The primary justification for the development of Land Data Assimilation Systems (LDAS), such as NLDAS, GLDAS and HRLDAS has been to provide improved surface state variables (soil moisture, temperature, snow pack) for initializing coupled forecasting systems. While LDAS have been shown to improve the forecasting of surface states and fluxes, the complex nature of the coupling between soil moisture, evaporation and convection makes it difficult to prove that a better initial condition provides a better forecast. Recent studies have examined aspects of land atmosphere coupling including the roles of soil moisture and vegetation, on the structure of the atmospheric boundary layer and initiation and evolution of clouds. However, due to limits in computational resources and/or theoretical knowledge, many of these studies have utilized highly parameterized representations of these components so that the true nature of land atmosphere coupling is still unknown. The NASA/GSFC Land Information System (LIS; http://lis.gsfc.nasa.gov) has now been successfully coupled to the Weather Research and Forecasting (WRF; http://www.wrfmodel.org) model, and now provides a testbed for conducting studies of land-atmosphere coupling at water and energy cycle process resolving horizontal spatial scales (1km or less). In this talk, we will present results from a set of coupled case studies in which we show the impacts of improved input forcing, improved surface parameters, and more complex boundary layer and convection schemes on precipitation forecasts in the 24-48 hour range. The surface states and fluxes, as well as the boundary layer properties and precipitation fields are evaluated primarily in the data-rich Southern Great Plains region, and our results demonstrate that a better soil moisture profile does not necessarily translate into a better precipitation forecast.