GIS-based Visual Analytics for Oceanographic data on Korea Operational oceanographic system

Title
GIS-based Visual Analytics for Oceanographic data on Korea Operational oceanographic system
Author(s)
김진아; 박광순
KIOST Author(s)
Kim, Jinah(김진아)
Publication Year
2014-08-25
Abstract
The goal of KOOS (Korea Operational Oceanographic System) is to provide integrated marine services including observations, forecasts, information products, knowledge and scientific advices to the marine users and policy makers for Korea coast and to resolve various ocean-related problems [1]. It will achieve its goals by implementing an integration of observations and modeling into a marine information system and design of a decision support system for coastal and regional seas.Primarily, the KOOS acquires and integrates real time coastal and ocean monitoring data from various observation platforms and generates nowcast and forecast ocean properties for 72 hours at every 12-hours about the sea state such as sea wind, sea surface pressure and elevation, storm surge, tides, wave, temperature, salinity, currents, suspended sediments and so on using weather models, wave models and ocean/coastal models. To the public, the information is used effectively in fisheries, safe navigation, leisure activities, and coastal development. Furthermore, morst and to resolve various ocean-related problems [1]. It will achieve its goals by implementing an integration of observations and modeling into a marine information system and design of a decision support system for coastal and regional seas.Primarily, the KOOS acquires and integrates real time coastal and ocean monitoring data from various observation platforms and generates nowcast and forecast ocean properties for 72 hours at every 12-hours about the sea state such as sea wind, sea surface pressure and elevation, storm surge, tides, wave, temperature, salinity, currents, suspended sediments and so on using weather models, wave models and ocean/coastal models. To the public, the information is used effectively in fisheries, safe navigation, leisure activities, and coastal development. Furthermore, mor
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/26049
Bibliographic Citation
Data Analytics 2014, pp.1 - 5, 2014
Publisher
IARIA
Type
Conference
Language
English
Publisher
IARIA
Related Researcher
Research Interests

AI/Machine Learning,Climate Change,Marine Disaster,인공지능/기계학습,기후변화,해양기상재해

Files in This Item:
There are no files associated with this item.

qrcode

Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse