• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.25-34
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• 2014

In winter 2013, extreme air pollution by fine particulate matter ($PM_{2.5}$) in China attracted much public attention. In order to simulate the $PM_{2.5}$ pollution, the Community Multiscale Air Quality model driven by the Weather Research and Forecasting model was applied to East Asia in a period from 1 January 2013 to 5 February 2013. The model generally reproduced $PM_{2.5}$ concentration in China with emission data in the year 2006. Therefore, the extreme $PM_{2.5}$ pollution seems to be mainly attributed to meteorological (weak wind and stable) conditions rather than emission increases in the past several years. The model well simulated temporal and spatial variations in $PM_{2.5}$ concentrations in Japan as well as China, indicating that the model well captured characteristics of the $PM_{2.5}$ pollutions in both areas on the windward and leeward sides in East Asia in the study period. In addition, contribution rates of four anthropogenic emission sectors (power generation, industrial, residential and transportation) in China to $PM_{2.5}$ concentration were estimated by conducting zero-out emission sensitivity runs. Among the four sectors, the residential sector had the highest contribution to $PM_{2.5}$ concentration. Therefore, the extreme $PM_{2.5}$ pollution may be also attributed to large emissions from combustion for heating in cold regions in China.

• Atmosphere
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• v.23 no.1
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• pp.1-11
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• 2013

The angular momentum transport of an idealized axisymmetric vortex in the developing stage was investigated using the Weather Research and Forecast (WRF) model. The balanced axisymmetric vortex was constructed based on an empirical function for tangential wind, and the temperature, geopotential, and surface pressure were obtained from the balanced equation. The numerical simulation was carried out for 6 days on the f-plane with the Sea Surface Temperature (SST) set as constant. The weak vortex at initial time was intensified with time, and reached the strength of tropical cyclone in a couple of days. The Absolute Angular Momentum (AAM) was transported along with the secondary circulation of the vortex. Total AAM integrated over a cylinder of radius of 2000 km decreased with simulation time, but total kinetic energy increased rapidly. From the budget analysis, it was found that the surface friction is mainly responsible for the decrease of total AAM. Also, contribution of the surface friction to the AAM loss was about 90% while that of horizontal advection was as small as 8%. The trajectory of neutral numerical tracers following the secondary circulation was presented for the Lagrangian viewpoint of the transports of absolute angular momentum. From the analysis using the trajectory of tracers it was found that the air parcel was under the influence of the surface friction continuously until it leaves the boundary layer near the core. Then the air parcel with reduced amount of angular momentum compared to its original amount was transported from boundary layer to upper level of the vortex and contributed to form the anti-cyclone. These results suggest that the tropical cyclone loses angular momentum as it develops, which is due to the dissipation of angular momentum by the surface friction.

• Journal of Climate Change Research
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• v.8 no.1
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• pp.11-19
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• 2017

This study examined the relationship between the initiation timing typhoon season in the Northwestern Pacific and the tropical sea surface temperature (SST) using a numerical simulation. The initiation timing of the typhoon season is closely associated with SSTs over the Indian Ocean (IO) and the eastern Pacific (EP) in the preceding winter and early-spring. The experiment based on the Weather and Research Forecast (WRF) model showed that the start date of the typhoon season is delayed for about one month when the SSTs over the IO and the EP increase in the preceding winter. The forced tropical SST pattern induces anticyclonic anomalies in the Northwestern Pacific, which is an unfavorable condition for typhoon development, and hence it could delay the initiation of the typhoon season.

• Atmosphere
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• v.28 no.2
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• pp.211-222
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• 2018

Cloud droplet activation process is well described by $K{\ddot{o}}hler$ theory and several parameterizations based on $K{\ddot{o}}hler$ theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the $K{\ddot{o}}hler$ equation, i.e., osmotic coefficient method (OSM) and ${\kappa}-K{\ddot{o}}hler$ method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.1-10
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• 2010

A regional climate model (RCM) can be a powerful tool to enhance spatial resolution of climate and weather information (IPCC, 2001). In this study we conducted dynamical downscaling using Weather Research and Forecasting Model (WRF) as a RCM in order to obtain high resolution regional agroclimate indices over the Korean Peninsula. For the purpose of obtaining detailed high resolution agroclimate indices, we first reproduced regional weather for the period of March to June, 2002-2008 with dynamic downscaling method under given lateral boundary conditions from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data. Normally, numerical model results have shown biases against observational results due to the uncertainties in the modelis initial conditions, physical parameterizations and our physical understanding on nature. Hence in this study, by employing a statistical method, the systematic bias in the modelis results was estimated and corrected for better reproduction of climate on high resolution. As a result of the correction, the systematic bias of the model was properly corrected and the overall spatial patterns in the simulation were well reproduced, resulting in more fine-resolution climatic structures. Based on these results, the fine-resolution agro-climate indices were estimated and presented. Compared with the indices derived from observation, the simulated indices reproduced the major and detailed spatial distributions. Our research shows a possibility to simulate regional climate on high resolution and agro-climate indices by using a proper downscaling method with a dynamical weather forecast model and a statistical correction method to minimize the model bias.

