Statistical Approach to Predict Meteorological Material for Real-time GOCI Data Processing

Title
Statistical Approach to Predict Meteorological Material for Real-time GOCI Data Processing
Author(s)
양현
KIOST Author(s)
Yang, Hyun(양현)
Publication Year
2019-09-26
Abstract
The Geostationary Ocean Color Imager (GOCI) can be utilized to analyze subtle changes on oceanic environments because it observes ocean colors around the Northeast Asia hourly, for 8 times a day. To realize this, the Korea Ocean Satel-liteCenter (KOSC) which is the main operating agency of GOCI has a role to re-ceive, process, and distribute its data within an hour. In this situation, we need several meteorological materials (e.g., ozone, wind, relative humidity, pressure, etc.) to successfully process the GOCI atmospheric corrections. Meteorological materials from National Aeronautics and Space Administration (NASA) Ocean Biology Processing Group (OBPG) are used when the GOCI atmospheric cor-rections are processed. Unfortunately, however, these materials cannot be used for the real-time GOCI data processing because they cannot be provided in real-time. In this paper, therefore, we propose a statistical approach for predicting the meteorological material and analyzed its accuracy.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/22390
Bibliographic Citation
ITISE 2019, pp.827 - 830, 2019
Publisher
University of Granada
Type
Conference
Language
English
Publisher
University of Granada
Related Researcher
Research Interests

Ocean Satellite ICT Convergence,Artificial Intelligence/Deep Learning,Ocean Big Data,해양 위성 ICT 융합,인공지능/딥러닝,해양 빅데이터

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