Table of Contents Author Guidelines Submit a Manuscript
Advances in Meteorology
Volume 2018, Article ID 7210137, 8 pages
https://doi.org/10.1155/2018/7210137
Research Article

Error Correction of Meteorological Data Obtained with Mini-AWSs Based on Machine Learning

1Korea Oceanic and Atmospheric System Technology, No. 1503, 90 Gyeongin-ro 53-gil, Guro-gu, Seoul 08215, Republic of Korea
2School of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea
3Department of Computer Engineering, College of Information Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Republic of Korea

Correspondence should be addressed to Yong-Hyuk Kim; rk.ca.wk@ylfdhy

Received 18 December 2017; Accepted 25 March 2018; Published 30 April 2018

Academic Editor: Herminia García Mozo

Copyright © 2018 Ji-Hun Ha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Severe weather events occur more frequently due to climate change; therefore, accurate weather forecasts are necessary, in addition to the development of numerical weather prediction (NWP) of the past several decades. A method to improve the accuracy of weather forecasts based on NWP is the collection of more meteorological data by reducing the observation interval. However, in many areas, it is economically and locally difficult to collect observation data by installing automatic weather stations (AWSs). We developed a Mini-AWS, much smaller than AWSs, to complement the shortcomings of AWSs. The installation and maintenance costs of Mini-AWSs are lower than those of AWSs; Mini-AWSs have fewer spatial constraints with respect to the installation than AWSs. However, it is necessary to correct the data collected with Mini-AWSs because they might be affected by the external environment depending on the installation area. In this paper, we propose a novel error correction of atmospheric pressure data observed with a Mini-AWS based on machine learning. Using the proposed method, we obtained corrected atmospheric pressure data, reaching the standard of the World Meteorological Organization (WMO; ±0.1 hPa), and confirmed the potential of corrected atmospheric pressure data as an auxiliary resource for AWSs.