This study investigated the cause of the heavy snowfall that occurred in the East Coast of Korea from 6 February to 14 February 2014. The synoptic conditions were analyzed using blocking index, equivalent potential temperature, potential vorticity, maritime temperature difference, temperature advection, and ground convergence. During the case period, a large blocking pattern developed over the Western Pacific causing the flow to be stagnant, and there was a North-South oriented High-to-Low pressure system over the Korean Peninsula because of this arrangement. The case period was divided into three parts based on the synoptic forcing that was responsible for the heavy snowfall; detailed analyses were conducted for the first and last period. In the first period, a heavy snowfall occurred over the entire Korean Peninsula due to strong updrafts from baroclinic instability and a low pressure caused by potential vorticity located at the mid-troposphere. In the lower atmosphere, a North-South oriented High-to-Low pressure system over the Eastern Korea intensified the easterly airflow and created a convergence zone near the ground which strengthened the upslope effect of the Taebaek Mountain range with a cumulative fresh snowfall amount of 41 cm in the East Coast region. In the last period, the cold air nestled in the Maritime Province of Siberia and Manchuria strengthened much more than that in the first half and extended to the East Sea. The temperature difference between the 850 hPa air and the SST was large and convective clouds developed over the sea. The highest cumulative fresh snow amount of 39.7 cm was recorded in the coastal area during this period. During the entire period, vertically oriented equivalent potential temperature showed neutral stability layer that helped the cloud formation and development in the East Coast. The 2014 heavy snowfall case over the East Coast provinces of Korea were due to: 1) stagnation of the system by blocking pattern, 2) the dynamic effect of mid-level potential vorticity of 1.6 PVU, 3) the easterly air flow from North-South oriented High-to-Low pressure system, 4) the existence of vertically oriented neutral stable layer, and 5) the expansion of strong cold air into the East Sea which created a large temperature difference between the air and the ocean.

The characteristics of heatwaves (HWs) in South Korea are studied using data from the European Centre for Medium-Range Weather Forecasts Reanalysis Interim (ERA Interim) dataset and from automatic surface weather stations.The synoptic conditions of three specific years (1994, 2013, and 2016) during which serious HWs affected South Korea are examined. HWs in South Korea are related to the northwestwardly extension of the western North Pacific subtropical high (WNPSH) toward the Korean Peninsula. Examination of the blocking indices revealed widespread blocking over the northern Pacific Ocean and Asia during South Korean HWs, and blocking is related to stationary weather patterns. The severe temperatures associated with HWs in this region are due to prolonged subsidence. Analysis of the moist static energy (MSE) budget indicated that the energy source of subsidence is derived from both MSE advection and the net heat flux. When compared to the synoptic situation during an HW in South Korea, the relative southward movement of the WNPSH is found before and after the HW. The blocking indices also revealed weak signals and changes in vertical motion due to MSE advection. K E Y W O R D S heatwave, maximum temperature, moist static energy budget, synoptic conditions

Radar data with high spatiotemporal resolution and automatic weather station (AWS) data are used in the data assimilation experiment to improve the precipitation forecast of a numerical model. The numerical model considered in this study is the Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated the radar equivalent reflectivity factor using higher resolution WRF and compared it with radar observations in South Korea. To compare the precipitation forecast characteristics of the three-dimensional variational (3D-Var) assimilation of radar data, four experiments were performed based on the scales of precipitation systems. Comparison of the 24 h accumulated rainfall with surface observation data, contoured frequency by altitude diagram (CFAD), time–height cross sections (THCS), and vertical hydrometeor profiles was used to evaluate the accuracy of the simulation of precipitation. The model simulations were performed with and without 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The combined effect of the radar and AWS data assimilation experiment improved the location of the precipitation area and rainfall intensity compared to the control run. There is a noticeable scale dependence in the improvement of precipitation systems. Improvements in simulating mesoscale convective systems were larger compared to synoptically driven precipitation systems.

<p>The accuracy of tropical cyclone (TC) forecasts from NWP models have been improved especially for the track. Relatively, TC intensity forecasts still include huge uncertainties though the dynamics, physics processes, and resolutions of NWP systems become higher in both horizontal and vertical. For this reason, many operational centers and academia for TC forecasts implemented statistical prediction systems and Artificial Intelligence (AI) algorithms based on long-term dynamic model forecasts for better predictions of typhoon intensity.<br />The National Hurricane Center (NHC) developed the Statistical Hurricane Intensity Prediction Scheme (SHIPS) which is a statistical model based on NWP forecasts (parameters from atmosphere and ocean). Also, infrared imagery from geostationary satellite is used as predictors for the regression. SHIPS is implemented for the North Atlantic and East Pacific regions. Otherwise, the Joint Typhoon Warning Center (JTWC) implemented this model for the Northwest Pacific region. Also, Korea Meteorological Administration (KMA) and Japan Meteorological Administration (JMA) developed the statistical based typhoon prediction systems (called STIPS and TIFS, respectively). However, the accuracy of these systems is not stable because it is not easy to define the tendency of NWP forecasts for TC intensity. <br />The National Typhoon Center of KMA developed a new statistical model (Statistical Prediction Intensity of Korea mEteorological administrator, SPIKE) for typhoon intensity prediction based on ECMWF forecast. While the ECMWF Integrated Forecast System (IFS) has an excellent performance in forecasting track of typhoons, the intensity tends to be underestimated compared to typhoons analysis information. <br />SPIKE is basically developed as a multi-linear regression model, and its predictors are extracted from the IFS forecast. The average prediction error of typhoon intensity of SPIKE in 2022 decreased by about 30% compared to the ECMWF forecasts. However, there was still a limitation, especially for cases of rapid intensification (RI). More studies to reflect real-time intensity, cloud development, center location, and prediction errors of the model are conducted. Then, the second multi-linear regression model to account for these parameters is developed. Finally, an additional improvement of about 30% was achieved. Also, the performance for RI cases developing more than 35 knots within 24 hours was greatly improved. </p>

Radar observation data with high temporal and spatial resolution are used in the data assimilation experiment to improve precipitation forecast of a numerical model. The numerical model considered in this study is Weather Research and Forecasting (WRF) model with double-moment 6-class microphysics scheme (WDM6). We calculated radar equivalent reflectivity factor using higher resolution WRF and compared with radar observations in South Korea. To compare the precipitation forecast characteristics of three-dimensional variational (3D-Var) assimilation of radar data, four experiments are performed based on different precipitation types. Comparisons of the 24-h accumulated rainfall with Automatic Weather Station (AWS) data, Contoured Frequency by Altitude Diagram (CFAD), Time Height Cross Sections (THCS), and vertical hydrometeor profiles are used to evaluate and compare the accuracy. The model simulations are performed with and with-out 3D-VAR radar reflectivity, radial velocity and AWS assimilation for two mesoscale convective cases and two synoptic scale cases. The radar data assimilation experiment improved the location of precipitation area and rainfall intensity compared to the control run. Especially, for the two convective cases, simulating mesoscale convective system was greatly improved.