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Precipitation Estimation from Himawari-8 Multiple Spectral Channels Using U-Net

Ngoc Do, H. and Xuan Ngo, T. and Hung Nguyen, A. and Thi Nhat Nguyen, T. (2023) Precipitation Estimation from Himawari-8 Multiple Spectral Channels Using U-Net. In: UNSPECIFIED.

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Abstract

Precipitation profoundly impacts Earth's ecosystems, from soil and plants to animals and humans. Accurate rainfall estimation is vital for various purposes, including weather and flood forecasting, enhancing agriculture, ensuring traffic safety, and minimizing natural disaster damage while safeguarding lives. Currently, rainfall estimation methods are mainly based on three main sources of information: satellite images, radar images and rain gauges data. However, these methods have limitations such as spatial coverage, limited range, capturing precipitation dynamics in complex terrainand, differentiating between precipitation types and accurately estimating intensity. To address these challenges, recent research explores deep learning-based methods that combine these data sources for accurate and comprehensive precipitation estimatation. This paper outlines directions for developing such methods using the U-Net deep learning model, incorporating data from Himawari-8 satellite imagery, rain radar data, meteorological data, and topographic information. This approach yields improved results in predicting rainfall rates and generating radar maps. © 2023 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Offices > Office of International Cooperation
Identification Number: 10.1109/KSE59128.2023.10299481
Uncontrolled Keywords: Deep learning; Disasters; Flood control; Floods; Radar imaging; Rain; Rain gages; Remote sensing; Space-based radar; Tracking radar, Deep learning; ERA5; Flood forecasting; Himawari-8; Net model; Precipitation estimation; Rainfall estimations; Spectral channels; Traffic safety; U-net model, Satellite imagery
Additional Information: cited By 0; Conference of 15th International Conference on Knowledge and Systems Engineering, KSE 2023 ; Conference Date: 18 October 2023 Through 20 October 2023; Conference Code:194303
URI: http://eprints.lqdtu.edu.vn/id/eprint/11042

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