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

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.