• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.3
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• pp.256-271
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• 2016

The source and transport of the severe Asian dust event (ADE) recently observed in the Korean peninsula were analyzed based on observations (surface weather charts and satellite data) and modeling study (WRF-CMAQ modeling systems). The ADE occurred on 20-21 March 2010 in South Korea with very high $PM_{10}$ concentrations (up to approximately $3,000{\mu}g/m^3$ in Daegu and Jeju). The dominant meteorological conditions affecting the dust outbreak and transport processes were found to be associated with the two synoptic features: (1) strong airflows (i.e., westerlies) induced by a strong pressure gradient resulting from a dense isobar pattern (west-high and east-low) between Tuva Republic and Mongolia and (2) a rapid movement of the strong westerlies merged with airflows generated near Gobi Desert and Inner Mongolia. The merged strong westerlies with a low pressure played a pivotal role in the huge amount of AD and its transport height of 5-8 km. The time and location of dust emissions calculated in the source regions were similar to those observed in the weather charts and satellite image. The ADE simulation mostly showed agreement in the patterns and the concentration levels of modeled dust (including $PM_{10}$) with those of the observations.

• Atmosphere
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• v.27 no.4
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• pp.399-409
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• 2017

The impact of land cover changed by tidal flats on local meteorology in Gyeonggi Bay was quantitatively evaluated based on a numerical modeling approach during 18 days (21 June to 9 July 2013). The analysis was carried out using three sets of simulation scenarios and the land cover of tidal flats for each simulation was applied as follows: (1) the herbaceous wetland representing coastal wetlands (i.e., EXP-BASE case), (2) the barren or sparsely vegetated representing low tide (i.e., EXP-LOW case), (3) the water bodies representing high tide (i.e., EXP-HIGH case). The area of tidal flats was calculated as about $552km^2$ (the ratio of 4.7% for analysis domain). During the daytime, the change (e.g. wetlands to water) of land cover flooded by high tide indicated the decrease of temperature (average $3.3^{\circ}C$) and the increase of humidity (average 13%) and wind speed (maximum $2.9m\;s^{-1}$). The changes (e.g. wetlands to barren or sparsely vegetated) of land cover induced by low tide were smaller than those by high tide. On the other hands, the effects of changed land cover at night were not apparent both high tide and low tide. Also, during the high tide, the meteorological change in tidal flats affected the metropolitan area (about 40 km from the tidal flat).

• Journal of the Korean Solar Energy Society
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• v.31 no.6
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• pp.8-22
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• 2011

Investigations on modeling methods of a CFD wind resource prediction program, WindSim for a ccurate predictions of wind speeds were performed with the field measurements. Meteorological Masts having heights of 40m and 50m were installed at two different sites in complex terrain. The wind speeds and direction were monitored from sensors installed on the masts and recorded for one year. Modeling parameters of WindSim input variables for accurate predictions of wind speeds were investigated by performing cross predictions of wind speeds at the masts using the measured data. Four parameters that most affect the wind speed prediction in WindSim including the size of a topographical map, cell sizes in x and y direction, height distribution factors, and the roughness lengths were studied to find out more suitable input parameters for better wind speed predictions. The parameters were then applied to WindSim to predict the wind speed of another location in complex terrain in Korea for validation. The predicted annual wind speeds were compared with the averaged measured data for one year from meteorological masts installed for this study, and the errors were within 6.9%. The results of the proposed practical study are believed to be very useful to give guidelines to wind engineers for more accurate prediction results and time-saving in predicting wind speed of complex terrain that will be used to predict annual energy production of a virtual wind farm in complex terrain.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.73-80
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• 1997

This study was performed to develop the dispersion modeling methodology for quantitative prediction of the hazard distance or toxic buffer distance by comparing 10-min average, 30-min average, and 1-hr average maximum ground-level concentration with $Cl_2$ regultaion concentration, IDLH and ERPG-3 concentration for hazardous toxic gas, $Cl_2$ releases from the storage tank of the chemical plant facilities. For this dispersion modeling, the source term model, dispersion model, meteorological and topographical data are incorporated into the SuperChems model, and then the effects of the atmospheric stability, wind speed, and surface roughness length changes on the maxum ground-level concentration were estimated.

