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All Journal Journal of Computation Physics and Earth Science
Rini Arista
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics

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Application of Himawari-9 and Radiosonde Data in Analyzing Extreme Rainfall Events (Case Study: Malang, November 25, 2023) Rini Arista; Muhammad Alvin Faiz
Journal of Computation Physics and Earth Science (JoCPES) Vol 4 No 2 (2024): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/sf6vx516

Abstract

This study uses Himawari-9 meteorological satellite data and radiosonde data to examine the severe rain event that happened in Malang on November 25, 2023. The Japan Meteorological Agency's (JMA) Himawari-9 satellite collects high-frequency atmospheric data, and radiosonde data provide vertical atmospheric information. This study determines the distribution of major convective clouds and meteorological characteristics that suggest the possibility of severe weather by analyzing satellite photos using the RGB technique and radiosonde data. Convective clouds identified by satellite images at 07:40 UTC started to blanket the Malang City area and spread until they filled the entire East Java region at 09:00 UTC, according to the analysis's findings. Weather metrics including the Showalter Index (SI), Lifted Index (LI), and Convective Available Potential Energy (CAPE) are displayed in radiosonde data to support the possibility of heavy rain. There is a significant chance that flooding in Malang will result from heavy rains due to these unstable atmospheric conditions.
Literature Review: Development of a Machine Learning-Based Early Warning System for Land and Forest Fires with IoT and Automated Action Recommendations Rini Arista; Daniela Adolfina Ndaumanu
Journal of Computation Physics and Earth Science (JoCPES) Vol 3 No 1 (2023): Journal of Computation Physics and Earth Science
Publisher : Yayasan Kita Menulis - JoCPES

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63581/JoCPES.v3i1.03

Abstract

Lightning strikes pose significant threats to human safety and infrastructure, particularly in tropical regions like Indonesia with high lightning activity. This study aims to develop a predictive model of lightning strike risk to humans based on spatial analysis and environmental factors, utilizing data on lightning distribution, land use, population density, and meteorological parameters. Using probabilistic decision trees and tropical lightning formulas, the model identifies key predictors, including rainfall, land use patterns, and humidity, which influence lightning density. The results reveal that densely populated areas with high lightning activity, such as parts of Java and Sumatra, are particularly vulnerable. Spatial risk maps generated from the model highlight high-risk zones, providing critical insights for disaster mitigation planning and infrastructure protection. Furthermore, the study emphasizes the significant correlation between lightning density, land use, and population exposure, offering a comprehensive framework for understanding lightning risks. This predictive model not only serves as a tool for early warning systems and sustainable spatial planning but also underscores the importance of integrating environmental and spatial data for effective lightning risk mitigation. Future research should incorporate temporal lightning variations and field validation to refine the model and enhance its applicability.
Co-Authors Daniela Adolfina Ndaumanu Muhammad Alvin Faiz