mlsanityData to 14 Oct 2026

From Binary to Bilingual: How the National Weather Service is Using Artificial Intelligence to Develop a Comprehensive Translation Program

arXiv 2510.14369 ↗First posted 16 Oct 2025cs.CL cs.AI cs.CYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 51 references that are in the map. First posted on arXiv 16 Oct 2025.

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2 of 51 references.

References · 2

  1. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  2. 2024Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communicationcited by 4

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