A STATISTICAL METHOD TO PREDICT METEOROLOGICAL DATA FOR REAL-TIME GOCI DATA PROCESSING

摘要

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.