With the growing threats of compound flooding due to storm surge, rainfall, and river flow to low-lying coastal regions, simulation of compound flood events using efficient modeling frameworks is essential. This work continues efforts to address this need, using the Discontinuous Galerkin Shallow Water Equation Model (DG-SWEM). DG methods require localized computations for each element, giving rise to local conservation properties and allowing certain capabilities that are infeasible with Continuous Galerkin ADCIRC, including rainfall on the mesh. In this study, we examine the benefit parametric rainfall models may provide to hurricane forecasting. Hurricane Beryl serves well as a case study for compound flooding, as it presented storm surge, intense rainfall, and winds, with 10-15 inches of rain hitting the Houston area. Many mesh elements were only inundated in the model including rainfall, and dry in the original models. The rainfall models diverge most prominently upstream, where surge is not the dominant forcing term, including at the observed high water marks. In many of these upstream areas, a 10 cm increase in water levels due to rainfall is observed, with a maximum increase of 1.7 m. Hence, parametric rainfall seems invaluable for upstream modeling of storms with significant rainfall quantities. With this baseline, we seek to validate and assess the addition of this parametric rainfall model for forecasting of tropical storms. For Beryl, there are 42 distinct advisories containing 3-5 days of official forecast data, which we use in this case instead of the compiled best track wind data. The resulting simulations may then be compared with the best track data, as well as models without rainfall included. For a small computational cost, the parametric rainfall model provides a higher fidelity model for forecasting.