Ensemble-Based Adaptive Targeting of Mobile Sensor Networks

This work presents an efficient algorithm for an observation targeting problem that is complicated by the combinatorial number of targeting choices. The approach explicitly incorporates an ensemble forecast to ensure that the measurements are chosen based on their expected improvement in the forecast at a separate verification time and location. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement on the uncertainty reduction over this verification site backwards. In particular, the approach determines the impact of a series of fictitious observations taken at the verification site back on the search space (and time), which provides all of the information needed to optimize the set of measurements to take and significantly reduces the number of times that the computationally expensive ensemble updates must be performed. A computation time analysis and numerical performance simulations using the two-dimensional Lorenz-95 chaos model are presented to validate the computational advantage of the proposed algorithm over conventional search strategies.