Forecasts from nowcasting models are increasingly becoming a crucial input to the rainfall-runoff models. A basic approach to the nowcast generation is based on extrapolation (advection) of current precipitation field. The main limitation of such nowcasting is the rapid decrease in accuracy with forecasting lead time, due to dynamical evolution of precipitation, especially when convection appears, therefore recent studies are focused on taking into account also the evolution of precipitation. According to subject literature, the conceptual cell lifecycle models are not sufficient to significantly increase forecast accuracy, thus at present new approaches based on autoregressive models are investigated. This paper presents the SNAR (Spectral Nowcasting with Autoregression) nowcasting model developed at IMGW-PIB. The aim of the present research is to improve the nowcasting reliability, and to extend the lead time. The model proposes two innovative solutions: (I) decomposition of precipitation field to layers associated with their spatial scale, (II) forecasting based on autoregressive model. The paper gives an overview of algorithms used in the SNAR model and provides preliminary results.