• Journal of Environmental Science International
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• v.26 no.12
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• pp.1307-1319
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• 2017

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean $PM_{10}$ concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.

• Journal of Environmental Science International
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• v.29 no.1
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• pp.33-44
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• 2020

Recently, a variety of modeling studies have been conducted to examine the air quality over South Korea during the Korea - United States Air Quality (KORUS-AQ) campaign period (May 1 to June 10, 2016). This study investigates the impact of different meteorological initializations on atmospheric modeling results. We conduct several simulations during the KORUS-AQ period using the Weather Research and Forecasting (WRF) model with two different initial datasets, which is FNL of NCEP and ERA5 of ECMWF. Comparing the raw initial data, ERA5 showed better accuracy in the temperature, wind speed, and mixing ratio fields than those of NCEP-FNL. On the other hand, the results of WRF simulations with ERA5 showed better accuracy in the simulated temperature and mixing ratio than those with FNL, except for wind speed. Comparing the nudging efficiency of temperature and wind speed fields, the grid nudging effect on the FNL simulation was larger than that on the ERA5 simulation, but the results of mixing ratio field was the opposite. Overall, WRF simulation with ERA5 data showed a better performance for temperature and mixing ratio simulations than that with FNL data. For wind speed simulation, however, WRF simulation with FNL data indicated more accurate results compared to that with ERA5 data.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.6
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• pp.554-569
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• 2017

Sensitivity analysis on $PM_{10}$ forecasting simulations was carried out by using two different initial and boundary conditions of meteorological fields: NCEP/FNL (National Centers for Environmental Prediction/Final Analysis) reanlaysis data and NCEP/GFS (National Centers for Environmental Prediction/Global Forecast System) forecasting data, and the comparisons were made between two different simulations. The two results both yielded lower $PM_{10}$ concentrations than observations, with relatively lower biased results by NCEP/FNL than NCEP/GFS. We explored the detailed individual meteorological variables to associate with $PM_{10}$ prediction performance. With the results of NCEP/FNL outperforming GFS, our conclusion is that no particular significant bias was found in temperature fields between NCEP/FNL and NCEP/GFS data, while the overestimated wind speed by NCEP/GFS data influenced on the lower $PM_{10}$ concentrations simulation than NCEP/FNL, by decreasing the duration time of high-$PM_{10}$ loaded air mass over both coastal and metropolitan areas. These comparative characteristics of FNL against GFS data such as maximum 3~4 m/s weaker wind speed, $PM_{10}$ concentration control with the highest possible factor of 1.3~1.6, and one or two hour difference of peak time for each case in this study, were also reflected into the results of statistical analysis. It is implying that improving the surface wind speed fluctuation is an important controlling factor for the better prediction of $PM_{10}$ over Korean Peninsula.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.195-201
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• 2001

Sowing date experiments were carried out by employing a rice variety "Kwanganbyeo" in both field and phytotron with natural daylength. In phytotron, temperatures were controlled at daily mean of 21$^{\circ}C$ and 24$^{\circ}C$. The responses of final leaf number and beading date were analyzed in relation to daylength during photo-sensitive period (PSP). Based on the component models predicting the final leaf number and leaf appearance rate, a rice phenology model was established and verified. Days from sowing to flowering (DSF) were shortened and final number of leaves (FNL) increased as sowing dates were delayed from 25 April to 5 June in field and phytotron. The increased leaf appearance rate (LAR) and the reduced FNL, respectively, due to the higher temperature and the shorter daylength in delayed sowings in the field brought about greater shortening of DSF than in the phytotron where only FNL was reduced by shorter daylength in delayed sewings. FNL showed very close relationship with the average daylength during PSP of six-leaf stage to panicle initiation, being well fitted to the following rational function ($R^2$=0.98):(equation omitted) where D is daylength and a, b, and c are the constants that were estimated as 14.694, -0.992, and -0.068 in Kwanganbyeo, respectively. The rice phonology model, which was composed of two component models for LAR and FNL, predicted DSF very accurately. The differences between the observed and predicted DSF was less than two days in the sewing date field experiments in 1999 and 2000 of which data were not used for the model construction.struction.

