• Journal of Environmental Science International
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• v.33 no.6
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• pp.435-442
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• 2024

We investigated the accuracy of surface air temperature prediction according to the selection of land-use data and initial meteorological data using the Weather Research and Forecasting model-v4.2.1. A numerical experiment was conducted at the Daegu Dyeing Industrial Complex. We initially used meteorological input data from GFS (Global forecast system)and GDAPS (Global data assimilation and prediction system). High-resolution input data were generated and used as input data for the weather model using the land cover data of the Ministry of Environment and the digital elevation model of the Ministry of Land, Infrastructure, and Transport. The experiment was conducted by classifying the terrestrial and topographic data (land cover data) and meteorological data applied to the model. For simulations using high-resolution terrestrial data(10 m), global data assimilation, and prediction system data(CASE 3), the calculated surface temperature was much closer to the automatic weather station observations than for simulations using low-resolution terrestrial data(900 m) and GFS(CASE 1).

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1141-1154
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• 2018

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.

• Atmosphere
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• v.21 no.2
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• pp.197-208
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• 2011

The objective of this study is to describe the short-range forecast system of the Korea Air Force (KAF) and to verificate its performace in 2009 summer. The KAF weather prediction model system, based on the Weather Research and Forecasting (WRF) model (i.e., the KAF-WRF), is configured with a parent domain overs East Asia and two nested domains with the finest horizontal grid size of 2 km. Each domain covers the Korean peninsula and South Korea, respectively. The model is integrated for 84 hour 4 times a day with the initial and boundary conditions from National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data. A quantitative verification system is constructed for the East Asia and Korean peninsula domains. Verification variables for the East Asia domain are 500 hPa temperature, wind and geopotential height fields, and the skill score is calculated using the difference between the analysis data from the NCEP GFS model and the forecast data of the KAF-WRF model results. Accuracy of precipitation for the Korean penisula domain is examined using the contingency table that is made of the KAF-WRF model results and the KMA (Korea Meteorological Administraion) AWS (Automatic Weather Station) data. Using the verification system, the operational model and parallel model with updated version of the WRF model and improved physics process are quantitatively evaluated for the 2009 summer. Over the East Aisa region, the parallel experimental model shows the better performance than the operation model. Errors of the experimental model in 500 hPa geopotential height near the Tibetan plateau are smaller than errors in the operational model. Over the Korean peninsula, verification of precipitation prediction skills shows that the performance of the operational model is better than that of the experimental one in simulating light precipitation. However, performance of experimental one is generally better than that of operational one, in prediction.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.51-56
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• 2006

Biases embedded in numerical weather precipitation forecasts by the RDAPS model was determined, quantified and corrected. The ultimate objective is to eventually enhance the reliability of reservoir operation by Korean Water Resources Corporation (KOWACO), which is based on precipitation-driven forecasts of stream flow. Statistical post-processing, so called MOS (Model Output Statistics) was applied to RDAPS to improve their performance. The Artificial Neural Nwetwork (ANN) model was applied for 4 cases of 'Probability of Precipitation (PoP) for wet and dry season' and 'Quantitative Precipitation Forecasts (QPF) for wet and dry season'. The reduction on the large systematic bias was especially remarkable. The performance of both networks may be improved by retraining, probably every month. In addition, it is expected that performance of the networks will improve once atmospheric profile data are incorporated in the analysis. The key to the optimal performance of ANN is to have a large data set relevant to the predictand variable. The more complex the process to be modeled by the ANN, the larger the data set needs to be.

• Journal of the Korea Institute of Military Science and Technology
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• v.6 no.4
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• pp.147-158
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• 2003

The prediction of wind field is very important fact in the radioactive and chemical warfare. In spite of advanced numerical weather prediction modelling and computing technology, the high resolution prediction of wind field is limited by the very high integration costs. In this study we coupled the mesoscale numerical model and microscale diagnostic numerical model with minimized integration costs. This coupled model has not only the ability of prediction of high resolution wind field including complex building but also microscale pollutant diffusion fields. For military operation this system can help making a practical and cost-effective decision in a battle field.

• Journal of the Korean Solar Energy Society
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• v.39 no.2
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• Journal of Environmental Science International
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• v.26 no.10
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• pp.1167-1180
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• 2017

Numerical Weather Prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are essential for forecasting one-day-ahead solar irradiance. In order to evaluate the performance of the WRF in forecasting solar irradiance over the Korean Peninsula, we compared WRF prediction data from 2008 to 2010 corresponding to weather observation data (OBS) from the Korean Meteorological Administration (KMA). The WRF model showed poor performance at polluted regions such as Seoul and Suwon where the relative Root Mean Square Error (rRMSE) is over 30%. Predictions by the WRF model alone had a large amount of potential error because of the lack of actual aerosol radiative feedbacks. For the purpose of reducing this error induced by atmospheric particles, i.e., aerosols, the WRF model was coupled with the Community Multiscale Air Quality (CMAQ) model. The coupled system makes it possible to estimate the radiative feedbacks of aerosols on the solar irradiance. As a result, the solar irradiance estimated by the coupled system showed a strong dependence on both the aerosol spatial distributions and the associated optical properties. In the NF (No Feedback) case, which refers to the WRF-only stimulated system without aerosol feedbacks, the GHI was overestimated by $50-200W\;m^{-2}$ compared with OBS derived values at each site. In the YF (Yes Feedback) case, in contrast, which refers to the WRF-CMAQ two-way coupled system, the rRMSE was significantly improved by 3.1-3.7% at Suwon and Seoul where the Particulate Matter (PM) concentrations, specifically, those related to the $PM_{10}$ size fraction, were over $100{\mu}g\;m^{-3}$. Thus, the coupled system showed promise for acquiring more accurate solar irradiance forecasts.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.121-126
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• 2010

The automotive weather strip has functions of isolating of water, dust, noise and vibration from outside. To achieve good sealing performance, weather strip should be designed to have the high contact force and wide contact area. However, these design causes excessive permanent deformation of weather strip. The causes of permanent deformation is generally explained to be the chemical material detrioration and physical variation and cyclic loading, etc. This paper introduces a numerical method to predict the permanent deformation using the time dependent viscoelastic model which is represented by Prony series in ABAQUS. Uniaxial tension and creep tests were conducted to obtain the material data. And the lab. test for the permanent deformation was accelerated during shorter time, 300 hours. The permanent deformation of weather strip was successfully predicted under the different loading conditions and different section shapes using the suggested numerical process.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.81-91
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• 2023

As abnormal weather conditions escalate, water disasters such as droughts and floods occur more frequently. These natural disasters are fatal to agricultural reservoirs, where the operation techniques vary greatly depending on the season and weather conditions, and response through intake works is limited. In response, governments like the Korea Rural Community Corporation have researched efficient water supply methods through irrigation channels. Therefore, previous studies analyzed the irrigation process using numerical models to determine an efficient irrigation system. However, SWMM and EPANET used in previous studies are limited in quantitative agricultural irrigation process analysis. Therefore, this study developed AIRISS to simulate and analyze agricultural irrigation. Specifically, we simulated the irrigation process in the Ssangbong area of South Korea and simulated the irrigation process to verify the performance of the numerical model. AIRISS, developed in this study, is specialized in simulating the agricultural irrigation process. It can check the supply to each paddy and the condition of each paddy.

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.140.1-140.1
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• 2009