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http://dx.doi.org/10.5391/JKIIS.2016.26.3.182

A Method for Correcting Air-Pressure Data Collected by Mini-AWS  

Ha, Ji-Hun (Department of Embedded Software Engineering, Kwangwoon University)
Kim, Yong-Hyuk (Department of Computer Science and Engineering, Kwangwoon University)
Im, Hyo-Hyuc (Korea Oceanic and Atmospheric System Technology)
Choi, Deokwhan (Korea Oceanic and Atmospheric System Technology)
Lee, Yong Hee (National Institute of Meteorological Science)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.26, no.3, 2016 , pp. 182-189 More about this Journal
Abstract
For high accuracy of forecast using numerical weather prediction models, we need to get weather observation data that are large and high dense. Korea Meteorological Administration (KMA) mantains Automatic Weather Stations (AWSs) to get weather observation data, but their installation and maintenance costs are high. Mini-AWS is a very compact automatic weather station that can measure and record temperature, humidity, and pressure. In contrast to AWS, costs of Mini-AWS's installation and maintenance are low. It also has a little space restraints for installing. So it is easier than AWS to install mini-AWS on places where we want to get weather observation data. But we cannot use the data observed from Mini-AWSs directly, because it can be affected by surrounding. In this paper, we suggest a correcting method for using pressure data observed from Mini-AWS as weather observation data. We carried out preconditioning process on pressure data from Mini-AWS. Then they were corrected by using machine learning methods with the aim of adjusting to pressure data of the AWS closest to them. Our experimental results showed that corrected pressure data are in regulation and our correcting method using SVR showed very good performance.
Keywords
Mini-AWS; AWS; correcting; atmospheric pressure; mteorological data;
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Times Cited By KSCI : 3  (Citation Analysis)
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