Improved short range forecasting by blending techniques using extrapolation and NWP model forecasts

Nowcasting and short range forecast rely more and more on "blending" techniques combining several data sources (both in situ and remote sensing observation, NWP, model output statistic data, high resolution topography, etc..) in a seamless way using lead-time-dependent weights. Developed nowcasting techniques blend extrapolation-based forecasts with numerical weather prediction (NWP)-based forecasts, heavily weighting the extrapolation forecasts at 0 3h lead times and transitioning emphasis to the NWP-based forecasts at the later lead times. Korea Meteorological Administration (KMA) employs NOAA's Local Analysis and Prediction System (LAPS) which is called KLAPS. It provides the hot-start initial condition to the very short-range forecasting system called advanced storm-scale analysis and prediction system (ASAPS) based on the Weather Research and Forecasting (WRF) model. MAPLE (McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation) uses radar composite maps to predict the location of precipitation echoes several hours in advance (up to 6 hours) using the variational echo tracking method and a semi-Lagrangian backward advection technique. This system has been operating in real-time since June 2008, the output being used in operations by KMA's weather forecasters and hydrologists. The spatial resolution of both products are 1km. The purpose of this study is to improve the accuracy of short range forecasting using the merging method (distance, similarity) between radar-based extrapolation forecast (MAPLE) and precipitation forecast from NWP model (ASAPS). In this study, a new approach to applying different weights to blend extrapolation and model forecasts based on intensities and forecast times is applied and tested. As a result, very short range forecasts was confirmed the possibility to be improved.