실시간 천리안해양관측위성 자료 처리에 사용되는 기후 자료를 예측하기위한 통계적 방법

Title
실시간 천리안해양관측위성 자료 처리에 사용되는 기후 자료를 예측하기위한 통계적 방법
Alternative Title
A Statistical Method to Predict Meteorological Data for Real-time GOCI Data Processing
Author(s)
양현; 한희정
KIOST Author(s)
Yang, Hyun(양현)
Publication Year
2017-05-16
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 Satellite Center (KOSC) which is the main operating agency of GOCI has a role to receive, process, and distribute its data within an hour. In this situation, we need several meteorological data (e.g., ozone, wind, relative humidity, pressure, etc.) to successfully process the GOCI atmospheric corrections. Meteorological data from National Aeronautics and Space Administration (NASA) Ocean Biology Processing Group (OBPG) are used when the GOCI atmospheric corrections are processed. Unfortunately, however, these data cannot be used for the real-time GOCI data processing because they cannot be provided in real time. In this paper, therefore, we proposed a statistic method for predicting the meteorological data and analyzed its accuracy.enter (KOSC) which is the main operating agency of GOCI has a role to receive, process, and distribute its data within an hour. In this situation, we need several meteorological data (e.g., ozone, wind, relative humidity, pressure, etc.) to successfully process the GOCI atmospheric corrections. Meteorological data from National Aeronautics and Space Administration (NASA) Ocean Biology Processing Group (OBPG) are used when the GOCI atmospheric corrections are processed. Unfortunately, however, these data cannot be used for the real-time GOCI data processing because they cannot be provided in real time. In this paper, therefore, we proposed a statistic method for predicting the meteorological data and analyzed its accuracy.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/23984
Bibliographic Citation
International Symposium on Remote Sensing 2017, pp.535 - 537, 2017
Publisher
Remote Sensing Society of Japan
Type
Conference
Language
English
Publisher
Remote Sensing Society of Japan
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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