It is widely recognized that comprehensive data assimilation and distributed hydrologic modeling are two of the most important components in hydrologic prediction for improving predictive skill and producing new information for hydrology and water resources applications. In typical (uncoupled) distributed hydrologic modeling, where the land surface model is driven by the observed and atmospheric model-predicted forcings for the current and future boundary conditions, respectively, Quantitative Precipitation Estimation (QPE) is very often the biggest source of uncertainty in the initial conditions of the model soil moisture states. The National Multisensor QPE system (NMQ) is a collaboration between NOAA/NSSL and NOAA/NWS/OHD to improve QPE and to quantify its uncertainty over the conterminous United States (CONUS). The input to the system includes radar, rain gauge, satellite, operational numerical weather prediction model output, and lightning data. NMQ is envisioned as a community testbed for research and development of next generation multisensor QPE applications and for generation of experimental QPE products over the CONUS. In this presentation, we describe the progress and plans, and seek community feedback particularly in the context of hydrologic and hydrometeorological data assimilation for operational hydrologic prediction. Due to intermittency, scale dependence and space-time variability of precipitation, multisensor QPE offers a challenging data assimilation problem in itself. Science issues and approaches being pursued for implementation on NMQ are also described.