In this study, atmospheric analyses obtained through assimilation of temperature, water vapor, and wind profiles from a potential network of ground-based remote sensing boundary layer profiling instruments were used to generate short-range ensemble forecasts for each assimilation experiment performed in Part I. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). Overall, the results show that the most accurate forecasts were achieved when mass (temperature and humidity profiles from the RAM, MWR, and/or AERI) and momentum (wind profiles from the DWL) observations were assimilated simultaneously, which is consistent with the main conclusion from Part I. For instance, the improved wind and moisture analyses obtained through assimilation of these observations contributed to more accurate forecasts of moisture flux convergence and the intensity and location of accumulated precipitation (ACPC) due to improved dynamical forcing and mesoscale boundary layer thermodynamic structure. An object-based verification tool was also used to assess the skill of the ACPC forecasts. Overall, total interest values for ACPC matched objects, along with traditional forecast skill statistics like the equitable threat score and critical success index, were most improved in the multisensor assimilation cases.
