• Journal of the Korean earth science society
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• v.41 no.1
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• pp.1-18
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• 2020

This study presented the effects of the assumed mass-size relationship for snow on the simulated surface precipitation by using cloud microphysics parameterizations in Weather Research and Forecasting (WRF) model. The selected cloud microphysics parameterizations are WRF Double-Moment 6-class (WDM6) and WRF Single-Moment 6-class (WSM6) in the WRF model. We replaced the mass-size relationship for snow in WDM6 and WSM6 with Thompson's mass-size relationship retrieved from measurement data. The sensitivity of the modified WDM6 and WSM6 was tested for the idealized 2-dimensional squall line and winter precipitation system over the Korean peninsula, respectively. The modified WDM6 and WSM6 resulted in the increase of graupel/rain mixing ratios and the decrease of snow mixing ratio in the low atmosphere. The changes of hydrometeor mixing ratio and surface precipitation could be due to the collision-coalescence process between raindrops and snow and the graupel melting process.

• Atmosphere
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• v.30 no.4
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• pp.421-437
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• 2020

The effects of the terminal fall velocity-diameter relationship for raindrops, which is prescribed based on the measurement, on the simulated surface precipitation over Korea during summer season were investigated in our study. Two rainfall cases, 1-month summer precipitation and mesoscale rainfall, have been simulated using the Weather Research and Forecasting (WRF) model. The selected cloud microphysics parameterizations are WRF Single-Moment 5-class (WSM5) and WRF Single-Moment 6-class (WSM6) in the WRF model. The measured terminal fall-diameter relationship for raindrops by Gunn and Kinzer (1949) was applied in both WSM5 and WSM6. The sensitivity experiments with WSM5 and WSM6, applying the measured fall-diameter relationship, presents the different responses in simulated precipitation amount for the 1-month summer precipitation case. Precipitation increases with WSM5, thus enhancing the precipitation statistical skills. However, precipitation decreases with WSM6 leading to the deterioration of precipitation statistical skills. For the mesoscale rainfall case, precipitation increases with both WSM5 and WSM6, which further enhances the positive bias in precipitation amount.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.557-577
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• 2023

This study defines non-precipitation information as areas with weak precipitation or cloud particles that radar cannot detect due to weak returned signals, and suggests methods for its utilization in data assimilation. Previous studies have demonstrated that assimilating radar data from precipitation echoes can produce precipitation in model analysis and improve subsequent precipitation forecast. However, this study also recognizes the non-precipitation information as valuable observation and seeks to assimilate it to suppress spurious precipitation in the model analysis and forecast. To incorporate non-precipitation information into data assimilation, we propose observation operators that convert radar non-precipitation information into hydrometeor mixing ratios and relative humidity for the Weather Research and Forecasting Data Assimilation system (WRFDA). We also suggest a preprocessing method for radar non-precipitation information. A single-observation experiment indicates that assimilating non-precipitation information fosters an environment conducive to inhibiting convection by lowering temperature and humidity. Subsequently, we investigate the impact of assimilating non-precipitation information to a real case on July 23, 2013, by performing a subsequent 9-hour forecast. The experiment that assimilates radar non-precipitation information improves the model's precipitation forecasts by showing an increase in the Fractional Skill Score (FSS) and a decrease in the False Alarm Ratio (FAR) compared to experiments in which do not assimilate non-precipitation information.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.687-696
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• 2018

In order to develop effective emission abatement strategies for coal-fired power plants, we analyzed the shutdown effects of coal-fired power plants on $PM_{2.5}$ concentration in June by employing air quality model for the period from 2013 to 2016. WRF (Weather Research and Forecast) and CMAQ(Community Multiscale Air Quality) models were used to quantify the impact of emission reductions on the averaged $PM_{2.5}$ concentrations in June over Chungcheongnam-do area in Korea. The resultant shutdown effects showed that the averaged $PM_{2.5}$ concentration in June decreased by 1.2% in Chungcheongnam-do area and decreased by 2.3% in the area where the surface air pollution measuring stations were located. As a result of this study, it was confirmed that it is possible to analyze policy effects considering the change of meteorology and emission and it is possible to quantitatively estimate the influence at the maximum impact region by utilizing the air quality model. The results of this study are expected to be useful as a basic data for analyzing the effect of $PM_{2.5}$ concentration change according to future emission changes.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.276-293
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• 2011