• Journal of Environmental Science International
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• v.1 no.1
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• pp.19-33
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• 1992

The high oxidants, which occur the daily maximum concentrations in the afternoon, are transported into the other region via long range transport mechanisms or trapped within the shallow mixing boundary layer and then removed physically (deposition, transport by mountain wind, etc.) and chemically (reaction with local sources). Therefore, modeling formation of photochemical oxidants requires a complex description of both chemical and meteorolog ital processecs . In this study, as a part of air quality studies, we reviewed various aspects of photochemical modeling on the basis of currently available literature. The result of the review shows that the model is based on a set of coupled continuity equations describing advection, diffusion, transport, deposition, chemistry, emission. Also photochemical oxidant models require a large amount of input data concerned with all aspects of the ozone life cycle. First, emission inventories of hydrocarbon and nitrogen oxides, with appropriate spatial and temporal resolution. Second, chemical and photochemical data allowing the quantitative description of the formation of ozone and other photochemically-generated secondary pollutants. Third, dry deposition mechanisms particularly for ozone, PAN and hydrogen peroxide to account for their removal by absorption on the ground, crops, natural vegetation, man-made and water surfaces. Finally, meteorological data describing the transport of primary pollutants away from their sources and of secondary pollutants towards the sensitive receptors where environmental damage may occur. In order to improve our present study, shortcomings and limitation of existing models are pointed out and verification Process through observation is emphasized.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.15-27
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• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

• Atmosphere
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• v.30 no.4
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• pp.335-346
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• 2020

Three free parameters included in a cumulus parameterization are optimized by using micro-genetic algorithm for three precipitation cases occurred in the Korea Peninsula during the summer season in order to reduce biases in a regional model associated with the uncertainties of the parameters and thus to improve the predictability of precipitation. The first parameter is the one that determines the threshold in convective trigger condition. The second parameter is the one that determines boundary layer forcing in convective closure. Finally, the third parameter is the one used in calculating conversion parameter determining the fraction of condensate converted to convective precipitation. Optimized parameters reduce the occurrence of convections by suppressing the trigger of convection. The reduced convection occurrence decreases light precipitation but increases heavy precipitation. The sensitivity experiments are conducted to examine the effects of the optimized parameters on the predictability of precipitation. The predictability of precipitation is the best when the three optimized parameters are applied to the parameterization at the same time. The first parameter most dominantly affects the predictability of precipitation. Short-range forecasts for July 2018 are also conducted to statistically assess the precipitation predictability. It is found that the predictability of precipitation is consistently improved with the optimized parameters.

• Journal of Environmental Impact Assessment
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• v.29 no.4
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• pp.272-285
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• 2020

We investigated statistical evaluation parameters for 3D meteorological and air quality models and selected several quantitative indicator references, and summarized the reference values of the statistical parameters for domestic air quality modeling researcher. The finally selected 9 statistical parameters are MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias), and the associated reference values are summarized. The results showed that MB and ME have been widely used in evaluating the meteorological model output, and NMB and NME are most frequently used for air quality model results. In addition, discussed are the presentation diagrams such as Soccer Plot, Taylor diagram, and Q-Q (Quantile-Quantile) diagram. The current results from our study is expected to be effectively used as the statistical evaluation parameters suitable for situation in Korea considering various characteristics such as including the mountainous surface areas.

• Journal of Environmental Science International
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• v.26 no.6
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• pp.741-750
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• 2017

A model coupling a meteorological predictive model and a vegetation photosynthesis and respiration model was used to simulate $CO_2$ concentrations over coastal basin areas, and modeling results were estimated with aircraft observations during a massive sampling campaign. Along with the flight tracks, the model captured the meteorological variables of potential temperature and wind speed with mean bias results of $0.8^{\circ}C$, and 0.2 m/s, respectively. These results were statistically robust, which allowed for further estimation of the model's performance for $CO_2$ simulations. Two high-resolution emission data sets were adopted to determine $CO_2$ concentrations, and the results show that the model underestimated by 1.8 ppm and 0.9 ppm at higher altitude over the study areas during daytime and nighttime, respectively, on average. Overall, it was concluded that the model's $CO_2$ performance was fairly good at higher altitude over the study areas during the study period.