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Moderate resolution imaging spectroradiometer (MODIS) data have been analyzed over four different regions (Yellow sea, Korean inland, East Sea, and South Sea) in Republic of Korea to investigate the seasonal variability of aerosol-cloud properties and aerosol indirect effect during the past decade (2000–2009). Aerosol optical depth (AOD) was found to be consistently high during spring. Cloud ice radius (CIR) also showed higher values during spring, while an enhancement in cloud water radius (CWR) and fine mode fraction (FMF) was observed during summer. AOD and aerosol index (AI) were found to be higher during January to June. However, FMF and CWR showed enhancement during July to December. Aerosol indirect effect (AIE) in each year has been estimated and found to be showing positive and negative indirect effects. The AIE for fixed cloud ice path (CIP) showed positive indirect effect (Twomey effect) over Yellow sea, while the AIE for fixed cloud water path (CWP) showed a major negative indirect effect (anti-Twomey effect) over all regions. During Changma (summer monsoon) period, the AIE for both CIP and CWP showed dominant anti-Twomey effect in middle and low level clouds, indicating the growth of cloud droplet radius with changes in aerosols, enhancing the precipitation.

The objective of this study is to evaluate the impact of the high resolution topographies and landuses data on simulated meteorological variables (wind speed at 10 m, temperature at 2 m and relative humidity at 2 m) in WRF. We compare the results with WRF simulation using each resolution of the topographies and landuses, and with 37 AWS observation data on the Seoul metropolitan regions. According to results of using high-resolution topography, WRF model gives better topographical expression over domain. And we can separate more detail (Low intensity residential, high intensity residential, industrial or commercial) using high resolution landuses data. The result shows that simulated temperature and wind speed are generally higher than AWS observation data. However, simulation trend with temperature, wind speed, and relative humidity are similar to observation data. The reason for that is that the high precipitation event occurred in CASE 1 and 2. Temperature have correlation of 0.43~0.47 and standard deviation of 2.12~2.28 o C in CASE 1, while correlation of more than 0.8 and standard deviation of 3.05~3.18 m s −1 in CASE 2. In case of wind speed, correlation have lower than 0.5 and Standard Deviation of 1.88~2.34 m s −1 in CASE 1 and 2. In statistical analysis shows that using highest resolution (U01) results are more close to the AWS observation data. It can be concluded that the topographies and landuses are important factor that affect model simulation. However, the tendency to always use high resolution topographies and landuses data appears to be unjustified, and optimal solution depends on the combination of scale effect and mechanisms of dynamic models.

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