Performance evaluation of the time balance scheduling algorithm for phased-array radar adaptive weather sensing

Phased array radars have the capability of dynamically controlling the beam position on a pulse-by-pulse basis, which allows a single radar to simultaneously perform multiple functions. The phased array radar (PAR) installed at the National Weather Radar Testbed (NWRT) in Norman, Oklahoma is the first phased array system in the nation dedicated to weather radar research and can electronically steer the beam in both azimuth and elevation. The concept of Time Balance (TB) was introduced and demonstrated in the previous work as a means to dynamically schedule multiple tasks of tracking and surveillance for adaptive weather sensing. Two quality measures were theoretically established to quantify the gain of adaptive sensing relative to the conventional Volume Coverage Pattern (VCP) used in the operational Weather Surveillance Radar-1988 Doppler (WSR-88D).

Here, the previous work is put in the context by establishing a complete scheduling framework that can be easily implemented for real-time operation on the NWRT PAR. The framework is modular and consists of four processes: (1) a storm identification algorithm, (2) a storm tracking algorithm, (3) a tasks configuration module, and (4) the TB scheduling algorithm. The storm identification algorithm automatically identifies 3D storm cells during the surveillance task so that updated information about the number, locations, and size of storm cells is provided to the tracking and TB algorithms. In this work, the same framework is used to simulate and evaluate the performance of the TB scheduling algorithm on multiple archived weather data cases. The performance of the TB scheduling algorithm can then be characterized by statistical analyses of the error between the theoretical and estimated quality measures. This is the 1st step towards a real-time implementation of the TB scheduling algorithm on the NWRT PAR, which will enable the demonstration of improvements that can be realized with focused, adaptive weather observations.