• Journal of Environmental Science International
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• v.23 no.8
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• pp.1481-1493
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• 2014

In order to understand the relation between the distribution of sea surface temperature and heavy snowfall over western coast of the Korean peninsula, several numerical assessments were carried out. Numerical model used in this study is WRF, and sea surface temperature data were FNL(National Center for Environment Prediction-Final operational global analysis), RTG(Real Time Global analysis), and OSTIA(Operational Sea Surface Temperature and Sea Ice Analysis). There were produced on the basis of remote sensing data, such as a variety of satellite and in situ observation. The analysis focused on the heavy snowfall over Honam districts for 2 days from 29 December 2010. In comparison with RTG and OSTIA SST data, sensible and latent heat fluexes estimated by numerical simulation with FNL data were higher than those with RTG and OSTIA SST data, due to higher sea surface temperature of FNL. General distribution of RTG and OSTIA SST showed similar, however, fine spatial differences appear in near western coast of the peninsula. Estimated snow fall amount with OSTIA SST was occurred far from the western coast because of higher SST over sea far from coast than that near coast. On the other hand, snowfall amount near coast is larger than that over distance sea in simulation with RTG SST. The difference of snowfall amount between numerical assessment with RTG and OSTIA is induced from the fine difference of SST spatial distributions over the Yellow sea. So, the prediction accuracy of snowfall amount is strongly associated with the SST distribution not only over near coast but also over far from the western coast of the Korean peninsula.

• Atmosphere
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• v.22 no.4
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• pp.387-400
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• 2012

This study aims at examining the sensitivity of numerical simulations to the resolution of initial and boundary data, and to an application of WRF (Weather Research and Forecasting) 3DVAR (Three Dimension Variational data Assimilation). To do this, we ran the WRF model by using GDAS (Global Data Assimilation System) FNL (Final analyses) and the KLAPS (Korea Local Analysis and Prediction System) analyses as the WRF's initial and boundary data, and by using an initial field made by assimilating the radar data to the KLAPS analyses. For the sensitivity experiment, we selected a heavy rainfall case of 21 September 2010, where there was localized torrential rain, which was recorded as 259.5 mm precipitation in a day at Seoul. The result of the simulation using the FNL as initial and boundary data (FNL exp) showed that the localized heavy rainfall area was not accurately simulated and that the simulated amount of precipitation was about 4% of the observed accumulated precipitation. That of the simulation using KLAPS analyses as initial and boundary data (KLAPC exp) showed that the localized heavy rainfall area was simulated on the northern area of Seoul-Gyeonggi area, which renders rather difference in location, and that the simulated amount was underestimated as about 6.4% of the precipitation. Finally, that of the simulation using an initial field made by assimilating the radar data to the KLAPS using 3DVAR system (KLAP3D exp) showed that the localized heavy rainfall area was located properly on Seoul-Gyeonggi area, but still the amount itself was underestimated as about 29% of the precipitation. Even though KLAP3D exp still showed an underestimation in the precipitation, it showed the best result among them. Even if it is difficult to generalize the effect of data assimilation by one case, this study showed that the radar data assimilation can somewhat improve the accuracy of the simulated precipitation.

• Journal of the Korean earth science society
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• v.41 no.5
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• pp.447-458
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• 2020