In this study a modeling system consisting of Weather Research and Forecasting (WRF), Sparse Matrix Operator Kernel Emissions (SMOKE), the Community Multiscale Air Quality (CMAQ) model, and the CMAQ-Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (MADRID) model has been applied to estimate enhancements of $PM_{10}$ during Asian dust events in Korea. In particular, 5 experimental formulas were applied to the WRF-SMOKE-CMAQ (MADRID) model to estimate Asian dust emissions from source locations for major Asian dust events in China and Mongolia: the US Environmental Protection Agency (EPA) model, the Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model, and the Dust Entrainment and Deposition (DEAD) model, as well as formulas by Park and In (2003), and Wang et al. (2000). According to the weather map, backward trajectory and satellite image analyses, Asian dust is generated by a strong downwind associated with the upper trough from a stagnation wave due to development of the upper jet stream, and transport of Asian dust to Korea shows up behind a surface front related to the cut-off low (known as comma type cloud) in satellite images. In the WRF-SMOKE-CMAQ modeling to estimate the PM10 concentration, Wang et al.'s experimental formula was depicted well in the temporal and spatial distribution of Asian dusts, and the GOCART model was low in mean bias errors and root mean square errors. Also, in the vertical profile analysis of Asian dusts using Wang et al's experimental formula, strong Asian dust with a concentration of more than $800\;{\mu}g/m^3$ for the period of March 31 to April 1, 2007 was transported under the boundary layer (about 1 km high), and weak Asian dust with a concentration of less than $400\;{\mu}g/m^3$ for the period of 16-17 March 2009 was transported above the boundary layer (about 1-3 km high). Furthermore, the difference between the CMAQ model and the CMAQ-MADRID model for the period of March 31 to April 1, 2007, in terms of PM10 concentration, was seen to be large in the East Asia area: the CMAQ-MADRID model showed the concentration to be about $25\;{\mu}g/m^3$ higher than the CMAQ model. In addition, the $PM_{10}$ concentration removed by the cloud liquid phase mechanism within the CMAQ-MADRID model was shown in the maximum $15\;{\mu}g/m^3$ in the Eastern Asia area.

• Journal of Korean Society for Atmospheric Environment
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• v.31 no.5
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• pp.430-436
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• 2015

Numerical air quality forecasting suffers from the large uncertainties of input data including emissions, boundary conditions, earth surface properties. Data assimilation has been widely used in the field of weather forecasting as a way to reduce the forecasting errors stemming from the uncertainties of input data. The present study aims at evaluating the effect of input data on the air quality forecasting results in Korea when data assimilation was invoked to generate the initial concentrations. The forecasting time was set to 36 hour and the emissions and initial conditions were chosen as tested input parameters. The air quality forecast model for Korea consisting of WRF and CMAQ was implemented for the test and the chosen test period ranged from November $2^{nd}$ to December $1^{st}$ of 2014. Halving the emission in China reduces the forecasted peak value of $PM_{10}$ and $SO_2$ in Seoul as much as 30% and 35% respectively due to the transport from China for the no-data assimilation case. As data assimilation was applied, halving the emissions in China has a negligible effect on air pollutant concentrations including $PM_{10}$ and $SO_2$ in Seoul. The emissions in Korea still maintain an effect on the forecasted air pollutant concentrations even after the data assimilation is applied. These emission sensitivity tests along with the initial condition sensitivity tests demonstrated that initial concentrations generated by data assimilation using field observation may minimize propagation of errors due to emission uncertainties in China. And the initial concentrations in China is more important than those in Korea for long-range transported air pollutants such as $PM_{10}$ and $SO_2$. And accurate estimation of the emissions in Korea are still necessary for further improvement of air quality forecasting in Korea even after the data assimilation is applied.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.3
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• pp.152-163
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• 2020

The present study introduces a procedure to prepare and manage a high-resolution rainfall and soil moisture (SM) database in the LAMP prediction system, especially for landslide researchers. The procedure also includes converting the data into spatial resolution suitable for their interest regions following proper map projection methods. The LAMP model precipitation and SM data are quantitatively and qualitatively evaluated to identify the model prediction characteristics using the ERA5 reanalysis precipitation and observed 10m depth SM data. A detailed process of converting LAMP Weather Research and Forecasting (WRF) output data for 10m horizontal resolution is described in a step-wise manner, providing technical convenience for users to easily convert NetCDF data from the WRF model into TIF data in ArcGIS. The converted data can be viewed and downloaded via the LAMP website (http://df.ncam.kr/lamp/index.do) of the National Center for AgroMeteorology. The constructed database will contribute to monitoring and prediction of landslide risk prior to landslide response steps and should be data quality controlled by more observation data.

• Journal of Environmental Science International
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• v.19 no.2
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• pp.171-184
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• 2010

Accurate simulation of the meteorological field is very important to assess the wind resources. Some researchers showed that sea surface temperature (SST) plays a leading role on the local meterological simulation. New Generation Sea Surface Temperature (NGSST), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Real-Time Global Sea Surface Temperature (RTG SST) have different spatial distribution near the coast and OSTIA shows the best accuracy compared with buoy data in the southeastern coast of the Korean Peninsula. Those SST products are used to initialize the Weather Research and Forecasting (WRF) Model for November 13-23 2008. The simulation of OSTIA shows better result in comparison with NGSST and RTG SST. NGSST shows a large difference with OSTIA in horizontal and vertical wind fields during the weak synoptic condition, but wind power density shows a large difference during strong synoptic condition. RTG SST shows the similar patterns but smaller the magnitude and the extent.

• Journal of Environmental Science International
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• v.25 no.4
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• pp.603-613
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• 2016

Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.

• 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.