Abstract
This paper describes the development of the “Collaboration with Startups for Localized Severe Weather Countermeasures: Building a Real-time Disaster Prevention Field using AI” project, which is positioned as part of the BRIDGE program. The core technology used in this BRIDGE project is based on collaborative work between the Meteorological Research Institute and East Japan Railway Company, which involves using deep learning to automatically detect low-level rotational airflows associated with wind gusts in high-resolution radar data. The key objectives of the BRIDGE project are to enhance the existing deep learning models for accurate tornado vortex detection, expand the application scope beyond railway operations to broader sectors by integrating GPS location data, and foster industry- academia-government collaboration, including partnerships with startups, for efficient technology development and practical implementation. The paper outlines the principles of observing tornado vortices using Doppler radar, the construction of deep learning models for detecting tornado vortex patterns, and the processing flow and application examples in train operation control, building upon our previous work presented in Kusunoki et al. (2022). It also provides an overview of the BRIDGE program and the positioning of this BRIDGE project within it, highlighting the industry- academia-government collaboration system and the involvement of startup companies. The initial results of the project are presented, including the development of advanced deep learning models for tornado vortex detection, comparing their performance against the VGG model which we previously developed, and the efforts towards building a real-time disaster prevention information dissemination system integrated with GPS data. The paper concludes by discussing the expected future developments, academic insights, and societal impacts of this research, which aims to strengthen resilience against localized meteorological disasters while contributing to the advancement of tornado research.
