• Atmosphere
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• v.25 no.3
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• pp.483-499
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• 2015

This study examined the impact of change of land-use and meteorological condition due to urbanization on heat environment in Seoul metropolitan area over a decade (2000 and 2009) using Weather Research and Forecasting (WRF)-Urban Canopy Model (UCM). The numerical simulations consist of three sets: meteorological conditions of (1) October 2000 with land-use data in 2000 (base simulation), (2) October 2009 with land-use data in 2000 (meteorological condition change effect) and (3) October 2009 with land-use data in 2009 (both the effects of land-use and meteorological condition change). According to the experiment results, the change of land-use and meteorological condition by urbanization over a decade showed different contribution to the change of heat environment in Seoul metropolitan area. There was about $1^{\circ}C$ increase in near-surface (2 m) temperature over all of the analyzed stations due to meteorological condition change. In stations where the land-use type changed into urban, large temperature increase at nighttime was observed by combined effects of meteorological condition and land-use changes (maximum $4.23^{\circ}C$). Urban heat island (UHI) over $3^{\circ}C$ (temperature difference between Seoul and Okcheon) increased 5.24% due to the meteorological condition change and 26.61% due to the land-use change. That is, land-use change turned out to be contributing to the strengthening of UHI more than the meteorological condition change. Moreover, the land-use change plays a major role in the increase of sensible heat flux and decrease of latent heat flux.

• Atmosphere
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• v.29 no.2
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• pp.219-226
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• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.887-899
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• 2022

Drought caused by meteorological factors negatively affects vegetation in terrestrial ecosystems. In this study, the state in which meteorological drought affects vegetation was defined as the ecological drought of vegetation, and the ecological drought condition index of vegetation (EDCI-veg) was proposed to quantitatively monitor the degree of impact. EDCI-veg is derived from a copula-based bi-variate joint probability model between vegetation and meteorological drought information, and can be expressed numerically how affected the current vegetation condition was by the drought when the drought occurred. Comparing past meteorological drought events with their corresponding vegetation condition, the proposed index was examined, and it was confirmed that EDCI-veg could properly monitor the ecological drought of vegetation. In addition, it was possible to spatially identify ecological drought conditions by creating a high-resolution drought map using remote sensing data.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.3
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• pp.177-190
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• 1998

The concentrations of air pollutants In large cities such as Pusan area have been increased every year due to the increasing of fuels consumption at factories and by vehicles as well as the gravitation of the population. In addition to the pollution sources, time and spatial variation of air pollutants concentration and meteorological factors have a great influence on the air pollution problem. Especially , its concentration is governed by wind direction, wind speed, precipitation, solar radiation, temperature, humidity and cloud amounts, etc. In this study, we have analyzed various data of meteorological factors using typical patterns of the air pressure to investigate how the concentration of air pollutants is varied with meteorological condition. Using the relationship between meteorological factors (air temperature, relative humidity, wind speed and solar radiation) and the concentration of air pollutants (SO2, O3) , experimental prediction formulas for their concentration were obtained. Therefore, these prediction formulas at each meteorological factor in a pressure pattern may be roughly used to predict the air pollutants concentration and contributed to estimate the variation of its value according to the weather condition in Pusan city.

• Journal of Environmental Science International
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• v.9 no.2
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• pp.109-114
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• 2000

The purpose of the present study is to develope the estimation scheme for sensible heat flux by semi-empirical approach using routine meteorological data such as solar radiation and air temperature. To compare observed sensible heat flux with estimated sensible heat flux, the sensible heat fluxes were measured by three dimensional sonic anemometer-thermometer. The field observation was performed during 1 year from December 1, 1995 to November 30, 1996 on a rice paddy field in Chunchon basin. The heat fluxes were measured at a heights of 5m and mean meteorological variables were obtained at two levels, 2.5m(or 1.5m) and 10m. Since condition of rice paddy field such as, wetness of the field, roughness length, vary widely, we devided annual data to 5 periods. Comparing with two sensible heat fluxes, the results showed that the correlation coefficients were more than 0.86. Thus, we can conclude that the estimation method of sensible heat fluxes using routine meteorological data is practical and reliable enough.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.609-626
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• 2013

