• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.134-137
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• 2007

In this paper the heat health alert system, which is operated this year by way of showing an example, is a simulator linked to the Geographic Information System (GIS), and it uses meteorological data that are observed at Automatic Weather Systems (AWSs) in Seoul, Korea. Simulation results show that it is possible to use meteorological data observed by AWSs when the Korea Meteorological Administration (KMA) has issued alerting the public to the threat of heat waves, and to connect meteorological data to spatial data when the KMA offers local forecasts and weather-related information. However, most AWSs that were installed to manage urban disasters do not measure humidity, so general humidity is used in all districts. Therefore, to issue heat wave warnings about different localities on a small scale, we will study how to complement this problem and to examine the accuracy of data observed at AWS in the future.

• Atmosphere
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• v.28 no.2
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• pp.163-174
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• 2018

In order to assimilate MHS satellite data into the convective scale model at KMA, ATOVS data are reprocessed to utilize the original high-resolution data. And then to improve the preprocessing experiments for cloud detection were performed and optimized to convective-scale model. The experiment which is land scattering index technique added to Observational Processing System to remove contaminated data showed the best result. The analysis fields with assimilation of MHS are verified against with ECMWF analysis fields and fit to other observations including Sonde, which shows improved results on relative humidity fields at sensitive level (850-300 hPa). As the relative humidity of upper troposphere increases, the bias and RMSE of geopotential height are decreased. This improved initial field has a very positive effect on the forecast performance of the model. According to improvement of model field, the Equitable Threat Score (ETS) of precipitation prediction of $1{\sim}20mm\;hr^{-1}$ was increased and this impact was maintained for 27 hours during experiment periods.

• Atmosphere
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• v.23 no.2
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• pp.187-204
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• 2013

This study analyzed the synoptic distribution and vertical structure about four cases of precipitation occurrences using NCEP/NCAR reanalysis data and upper level data of winter intensive observation to be performed by National Institute of Meteorological Research at Bukgangneung, Incheon, Boseong during 63days from 4 JAN to 6 MAR in 2012, and Observing System Experiment (OSE) using 3DVAR-WRF system was conducted to examine the precipitation predictability of upper level data at western and southern coastal regions. The synoptic characteristics of selected precipitation occurrences were investigated as causes for 1) rainfall events with effect of moisture convergence owing to low pressure passing through south sea on 19 JAN, 2) snowfall events due to moisture inflowing from yellow sea with propagation of Siberian high pressure after low pressure passage over middle northern region on 31 JAN, 3) rainfall event with effect of weak pressure trough in west low and east high pressure system on 25 FEB, 4) rainfall event due to moisture inflow according to low pressures over Bohai bay and south eastern sea on 5 MAR. However, it is identified that vertical structure of atmosphere had different characteristics with heavy rainfall system in summer. Firstly, depth of convection was narrow due to absence of moisture convergence and strong ascending air current in middle layer. Secondly, warm air advection by veering wind with height only existed in low layer. Thirdly, unstable layer was limited in the narrow depth due to low surface temperature although it formed, and also values of instability indices were not high. Fourthly, total water vapor amounts containing into atmosphere was small due to low temperature distribution so that precipitable water vapor could be little amounts. As result of OSE conducting with upper level data of Incheon and Boseong station, 12 hours accumulated precipitation distributions of control experiment and experiments with additional upper level data were similar with ones of observation data at 610 stations. Although Equitable Threat Scores (ETS) were different according to cases and thresholds, it was verified positive influence of upper level data for precipitation predictability as resulting with high improvement rates of 33.3% in experiment with upper level data of Incheon (INC_EXP), 85.7% in experiment with upper level data of Boseong (BOS_EXP), and 142.9% in experiment with upper level data of both Incheon and Boseong (INC_BOS_EXP) about accumulated precipitation more than 5 mm / 12 hours on 31 January 2012.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.340-343
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• 2003

This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.525-529
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• 2009

On the limited urban area meteorological data are hard to be collected because of the cost problem. The facilities collecting the data require high installment cost. Recently, the sensor network technique comes to the fore as a solution. Furthermore a mobile phone also becomes to be recognized as a sensor. This paper studies an application to service the meteorological map using mobile phone sensor. A design results for system implementation are introduced in this paper.

