Strategic Deployment of a Single Mobile Weather Radar for the Enhancement of Meteorological Observation: A Coverage-Based Location Problem

Abstract

Mobile weather radars have been routinely deployed to acquire high-quality meteorological data for research purposes, particularly for monitoring rapidly evolving weather phenomena at low altitudes. However, identifying an optimal location for mobile weather radar deployment is a complex challenge, as it requires consideration of operational safety, data quality, and environmental constraints. In this study, we introduce a framework using a coverage-based location problem to solve the strategic deployment of a single mobile weather radar. This approach aims to enhance weather observation while accounting for the deployment space’s safety constraints and geospatial characteristics. The proposed location problem is solved optimally using the geometric branch-and-bound algorithm and heuristically using swarm-based optimization algorithms. The implementation relies entirely on open-source Python packages, allowing the work to be verified, replicated, and expanded upon by the broader scientific community. Results demonstrate that exact solution methods are ideal when ample time is available for decision-making and optimal deployment locations are desired. In contrast, heuristic algorithms can efficiently identify multiple near-optimal deployment locations, making them highly suitable for rapid decision-making and evaluating alternative deployment options. Moreover, the findings highlight the potential of quantitative decision-making techniques in improving the effectiveness of mobile radar positioning, thereby contributing to efficient weather observation, forecasting, and better-informed emergency response strategies.