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High-resolution sea surface temperature (SST) products and idealized SST distributions were used to simulate snowfall over the Yellow Sea during 30-31 December 2007 using the Weather Research and Forecasting Model (WRF). Large differences were found between the SST distributions in the New Generation Sea Surface Temperature (NGSST) and Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) datasets near the Yellow Sea coast. Idealized SST datasets were defined to examine the influence of this difference in detail. The SST differences influenced the cloud streets and resultant snowfall formation. In simulations with the idealized SST distributions, convection developed and intensified later when the SST gradient was increased. In addition, the intensity of cloud streets was enhanced along the center of the flow. The simulations using the NGSST dataset showed widely distributed cloud streets and snowfall and heavier snowfall over the western Korean Peninsula, while those using the OSTIA dataset showed a concentrated distribution of cloud streets and snowfall along the center of airflow and more intense snowfall over North Jeolla Province, Korea, than in other regions. Comparing real SST products with observations qualitatively and quantitatively, OSTIA is found to have simulated the distribution and intensity of snowfall better than NGSST.

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Abstract. In this study, we apply the four-dimensional variational (4D-Var) data assimilation to optimize initial ozone state and to improve the predictability of air quality. The numerical modeling systems used for simulations of atmospheric condition and chemical formation are the Weather Research and Forecasting (WRF) model and the Community Multiscale Air Quality (CMAQ) model. The study area covers the capital region of South Korea, where the surface measurement sites are relatively evenly distributed.The 4D-Var code previously developed for the CMAQ model is modified to consider background error in matrix form, and various numerical tests are conducted. The results are evaluated with an idealized covariance function for the appropriateness of the modified codes. The background error is then constructed using the NMC method with long-term modeling results, and the characteristics of the spatial correlation scale related to local circulation are analyzed. The background error is applied in the 4D-Var research, and a surface observational assimilation is conducted to optimize the initial concentration of ozone. The statistical results for the 12-hour assimilation periods and the 120 observatory sites show a 49.4 % decrease in the root mean squared error (RMSE), and a 59.9 % increase in the index of agreement (IOA). The temporal variation of spatial distribution of the analysis increments indicates that the optimized initial state of ozone concentration is transported to inland areas by the clockwise-rotating local circulation during the assimilation windows.To investigate the predictability of ozone concentration after the assimilation window, a short-time forecasting is carried out. The ratios of the RMSE (root mean squared error) with assimilation versus that without assimilation are 8 and 13 % for the +24 and +12 h, respectively. Such a significant improvement in the forecast accuracy is obtained solely by using the optimized initial state. The potential improvement in ozone prediction for both the daytime and nighttime with application of data assimilation is also presented.