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This paper presents an efficient targeting algorithm to coordinate a team of mobile sensor platforms in order to extract information from the natural environment for the purpose of improved forecasting. This coordinated targeting is complicated by the large dimensionality of the natural dynamic systems (and thus of the decision space), as well as by the constraints in the vehicle motions. While the backward formulation developed by the present authors provides a baseline framework to efficiently address the dimensionality challenge in an unconstrained setting, the key contributions of this paper are twofold: (a) to delineate how to effectively incorporate the sensor platform constrained mobility in the targeting process and (b) to demonstrate the importance of the interteam information sharing to achieve good targeting performance. Numerical examples of simplified weather forecasting verify that the presented method renders good targeting solutions while retaining computational tractability, which is crucial for the design of sensor networks that tightly interact with, and rapidly adapt to, large-scale dynamic environments.