in estimating the spatial and temporal distribution of rainfall during the MCS event. M-IMSRA was developed through several sequential stages: IMSRA (IMR), polynomial bias correction (IMC), event-based orographic correction using GSMaP MVK ratios, cloud growth correction, and assimilation of 31 BMKG AWS/ARG observation points using the Cressman scheme. Validation was conducted using the Pearson correlation coefficient (r), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and bias. The results show that M-IMSRA accurately mapped the spatial evolution of the MCS, with extreme rainfall accumulation (210–270 mm) concentrated in the central-eastern mountainous region of Bali during the peak phase, consistent with orographic lifting mechanisms. Statistical validation indicated moderate to very high correlation across all event phases (r = 0.663–0.941), accompanied by low RMSE and MAE values and a small, consistent bias (−0.08 to −1.14 mm). It is concluded that M-IMSRA is an accurate rainfall estimation method with strong potential for operational implementation in hydrometeorological disaster monitoring and early warning systems in Indonesian archipelagic regions with limited surface observation networks.
References
Abdillah, L. A., Mappanyompa, Sabtohadi, J., Isma, A., Effiyaldi, Mulyodiputro, M. D., . . . Hasanuddin, R. (2024). Metode penelitian kuantitatif: Konsep dan aplikasi. Sumedang: Mega Press Nusantara.
Adiningsih, E. S., Sofan, P., & Prasasti, I. (2016). Pemanfaatan teknologi penginderaan jauh untuk monitoring kejadian iklim ekstrem di Indonesia. Jurnal Sumberdaya Lahan, 10(2).
Badan Nasional Penanggulangan Bencana. (2025). Info bencana: Data dan informasi kebencanaan bulanan teraktual. 6(9).
Djollong, A. F. (2014). Tehnik pelaksanaan penelitian kuantitatif. Istiqra: Jurnal Pendidikan dan Pemikiran Islam, 2(1).
Gairola, R. M., Mahesh, C., & Bushair, M. T. (2016). Algorithm theoretical basis document–rainfall estimation (modified) (Report No. SAC/EPSA/AOSG/SR/12/2016). Space Applications Centre.
Gairola, R. M., Mishra, A., Prakash, S., & Mahesh, C. (2010). Development of INSAT multi-spectral rainfall algorithm (IMSRA) for monitoring rainfall events over India using Kalpana-IR and TRMM precipitation radar observations (Report No. SAC/EPSA/AOSG/INSAT/SR-39/2010). Space Applications Centre.
Gao, R., Li, L., Wang, Y., Li, W., Yun, Z., & Gai, Y. (2024). Improvements and limitations of the latest version 8 of GSMaP compared with its former version 7 and IMERG V06 at multiple spatio-temporal scales in mainland China. Atmospheric Research, 308, 107517.
Khan, A. W., Mahesh, C., Bushair, M. T., & Gairola, R. M. (2021). Estimation and evaluation of rainfall from INSAT-3D improved IMSRA algorithm during 2018 summer monsoon season. Journal of Earth System Science, 130(1), 37.
Kushardono, D. (2012). Kajian satelit penginderaan jauh cuaca generasi baru Himawari-8 dan Himawari-9. Jurnal Inderaja, 3(5).
Levizzani, V., Amorati, R., & Meneguzzo, F. (2002). A review of satellite-based rainfall estimation methods (Report No. EVK1-CT-2000-00058). European Commission Project MUSIC.
Mulsandi, A., Mamenun, M., Fitriano, L., & Hidayat, R. (2019). Perbaikan estimasi curah hujan berbasis data satelit dengan memperhitungkan faktor pertumbuhan awan. Jurnal Sains dan Teknologi Modifikasi Cuaca, 20(2), 67–78.
Pratama, A., Agiel, H. M., & Oktaviana, A. A. (2022). Evaluasi satellite precipitation product (GSMaP, CHIRPS, dan IMERG) di Kabupaten Lampung Selatan. Journal of Science and Applicative Technology, 6(1), 32–40.
Radová, M., & Seidl, J. (2008). Parallax applications when comparing radar and satellite data. In The 2008 EUMETSAT Meteorological Satellite Conference.
Roy Bhowmik, S. K., & Das, A. K. (2007). Rainfall analysis for Indian monsoon region using the merged rain gauge observations and satellite estimates: Evaluation of monsoon rainfall features. Journal of Earth System Science, 116(3), 187–198.
Silalahi, F. S. A., Farda, N. M., & Nurjani, E. (2024). Uji perbandingan metode estimasi curah hujan menggunakan Satelit Himawari: Metode konvensional dan machine learning. Jurnal Geosains dan Remote Sensing, 5(2), 131–141.
Sujarweni, V. W. (2014). Metodologi penelitian. Yogyakarta: Pustaka Baru Press.
Upadhyaya, S., & Ramsankaran, R. A. A. J. (2016). Modified-INSAT multi-spectral rainfall algorithm (M-IMSRA) at climate region scale: Development and validation. Remote Sensing of Environment, 187, 186–201.
Vijayalakshmi, S. (2023). IMSRA rainfall estimation validation and variability over Indian region during GAJA cyclone. Mausam, 74(1), 129–140.
Wilks, D. S. (2006). Statistical methods in the atmospheric sciences. Academic Press.
Zakir, A. W., Sulistya, & Khotimah, M. K. (2010). Perspektif operasional cuaca tropis. Jakarta: Pusat Penelitian dan Pengembangan Badan Meteorologi, Klimatologi, dan Geofisika (BMKG).