This study presents the K-means clustering analysis-based classification of the meteorological patterns affecting the occurrence of high PM₁₀ concentration in the southeastern region of the Korean peninsula for the last five years (2014-2018). Regional differences in Busan, Ulsan, and Gyeongnam related to high PM₁₀ episodes, were clarified through the statistical comparison study using synoptic scale meteorological elements using NCEP (National Centers for Environmental Prediction/FNL (Final Operational Global Analysis) re-analysis meteorological data. Meteorological patterns were classified into a total of five categories (C1-C5). The incidence of each cluster was 24.8% (C1), 21.3% (C2), 20.4% (C3), 17.3% (C4), and 16.2% (C5), respectively. The high PM₁₀ concentration in the southeastern region resulted from long and short range transports (C1, C3, C5) from outside of the region, and the emissions (C2, C4) inside the region. In the high PM₁₀ episodes in Busan, Ulsan, and Gyeongnam regions, meteorological characteristics such as different geopotential height and wind speed at 500 hPa in each cluster and the change in the location of high pressure over Korean Peninsula is strongly associated with the dispersion of PM₁₀ around inventories in the region and the tendency of long-range transportation of PM₁₀ emitted from outside of region.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.2
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• pp.119-130
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• 2012

Heat island phenomena in Chuncheon (Korea) were investigated using air temperature measured by automatic weather stations and temperature dataloggers located at rural and urban sites. Numerical simulation of the phenomena was performed using Weather Research and Forecasting Urban Canopy Model (WRF-UCM) and results were compared with the observation. The model was initialized with NCEP/FNL data. The horizontal resolution of the fine domain is 0.33 km. The results of observational analyses show that the intensity of heat island was significantly higher during the nighttime than during the daytime. The highest measured temperature difference between rural and urban site is $3.49^{\circ}C$ and average temperature difference varies between 1.4 and $1.9^{\circ}C$. Good agreement was found between the simulated and observed temperatures. However, significantly overestimated wind speed was found at the urban sites. The linear regression analysis between observed and simulated temperature shows high correlation coefficient 0.96 for urban and 0.94 for rural sites while for wind speed, a very low correlation coefficient was found, 0.30 and 0.55 respectively.

• Atmosphere
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• v.16 no.3
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• pp.235-246
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• 2006

Remote sensing techniques using satellites or the scanning weather radars depend mostly on the presence of clouds or precipitation, and leave the extensive regions of clear air unobserved. But wind profilers provide the most direct measurements of mesoscale vertical air motion in the troposphere, even in the context of heavy precipitation. In this paper, the precipitation events during the Changma period was classified into 4 precipitation types - stratiform, mixed stratiform/ convective, deep convective, and shallow convective. The parameters for the classification of analysis are the vertical structure of reflectivity, Doppler velocity, and spectral width measured with the wind profiler at Haenam for a three-year period (2003-2005). In addition, the synoptic fields and total amount of precipitation were analyzed using the Global Final Analyses (FNL) data and the Global Precipitation Climatology Project (GPCP) data. During the Changma period, the results show that the stratiform type was dominant under the moist-neutral atmosphere in 2003, whereas the deep convective type was under the moist unstable condition in 2004. The stratiform type was no less popular than the deep convective type among four seasons because the moist neutral layer was formed by the convergence between the upper-level jet and the low-level jet, and by the moisture transport along the western rim of the North Pacific subtropical anticyclone.

• Journal of Environmental Science International
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• v.31 no.12
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• pp.999-1012
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• 2022

To investigate the reason for the spatial difference in PM_2.5 (Particulate Matter, < 2.5 ㎛) concentration despite a similar synoptic pattern, a synoptic analysis was performed. The data used for this study were the daily average PM_2.5 concentration and meteorological data observed from 2016 to 2020 in Busan and Seoul metropolitan areas. Synoptic pressure patterns associated with high PM_2.5 concentration episodes (greater than 35 ㎍/m³) were analyzed using K-means cluster analysis, based on the 900 hPa geopotential height of NCEP (National Centers for Environmental Prediction) FNL (Final analysis) data. The analysis identified three sub-groups related to high concentrations occurring only in Busan and Seoul metropolitan areas. Although the synoptic patterns of high PM_2.5 concentration episodes that occur independently in Busan and Seoul metropolitan areas were similar, there was a difference in the intensity of pressure gradient and its direction, which tends to be an important factor determining the movement time of pollutants. The spatial difference in PM_2.5 concentration in the Korean Peninsula is due to the difference and direction of the atmospheric pressure gradient that develops from southwest to northeast direction.