• Journal of Environmental Impact Assessment
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• v.21 no.5
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• pp.665-681
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• 2012

This study investigates the influence of anthropogenic heat (AH) release on urban boundary layer in the Gyeong-In region using the Weather Research and Forecasting model that includes the Seoul National University Urban Canopy Model (SNUUCM). The gridded AH emission data, which is estimated in the Gyeong-In region in 2002 based on the energy consumption statistics data, are implemented into the SNUUCM. The simulated air temperature and wind speed show good agreement with the observed ones particularly in terms of phase for 11 urban sites, but they are overestimated in the nighttime. It is found that the influence of AH release on air temperature is larger in the nighttime than in the daytime even though the AH intensity is larger in the daytime. As compared with the results with AH release and without AH release, the contribution of AH release on urban heat island intensity is large in the nighttime and in the morning. As the AH intensity increases, the water vapor mixing ratio decreases in the daytime but increases in the nighttime. The atmospheric boundary layer height increases greatly in the morning (0800 - 1100 LST) and midnight (0000 LST). These results indicate that AH release can have an impact on weather and air quality in urban areas.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.218-233
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• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

• Journal of the Korean earth science society
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• v.45 no.1
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• pp.1-18
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• 2024

This study analyzed the effect of warming on PM_2.5 aerosol production in mid-latitude East Asia during June 2020 using PM_2.5 aerosol anomalies, which were identified by incorporating meteorological and climate data into the Weather Research Forecasting model coupled with Chemistry (WRF-Chem) model. The decadal temperature change trend over a 30-year period (1991-2020) in East Asia showed that recent warming has been greater in summer than in winter. Summer warming in East Asia generated low and high pressure in the lower and upper troposphere, respectively, over China. The boundary between the lower tropospheric low and upper tropospheric high pressure sloped along the terrain from the Tibetan Plateau to Korea. The eastern China, Yellow Sea, and Korean regions experienced a convergence of warm and humid southwesterly airflows originating from the East China Sea with the development of a northwesterly Pacific high pressure. In June 2020, the highest temperatures were observed since 1973 in Korea. Meanwhile, enhanced warming in East Asia increased the production of PM_2.5 aerosols that travelled long distances from eastern China to Korea. PM_2.5 anomalies, which were derived solely by inputting meteorological and climatic data (1991-2020) into the WRF-Chem model and excluding emission variations, showed a positive distribution extending from eastern China to South Korea across the Yellow Sea as well as over the Pacific Northwest. Thus, the contribution of warming to PM_2.5 aerosols in East Asia during June 2020 was more than 50%. In particular, PM_2.5 aerosols were transported from eastern China to Korea through the Yellow Sea, where the warm and humid southwesterly airflows implied wet scavenging of sulfate but promoted nitrate production.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.213-213
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• 2015

밭에서의 점적 관개를 이용한 노지 재배의 경우 적정 관개 계획 수립을 위해서는 작물 및 토양의 수분 정보에 대한 정확한 파악이 필요하다. 본 연구에서는 밭 토양을 GIS(Geographic Information System)를 통해 격자 형태로 분할하여 작물의 증발산량 및 토양의 수분함량을 모의할 수 있는 격자 기반 토양 물수지 모형을 개발하였다. 본 모형을 통해 작물의 소비수량 및 필요 수량을 파악함으로써 작부기간 중 필요한 관개수량을 제시하는 것이 가능하다. 고도화 기상자료로는 국가농림기상센터에서 운영 중인 고해상도 WRF(Weather Research and Forecasting) 모형에서 생산된 격자 형태의 복사, 온도, 바람, 강수 자료를 사용하였고 고도화 기상자료의 격자 해상도 별로 모의되는 작물 및 토양의 수분 정보 간 비교 및 분석을 실시하였다. 토양 물수지 모형에 입력되는 격자형태의 자료로는 기상, 토성 및 토지이용 자료가 있으며 기상자료의 경우 가로 및 세로의 크기가 각 270, 810, 2430m로 동일한 3가지 경우로 나누어 적용했으며 토성 및 토지이용 자료의 경우 기상 격자의 최소 크기에 맞춰 가로 및 세로의 크기가 각 270m인 격자로 분할하였다. 이와 같은 과정에 의한 모의 결과 각 격자별 작물 증발산량, 토양수분함량 및 관개수량의 일 연별 시계열 자료를 얻을 수 있으며 동시간대 격자별 수문인자 값을 산정하고 위치에 따른 공간적 상호 상관성을 분석하였다. 결과적으로, 고도화 기상자료의 격자 크기에 따른 밭 토양 물수지 분석 결과를 통해 고도화 기상 격자의 규모별 밭 토양 물수지 분석 효용성을 파악하고자 하였다. 더불어, 시험 지역(Test Bed) 선정을 통해 토양수분 및 증발산량을 실측하고 본 모형의 모의 결과와 비교함으로써 검정하는 것을 향후 연구 계획으로 한다.

• Journal of Environmental Science International
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• v.21 no.2
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• pp.203-215
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• 2012

In order to clarify the impacts of thermal difference in atmospheric boundary layer due to the different sophistication of building information in Busan metropolitan areas, several numerical simulations were carried out. ACM (Albedo Calculation Model) and WRF (Weather Research and Forecasting) was applied for estimating albedo and meteorological elements in urban area, respectively. In comparison with coarse aggregated and small buildings, diurnal variation of albedo is highly frequent and its total value tend to be smaller in densely aggregated and tall buildings. Estimated TKE and sensible heat flux with sophisticatedly urban building parameterization is more resonable and valid values are mainly induced by urban building sophistication. The simulation results suggest that decreased albedo and increased roughness due to skyscraper plays an important role in the result of thermal change in atmospheric boundary layer.

