As the bypass ratio of modern turbofan engines continues to increase, the proximity between the wing and the engine in underwing installations tends to aggravate the noise generation due to jet-surface interactions (JSI). To ensure that increasingly strict legislation requirements will be met with minimal aircraft performance penalty, the capacity to predict the JSI noise is crucial. Although advanced numerical techniques such as LES can effectively tackle this problem, they are still computationally expensive to use for optimization or design. Thus, there exists a demand for fast, yet reliable, predictive methods, that can be used to estimate the impacts of the engine relative position. In this work, the reduced order model proposed by Miller is used to provide the nearfield pressure field, which is in turn used as input for the Lyu and Dowling’s model for the prediction of farfield installation noise. The nearfield model parameters are calibrated based on measurements of the nearfield PSD for a round nozzle and RANS simulations. Once the optimal model parameters are calculated, the only input is derived quantities from RANS simulations for the isolated jet, which yields a model for JSI that is fast, robust, and relies on minimal input.