Hurricanes and tropical storms significantly impact coastal and inland communities. As of 2018, the National Oceanic and Atmospheric Administration (NOAA) reported three of the top five most costly hurricanes made landfall in 2017. In this study, we examine the error fields of hurricane precipitation forecasts and use spatial analysis to quantify uncertainty in those forecasts. The study includes 48 storms of tropical storm strength or greater that made landfall in the contiguous U.S. between 2004 and 2017. For observations, we use the Stage IV dataset (~4-km, hourly resolution), and for the forecast, we use the ~12km North American Mesoscale (NAM) forecast model. Both datasets are available through NOAA/National Centers for Environmental Prediction (NCEP). After interpolation, we study the spatial correlation structure generated by all precipitation within a 700km buffer from the eye of the storm, as well as precipitation constrained above a threshold. Upon examining disparities between forecasted and observed fields, we shall explore the relationship of variogram parameter estimates based upon storm characteristics including, but not limited to, landfall location and intensity.