Air quality models have been widely used to study and simulate many air quality issues. In the simulation, it is important to raise the accuracy of meteorological predicted data because the results of air quality modeling is deeply connected with meteorological fields. Therefore in this study, we analyzed the effects of meteorological fields on the air quality simulation. This study was designed to evaluate MM5 predictions by using different initial condition data and different observations utilized in the data assimilation. Among meteorological scenarios according to these input data, the results of meteorological simulation using National Centers for Environmental Prediction (Final) Operational Global Analysis data were in closer agreement with the observations and resulted in better prediction on ozone concentration. And in Seoul, observations from Regional Meteorological Office for data assimilations of MM5 were suitable to predict ozone concentration. In other areas, data assimilation using both observations from Regional Meteorological Office and Automatical Weather System provided valid method to simulate the trends of meteorological fields and ozone concentrations. However, it is necessary to vertify the accuracy of AWS data in advance because slightly overestimated wind speed used in the data assimilation with AWS data could result in underestimation of high ozone concentrations.

• Atmosphere
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• v.21 no.2
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• pp.163-172
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• 2011

Numerical forecasting depends on the initial condition error strongly because numerical model is a chaotic system. To calculate the sensitivity of some forecast aspects to the initial condition in the Korea Meteorological Administration (KMA) Unified Model (UM) which is originated from United Kingdom (UK) Meteorological Office (MO), an algorithm to calculate adjoint sensitivities is developed by modifying the adjoint perturbation forecast model in the KMA UM. Then the new algorithm is used to calculate adjoint sensitivity distributions for typhoon DIANMU (201004). Major initial adjoint sensitivities calculated for the 48 h forecast error are located horizontally in the rear right quadrant relative to the typhoon motion, which is related with the inflow regions of the environmental flow into the typhoon, similar to the sensitive structures in the previous studies. Because of the upward wave energy propagation, the major sensitivities at the initial time located in the low to mid- troposphere propagate upward to the upper troposphere where the maximum of the forecast error is located. The kinetic energy is dominant for both the initial adjoint sensitivity and forecast error of the typhoon DIANMU. The horizontal and vertical energy distributions of the adjoint sensitivity for the typhoon DIANMU are consistent with those for other typhoons using other models, indicating that the tools for calculating the adjoint sensitivity in the KMA UM is credible.

• Journal of Ocean Engineering and Technology
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• v.28 no.3
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• pp.254-259
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• 2014

The infrared signature of a ship is largely influenced by the ocean environment of the operating area, which has been known to cause large changes in the signature. As a result, the weather condition has to be clearly set for an analysis of the infrared signatures. It is necessary to analyze meteorological data for all the oceans where the ship is supposed to be operated. This is impossibly costly and time consuming because of the huge size of the data. Therefore, the creation of a standard environmental variable for an infrared signature research is necessary. In this study, we compared and analyzed sampling methods to represent ocean data close to the Korean peninsula. In order to perform this research, we collected ocean meteorological records from KMA (Korea Meteorological Administration), and sampled these in numerous ways considering five variables that are known to affect the infrared signature. Specifically, a simple random sampling method for all the data and 1-D, 2-D, and 3-D stratified sampling methods were compared and analyzed by considering the mean square errors for each method.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.6 s.144
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• pp.679-685
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• 2005

Until now, the stealth design to reduce the infrared signature of ship haven't been carried out using the proper design criteria. The study on the maritime meteorological environment in the Korean seas hasn't been accomplished yet, so the design criteria of the maritime meteorological environment was just given by the engineering sense without experience of the Navy and/or of the shipyard. Even in rather good conditions(summer condition), the estimated IR signature of a ship showed larger values and couldn't predict the worst condition during the operation of a ship at sea. In this study, domestic maritime meteorological data were collected and variables affecting the IR signature of a ship had been derived through the sensitivity study of IR signature according to the maritime meteorological environment in Korean seas. The basic study on the criteria of the stealth design of IR signature has been carried out.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.