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The growing utilization of wind energy resources has been evidenced to have had substantial positive impacts on mitigating climate change consequences. Unlike stationary wind turbines, mobile wind turbines (MWTs) can travel along the local transportation system (TS) via a truck, supplying power to microgrids (MGs). This spatiotemporal flexibility can provide significant benefits, including enhancing system resilience in the aftermath of high-impact low-probability (HILP) events and contributing to energy system decarbonization. In this paper, we propose a two-stage stochastic optimization model for the service restoration problem by coordinating the routing and scheduling of MWTs with hydrogen storage systems (HSSs) in MGs. The first-stage problem focuses on optimizing the deployment of MWTs based on the shortest-path information obtained through Dijkstra's algorithm. The second-stage problem aims to minimize the expected power outage costs while accounting for wind energy uncertainties estimated through a Monte Carlo simulation approach. Case studies on an integrated transportation and energy network – a central Alabama interstate TS and four IEEE 33-node test power systems – demonstrate the effectiveness of the proposed restoration scheme in boosting MGs resilience and reducing carbon emissions. • Design a two-stage model to assign mobile wind turbines in hydrogen-based microgrids. • Use the shortest-path algorithm to enhance efficiency of MWTs' dispatch decision. • Integrate microgrids and a transportation system, enabling use of wind energy. • Discuss insight on striking balance between power outage costs and carbon emissions. [ABSTRACT FROM AUTHOR]