The skillful prediction of high-impact weathers remains one of the greatest scientific and societal challenges of the 21st century. The goal of FRL are (a) to enhance the understanding of atmospheric phenomena, particularly the structure of severe weather phenomena and their formation and development mechanisms, (b) to extend a leading time for early warning of severe weathers, (c) to build up short-range quantitative precipitation forecast capabilities, and (d) to strengthen the now-casting skills. Development of atmospheric numerical weather prediction models Study of synoptic and dynamical structure of the atmosphere Short and medium-range weather prediction Study of meso-scale weather phenomena The research area of FRL is focused on the performance of research and development integrating 3 components such as the intensive observation, understanding of mechanism, and modeling on high-impact weather. Main researches at FRL are mainly divided into three parts: 1) Korean Enhanced Observing (KEOP) as a atmospheric field-based experiment, 2) Development and Operation of Short-range Analysis and Prediction System (SRAPS), and 3) Development of Nowcasting system for very short-range prediction. Warm season quantitative precipitation forecast has been a challenging issue due to its high societal impact and the low level operational forecast skill. To provide more accurate precipitation forecast guidance Forecast Research Lab. has been developing the Short-Range Analysis and Prediction System (SRAPS). The SRAPS consists of 6 h assimilation cycle for 15km lager domain and 3 h hot star cycle for 5 km inner domain. Radar reflectivity, lightning, satellite, and METAR observations are essential components for diabatic initialization applied to short-range forecasts. In the meantime, the assimilation system is also capable of providing comprehensive 3D analysis fields every hour in about 10 min process time. Various kinds of local observations are ingested into the analysis system. The final 3D analysis fields are displayed through FAS system. One of the most practical ways to improve quantitative precipitation forecasts using numerical model is ensemble approach that samples uncertainties in models and then gives probabilistic guidance. The Short-range ENnsemble prediction System (SENS) seeks solution using a number of different short-range precipitation forecasts which come from SRAPS prediction with slightly different configuration. A super ensemble approach based on SVD analysis and a PDF matching over 9 h training period yield to additional improvements of precipitation forecast skill score. High-impact weather is defined by its effect on society, economy and environment. The World Meteorological Organization (WMO) reported that global costs of extreme weather events have increased in the last decades. Improved weather forecasts will help reduce the societal costs of high-impact weather events. Prediction accuracy has continuously increased due to advances in NWP and computer performance. However, most operational models have low ability to forecast high-impact weather. High-impact weather systems are typically associated with rapid developing cyclones. These include high precipitation events and areas of very strong surface wind speeds. Major failures in forecasting high-impact weather arise from combinations of inaccurate initial conditions and model errors. Goals of this project : 1) Examine predictability and observing systems. 2) Establish the potential to produce significant and statistically verifiable improvements in forecasting high-impact weather. The National center for High-Impact weather Intensive Observation has been operated by the Meteorological Research Institute (METRI). Cloud streaks (black circles in the figure) are inclined from right-bottom to left-up. From the incline of a cloud streak, the movement and the velocity of convective system can be calculated. As the result, convective systems propagate eastward with a persistent pattern in the longitude-time space even though it is forced by synoptic systems. These characteristics are helpful in determining its predictability. Explicit initialization of microphysical species is a promising approach to alleviate the spin-up problem in the short-range (0-12 h) precipitation forecasts. FRL has focused on applying diabatic initialization technique based on the LAPS which was originally developed by GSD/NOAA. Initialization of microphysical species is based on 3D cloud analysis using satellite, radar reflectivity, and surface cloud reports. Another crucial step is to make balance between adjusted moisture field and other dynamic variables. A series of sensitivity studies shows that forecasts after the diabatic initialization yield to dramatic improvement of precipitation score up to 6-9 h forecast time. The result indicates that it could be used as a reasonable guidance to short-time precipitation forecast. SCAN is an integrated suite of multisensor applications which detects, analyzes, and monitors convection and generates short-term probabilistic forecast and warning guidance for severe weather automatically within FAS. SCAN was developed by MDL (Meteorological Development Laboratory) under NOAA, USA. SCAN was introduced for nowcasting in KMA. METRI has been developing SCAN adequate for Korean Environment . SCAN will provide forecasters with accurate, timely, and consistent severe weather guidance and supplement forecaster event monitoring with multi-sensor, automated event monitoring.   The intended benefits are 1) Longer lead times on warned events 2) Fewer missed events 3) Increased forecaster situational awareness 4) Reduced forecaster fatigue during warning situations.