NCEP's Global Data Assimilation System (GDAS) assimilates both conventional and non-conventional (satellite) raw observations through a sophisticated variational quality control (VARQC) system. This system, as a first step, screens for poor quality observations and saves observations through an iterative process. In the second step, these observations are further scrutinized in the Gridpoint Statistical Interpolation (GSI) 3DVAR assimilation system to generate the final assimilated statistics. This VARQC procedure will enable the GSI system to generate the best possible initial conditions for NCEP's Global Forecast System (GFS) model. Clarity in the representation of the volume of incoming data from each source at the received, selected, and assimilated levels is important when assessments of the value of those sources are considered. The received, selected, and assimilated observational statistics are important to investigating the forecast model skill dropouts, data denial experiments, and the impact of observations in data sparse regions. The goal of this project is to create a web interface that generates statistics of all observational types for use in operational Numerical Weather Prediction (NWP). A perl script with a web graphical interface is developed to access the observational statistics. This graphical interface system will be implemented into NCEP's future operational suite.