• Atmosphere
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• v.18 no.4
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• pp.367-386
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• 2008

The Weather Research and Forecasting (WRF) model was designed to identify the role of the Taebaek Mountains in the occurrence of heavy snowfall in Yeongdong area with a strong northeast wind on January 20-21, 2008. To this end, in addition to the control simulation with the realistic distribution of the Taebaek Mountains, a sensitivity experiment that removed the orography over the Taebaek Mountains was performed. The control simulation results showed that the resulting wind field and precipitation distribution were similar to what were observed. Results from the sensitivity experiment clearly demonstrates the presence of orographic lifting on the windward slope of the mountains. It is concluded that the altitude of the Taebaek Mountains is the main controlling factor in determining the distribution and amount of precipitation in the Yeongdong area for the case of heavy snowfall in January 2008.

• Atmosphere
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• v.29 no.5
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• pp.671-687
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• 2019

This study evaluates the long-term seasonal predictability of summer (June, July and August) heatwaves over South Korea using 30-year (1989~2018) Hindcast data of the Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. Heatwave indices such as Number of Heatwave days (HWD), Heatwave Intensity (HWI) and Heatwave Warning (HWW) are used to explore the long-term seasonal predictability of heatwaves. The prediction skills for HWD, HWI, and HWW are evaluated in terms of the Temporal Correlation Coefficient (TCC), Root Mean Square Error (RMSE) and Skill Scores such as Heidke Skill Score (HSS) and Hit Rate (HR). The spatial distributions of daily maximum temperature simulated by WRF are similar overall to those simulated by NCEP-R2 and PNU CGCM. The WRF tends to underestimate the daily maximum temperature than observation because the lateral boundary condition of WRF is PNU CGCM. According to TCC, RMSE and Skill Score, the predictability of daily maximum temperature is higher in the predictions that start from the February and April initial condition. However, the PNU CGCM-WRF chain tends to overestimate HWD, HWI and HWW compared to observations. The TCCs for heatwave indices range from 0.02 to 0.31. The RMSE, HR and HSS values are in the range of 7.73 to 8.73, 0.01 to 0.09 and 0.34 to 0.39, respectively. In general, the prediction skill of the PNU CGCM-WRF chain for heatwave indices is highest in the predictions that start from the February and April initial condition and is lower in the predictions that start from January and March. According to TCC, RMSE and Skill Score, the predictability is more influenced by lead time than by the effects of topography and/or terrain feature because both HSS and HR varies in different leads over the whole region of South Korea.

• Journal of Korean Society for Atmospheric Environment
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• v.26 no.4
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• pp.367-379
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• 2010

Future climate changes over the Seoul metropolitan area (SMA) were predicted by the Weather Research and Forecasting (WRF) model using future land-use data from the urban growth model (SLEUTH) and forecast fields from ECHAM5/MPI-OM1 GCM (IPCC scenario A1B). Simulations from the SLEUTH model with GIS information (slope, urban, hill-shade, etc.) derived from the water management information system (WAMIS) and the intelligent transportation systems-standard nodes link (ITS-SNL) showed that considerable increase by 17.1% in the fraction of urban areas (FUA) was found within the SMA in 2020. To identify the effects of the urban growth on the temperature and wind variations in the future, WRF simulations by considering urban growth were performed for two seasons (summer and winter) in 2020s (2018~2022) and they were compared with those in the present (2003~2007). Comparisons of model results showed that significant changes in surface temperature (2-meter) were found in an area with high urban growth. On average in model domain, positive increases of $0.31^{\circ}C$ and $0.10^{\circ}C$ were predicted during summer and winter, respectively. These were higher than contributions forced by climate changes. The changes in surface temperature, however, were very small expect for some areas. This results suggested that surface temperature in metropolitan areas like the SMA can be significantly increased only by the urban growth during several decades.

• Atmosphere
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• v.31 no.5
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• pp.577-592
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• 2021

This study evaluates the predictability of the number of days of heat and cold damages by growth stages of rice in South Korea using the hindcast data (1986~2020) produced by Pusan National University Coupled General Circulation Model-Weather Research and Forecasting (PNU CGCM-WRF) model chain. The predictability is accessed in terms of Root Mean Square Error (RMSE), Normalized Standardized Deviations (NSD), Hit Rate (HR) and Heidke Skill Score (HSS). For the purpose, the model predictability to produce the daily maximum and minimum temperatures, which are the variables used to define heat and cold damages for rice, are evaluated first. The result shows that most of the predictions starting the initial conditions from January to May (01RUN to 05RUN) have reasonable predictability, although it varies to some extent depending on the month at which integration starts. In particular, the ensemble average of 01RUN to 05RUN with equal weighting (ENS) has more reasonable predictability (RMSE is in the range of 1.2~2.6℃ and NSD is about 1.0) than individual RUNs. Accordingly, the regional patterns and characteristics of the predicted damages for rice due to excessive high- and low-temperatures are well captured by the model chain when compared with observation, particularly in regions where the damages occur frequently, in spite that hindcasted data somewhat overestimate the damages in terms of number of occurrence days. In ENS, the HR and HSS for heat (cold) damages in rice is in the ranges of 0.44~0.84 and 0.05~0.13 (0.58~0.81 and -0.01~0.10) by growth stage. Overall, it is concluded that the PNU CGCM-WRF chain of 01RUN~05RUN and ENS has reasonable capability to predict the heat and cold damages for rice in South Korea.