Adaptive Task Scheduling Approaches for Weather Radar Networks
J. Srinivasan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
F. Fioranelli – Graduation committee member (Microwave Sensing, Signals & Systems)
A. Pappas – Mentor (Microwave Sensing, Signals & Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Weather-radar networks must allocate limited sensing time between repeated observations of evolving storms and background surveillance. Fixed scanning strategies provide systematic coverage, but cannot adapt the use of radar resources when several storm observations compete for service. This thesis investigates adaptive task scheduling for a multi-radar weather sensing network through two proposed approaches: Radar-Level Task Selection (RLTS) and Load-Aware Task Scheduling (LATS).
RLTS combines Time-Balance revisit urgency with dynamic storm priority, task feasibility, and radar-dependent information to select the next observation. LATS extends this approach with explicit storm-to-radar assignment based on scan time, traverse time, urgency, and radar workload. The methods are evaluated against cyclic storm-by-storm tracking and continuous 360◦ scanning using 50 Monte Carlo trials of 20 min across five storm scenarios, simulated in a developed framework.
The results show that adaptive scheduling becomes increasingly beneficial as radar resources become constrained. LATS achieves the shortest median revisit time in four of the five scenarios, while the cyclic baseline remains strongest under low load. Under high load, LATS reduces the median revisit time by 12.9% relative to RLTS while increasing mean surveillance coverage from 3.79% to 13.13%. Tracking accuracy remains broadly comparable between the two adaptive schedulers. The results demonstrate that adaptive task selection improves storm servicing when observations compete for radar time, while load-aware assignment provides an additional benefit by distributing the workload more effectively across the radar network and preserving greater surveillance capacity