Quadratic Programming Based Data Assimilation with Passive Drifting Sensors for Shallow Water Flows

We present a method for assimilating Lagrangian sensor measurement data into a shallow water equation model. Using our method, the variational data assimilation problem is formulated as a quadratic programming problem with linear constraints. Drifting sensors that gather position and velocity information in the modeled system can then be used to refine the estimate of the initial conditions of the system. A new sensor network hardware platform for gathering flow information is presented. We summarize the results of a field experiment designed to demonstrate the capabilities of our assimilation method with data gathered from the sensors. Validation of the results is performed by comparing them to an estimate derived from an independent set of static sensors.