• Journal of the Korean earth science society
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• v.36 no.6
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• pp.567-579
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• 2015

The accurate simulation of micro-scale weather phenomena such as fog using the mesoscale meteorological models is a very complex task. Especially, the uncertainty arisen from initial input data of the numerical models has a decisive effect on the accuracy of numerical models. The data assimilation is required to reduce the uncertainty of initial input data. In this study, the limitation of the mesoscale meteorological model was verified by WRF (Weather Research and Forecasting) model for a summer fog event around the Nakdong river in Korea. The sensitivity analyses of simulation accuracy from the numerical model were conducted using two different initial and boundary conditions: KLAPS (Korea Local Analysis and Prediction System) and LDAPS (Local Data Assimilation and Prediction System) data. In addition, the improvement of numerical model performance by FDDA (Four-Dimensional Data Assimilation) using the observational data from AWS (Automatic Weather System) was investigated. The result of sensitivity analysis showed that the accuracy of simulated air temperature, dew point temperature, and relative humidity with LDAPS data was higher than those of KLAPS, but the accuracy of the wind speed of LDAPS was lower than that of KLAPS. Significant difference was found in case of relative humidity where RMSE (Root Mean Square Error) for LDAPS and KLAPS was 15.7 and 35.6%, respectively. The RMSE for air temperature, wind speed, and relative humidity was improved by approximately $0.3^{\circ}C$, $0.2m\;s^{-1}$, and 2.2%, respectively after incorporating the FDDA.

• Atmosphere
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• v.19 no.4
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• pp.345-370
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• 2009

Numerical experiments using the Weather Research and Forecasting (WRF) model were done to identify the role of the mountain ranges in the northern part of the Peninsula (referred as "the northern mountain complex"), in the occurrence of two heavy snowfall events over the Yeongdong region on 7-8 December 2002 and 20-21 January 2008. To this end, control simulations with the topography of the northern mountain complex and other simulations without the topography of the mountain complex were performed. It was revealed that the amount of snowfall over the Yeongdong region from the control simulation much more exceeded that of the simulation without the topography of the mountain complex. This increase of the snowfall amount over the Yeongdong region can be explained as follows: As the upstream flow approached the northern mountain complex, it deflected around the northern mountain complex due to the blocking effect of the mountains with a low Froude number less than ~0.16. This lead to the strengthening of northeasterly over the East Sea and over the Yeongdong region. The strong northeasterly is accompanied with much more snowfall over the Yeongdong region by intensifying air-mass modification over the sea and the orographic effect of the Taeback mountains. Thus, it was concluded that the topography of the northern mountain complex is one of the main factors in determining the distribution and amount of precipitation in the Yeongdong region when there is an expansion of the Siberian High toward the East Sea.

• Atmosphere
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• v.25 no.4
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• pp.669-683
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• 2015

In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don't constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.

• Atmosphere
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• v.27 no.4
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• pp.455-466
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• 2017

In Korea, property and human damages occur annually due to heavy precipitation during the summer. On August 8, 2015, heavy rainfall occurred in the Seoul metropolitan area due to an outflow boundary, and $77mmhr^{-1}$ rainfall was recorded in Gwangju, Gyeonggi Province. In this study, the simulation of the WRF numerical model is performed to understand the cause and characteristics of heavy rainfall using the Conditional Instability of the Second Kind (CISK), potential vorticity (PV), frontogenesis function, and convective available potential energy (CAPE) analyses, etc. Convective cells initiated over the Shandong Peninsula and located on the downwind side of an upper level trough. Large amounts of water vapor were supplied to the Shandong Peninsula along the southwestern edge of a high pressure system, and from the remnants of typhoon Soudelor. The mesoscale convective system (MCS) developed through CISK process and moved over to the Yellow Sea. The outflow boundary from the MCS progressed east and pushed cold pool eastward. The warm and humid air over the Korean Peninsula further enhanced convective development. As a result, a new MCS developed rapidly over land. Because of the latent heat release due to convection and precipitation, strong potential vorticity was generated in the lower atmosphere. The rapid development of MCS and the heavy rainfall occurred in an area where the CAPE value was greater than $1300Jkg^{-1}$ and the fronto-genesis function value of 1.5 or greater coincided. The analysis result shows that the MCS driven by an outflow boundary can be identified using CISK process.

• Atmosphere
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• v.32 no.3
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• pp.207-221
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• 2022

In Jeju, on January 23, 2016, a cold surge accompanied by heavy snowfall with the most significant amount of 12 cm was the highest record in 32 years. During this period, the temperature of 850 hPa in January was the lowest in 2016. Notably, in 2016, the average surface temperature of January on the Polar cap was the highest since 1991, and 500 hPa geopotential height also showed the highest value. With this condition, the polar vortex in the northern hemisphere meandered and expanded into the subtropics regionally, covering the Korean Peninsula with very high potential vorticity up to 7 Potential Vorticity Unit. As a result, the strong cold advection, mostly driven by a northerly wind, around the Korean Peninsula occurred at over 2𝜎. Previous studies have not addressed this extreme synoptic condition linked to polar vortex expansion due to the unprecedented Arctic warming. We suggest that the occurrence of a strong Ural blocking event after the abrupt warming of the Barents/Karas seas is a major cause of unusually strong cold advection. With a specified mesoscale model simulation with SST (Sea Surface Temperature), we also show that the warmer SST condition near the Korean Peninsula contributed to the heavy snowfall event on Jeju Island.