Spaceborne precipitation radars, such as the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) Core Observatory, have been important platforms to provide a direct measurement of three-dimensional precipitation structure globally. Building upon the success of TRMM and GPR Core Observatory, the Japan Aerospace Exploration Agency (JAXA) is currently surveying the feasibility of a potential satellite mission equipped with a precipitation radar on a geostationary orbit (GPR). The use of both ground-based and spaceborne precipitation radar observations in numerical weather prediction has long been explored. Some studies showed promising results about data assimilation of radar reflectivity for convective-scale and tropical cyclone analyses. Nevertheless, it is still a challenge to build a general approach to data assimilation of radar reflectivity due to various factors such as the non-diagonal observation error covariance matrix, complex observation operator, and strong nonlinearity and model errors in the moist physical processes. In this study, we aim to develop a method to effectively assimilate radar reflectivity data for the GPR. We perform an observing system simulation experiment with the SCALE-LETKF system (Lien et al., 2017) and a satellite simulator known as the Joint-Simulator (Hashino et al., 2013). As the first step, we focus on a case of Typhoon Soudelor (2015), the strongest typhoon in the Western North Pacific in 2015. In the presentation, we will show what kind of observation we can get with the GPR and the impact of GPR observations on the analyses and forecasts of a simulated case of Typhoon Soudelor.