Impact of increasing the horizontal resolution of a regional forecast and analysis system based on the a ensemble kalman filter: a real case study

Abstract

In this study the impact of increasing the model resolution upon the quality of an ensemble based data assimilation system is investigated. This is done by performing data assimilation experiments with real observations over the Western North Pacific during a 40-day period of the 2008 typhoon season. Two ensembles of initial conditions with horizontal resolutions of 20 and 60 km are generated using the Local Ensemble Transform Kalman Filter (LETKF) and the Weather Research and Forecasting (WRF) model. 72-hour forecasts are initialized every 6 hours using the initial conditions generated with the different horizontal resolutions. Another set of forecasts is initialized with the NCEP operational Global Data Assimilation System (GDAS) analyses.