• Journal of Environmental Science International
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• v.14 no.2
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• pp.141-156
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• 2005

The importance of atmospheric conditions for the assessment of an air pollution situation has been demonstrated by their influence on the various compartments of an air pollution system, comprising all stages from emission to effects. Especially, air pollutants dispersion phenomenon are very sensitive according to wind data. But the discussions of how to apply representative meteorological data in air pollution dispersion model are not frequent in Korean environmental assessment processes. In this study, we investigated the difference of air pollutants dispersion phenomenon using U.S EPA ISCLT3 model according to applying the different meteorological data observed at two points for Seongseo industrial complex of Daegu. Two points are the spot site of Seongseo industrial complex and Daegu meteorological observatory. The winds speed of the spot site were smaller than those of Daegu meteorological observatory. In the winter season, the differences came to about $64\%$ for the period$(I\;February\;2001\~31\;January\;2002)$. Wind directions were also fairly different at two points. The air pollutants dispersion phenomenon estimated from our numerical experiments were also fairly different owing to the meteorological conditions at two points.

• Atmosphere
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• v.16 no.4
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• pp.379-385
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• 2006

Meteorological Research Institute (METRI) participates the R&E (Research and Education) program of Korea Science and Engineering Foundation,"Variability of ocean status around Ulleung basin and Dok-do by using ARGO data" as a part of "Carricula development for gifted students" program. From this program, we support students to have an opportunity for handling scientific data with advanced technology and inspire their scientific interests. In this article, we introduce the training processes of this program and the results of data analysis by the students themselves.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.235-245
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• 2023

Forest fuel moisture content is a crucial factor influencing the combustion rate and fuel consumption during forest fires, significantly impacting the occurrence and spread of wildfires. In this study, meteorological data were gathered using a meteorological measuring device (HOBO data logger) installed in the south and north slopes of Kangwon National University Forest, as well as on bare land outside the forest, from November 1, 2021, to October 31, 2022. The objective was to analyze the relationship between meteorological data and fuel moisture content. Fuel moisture content from the ground cover on the south and north slopes was collected. Fallen leaves on the ground were utilized, with a focus on broad-leaved trees (Prunus serrulata, Quercus dentata, Quercus mongolica, and Castanea crenata) and coniferous trees (Pinus densiflora and Pinus koraiensis), categorized by species. Additionally, correlation analysis with fuel moisture content was conducted using temperature (average, maximum, and minimum), humidity (average, minimum), illuminance (average, maximum, and minimum), and wind speed (average, maximum, and minimum) data collected by meteorological measuring devices in the study area. The results indicated a significant correlation between meteorological factors such as temperature, humidity, illuminance, and wind speed, and the moisture content of fuels. Notably, exceptions were observed for the moisture content of the on the north slope and that of the ground cover of Prunus serrulata and Castanea crenata.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Atmosphere
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• v.24 no.1
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• pp.29-38
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• 2014

A characteristic of snowfall and minimum temperature variability in South Korea with respect to the variability of Arctic Oscillation (AO) was investigated. The climatic snowfall regions of South Korea based on daily new fresh snowfall data of 59 Korea Meteorological Administration (KMA) stations data corresponding to the sign of AO index during December to February 1979~2011 were classified. Especially, the differences between snowfalls of eastern regions and that of western regions in South Korea were seen by each mean 1000hPa geopotential height fields, which is one of physical structure, for the selected cases over the East Asia including the Korean Peninsula. Daily minimum temperature variability of 59 KMA station data and daily AO index during the same period were investigated using Cyclo-stationary empirical orthogonal function (CSEOF) analysis. The first CSEOF of wintertime daily AO index and that of minimum temperature of 59 KMA stations explain 33% and 66% of total variability, respectively. Correlation between principal component time series corresponding to the first CSEOF of AO index and that of temperature at the period of 1990s is over about -0.7 when that of AO index leads about 40 days.