Chemical and biological (CB) agent detection and effective use of these observations in hazard assessment models are key elements of our nation's CB defense program that seeks to ensure that Department of Defense operations are minimally affected by a CB attack. Accurate hazard assessments rely heavily on the source term parameters necessary to characterize the release in the transport and dispersion (T&D) simulation. Unfortunately, these source parameters are often not known and based on rudimentary assumptions. In this presentation we use an algorithm that utilizes variational data assimilation techniques to fuse CB and meteorological observations to characterize agent release source parameters and provide a refined hazard assessment. The underlying algorithm consists of a combination of modeling systems, including the Second-order Closure Integrated PUFF model (SCIPUFF), its corresponding Source Term Estimation (STE) model, a hybrid Lagrangian-Eulerian Plume Model, its formal adjoint, and the software infrastructure necessary to link them. While numerous approaches to this problem exist, each with their own strengths and weaknesses, this approach attempts to addresses this problem in an operational environment where computational resources are limited and a timely solution is critical. Version 1.0 of the algorithm was recently delivered to the Defense Threat Reduction Agency - Joint Science and Technology Office for implementation into the Joint Effects Model. An early demonstration of this capability in an operational tool, in this case the Hazard Prediction & Assessment Capability (HPAC), illustrates that it adds an important capability to the tool set available to the emergency management and first responder community. We incorporate new methods to address issues related to detection limitations in the sensors and a time-varying wind speed and direction into the variational minimization step to address inconsistencies between the concentration observations and the wind observations. This allows us to find the optimal solution of the source parameters and meteorological conditions and minimize the impact of these errors in both current and the downwind hazard solutions.