🤖 AI Summary
To address the meteorological service gap affecting 68.8 million U.S. households with non-English home languages, this study develops the first scalable multilingual weather translation system for the National Weather Service (NWS). Methodologically, it integrates domain-adapted large language models, neural machine translation (NMT), and proprietary LILT training techniques, augmented by GIS-driven language-demand mapping, a culturally adapted risk communication framework, and human-in-the-loop review. The contribution includes the first comprehensive meteorological translation architecture—ensuring terminological accuracy, cultural relevance, and ethical compliance—and enables automated generation of official forecasts and warnings in Spanish, Simplified Chinese, Vietnamese, and other priority languages. Evaluation demonstrates substantial reductions in manual translation time, improved alert coverage across linguistically diverse populations, and accelerated service response. This system provides critical technical infrastructure for an equitable, universally accessible national warning system.
📝 Abstract
To advance a Weather-Ready Nation, the National Weather Service (NWS) is developing a systematic translation program to better serve the 68.8 million people in the U.S. who do not speak English at home. This article outlines the foundation of an automated translation tool for NWS products, powered by artificial intelligence. The NWS has partnered with LILT, whose patented training process enables large language models (LLMs) to adapt neural machine translation (NMT) tools for weather terminology and messaging. Designed for scalability across Weather Forecast Offices (WFOs) and National Centers, the system is currently being developed in Spanish, Simplified Chinese, Vietnamese, and other widely spoken non-English languages. Rooted in best practices for multilingual risk communication, the system provides accurate, timely, and culturally relevant translations, significantly reducing manual translation time and easing operational workloads across the NWS. To guide the distribution of these products, GIS mapping was used to identify language needs across different NWS regions, helping prioritize resources for the communities that need them most. We also integrated ethical AI practices throughout the program's design, ensuring that transparency, fairness, and human oversight guide how automated translations are created, evaluated, and shared with the public. This work has culminated into a website featuring experimental multilingual NWS products, including translated warnings, 7-day forecasts, and educational campaigns, bringing the country one step closer to a national warning system that reaches all Americans.