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http://dx.doi.org/10.5532/KJAFM.2012.14.2.053

High Resolution Gyeonggi-do Agrometeorology Information Analysis System based on the Observational Data using Local Analysis and Prediction System (LAPS)  

Chun, Ji-Min (Meteorological Application Research Laboratory, National Institute of Meteorological Research)
Kim, Kyu-Rang (Meteorological Application Research Laboratory, National Institute of Meteorological Research)
Lee, Seon-Yong (Meteorological Application Research Laboratory, National Institute of Meteorological Research)
Kang, Wee-Soo (College of Agriculture and life Sciences, Seoul National University)
Park, Jong-Sun (College of Agriculture and life Sciences, Seoul National University)
Yi, Chae-Yon (Meteorological Application Research Laboratory, National Institute of Meteorological Research)
Choi, Young-Jean (Meteorological Application Research Laboratory, National Institute of Meteorological Research)
Park, Eun-Woo (College of Agriculture and life Sciences, Seoul National University)
Hong, Sun-Sung (Gyeonggi-do Agricultural Research and Extension Services)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.14, no.2, 2012 , pp. 53-62 More about this Journal
Abstract
Demand for high resolution weather data grows in the agriculture and forestry fields. Local Analysis and Prediction System (LAPS) can be used to analyze the local weather at high spatial and temporal resolution, utilizing the data from various sources including numerical weather prediction models, wind or temperature profilers, Automated Weather Station (AWS) networks, radars, and satellites. LAPS has been set to analyze weather elements such as air temperature, relative humidity, wind speed, and wind direction every hour at the spatial resolution of $100m{\times}100m$ for Gyeonggi-do on near real-time basis. The AWS data were revised by adding the agricultural field AWS data (33 stations) in addition to the KMA data. The analysis periods were from 1 to 31 August 2009 and from 15 to 21 February 2010. The comparison of the LAPS output showed the smaller errors when using the agricultural AWS observation data together with the KMA data as its input data than using only either the agricultural or KMA AWS data. The accuracy of the current system needs improvement by further optimization of analyzing options of the system. However, the system is highly applicable to various fields in agriculture and forestry because it can provide site specific data with reasonable time intervals.
Keywords
LAPS (local analysis and prediction system); High resolution weather data; Agro-meteorology information system;
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