{"id":4459,"date":"2026-10-15T10:00:08","date_gmt":"2026-10-15T01:00:08","guid":{"rendered":"https:\/\/www.mti.co.jp\/eng\/?p=4459"},"modified":"2026-10-15T10:00:08","modified_gmt":"2026-10-15T01:00:08","slug":"in-pursuit-of-reliable-avoidance-of-adverse-weather-conditions-zipair-mti-and-jaxa-will-begin-collaboration-on-3d-arvi-3","status":"publish","type":"post","link":"https:\/\/www.mti.co.jp\/eng\/?p=4459","title":{"rendered":"MTI’s Aviation Weather Service “3DARVI” integrates BlueWX’s AI Turbulence Prediction for operational deployment on ZIPAIR in October"},"content":{"rendered":"

ZIPAIR Tokyo Inc. (hereinafter “ZIPAIR”), BlueWX Co., Ltd. (hereinafter “BlueWX”), and MTI Ltd. (hereinafter “MTI”) announced that BlueWX’s AI Turbulence Prediction is now integrated into MTI’s aviation weather service “3DARVI,” beginning operation on ZIPAIR flights on Thursday, October 8.<\/p>\n

This integration allows ZIPAIR pilots to access AI-based turbulence predictions in flight via “3DARVI.” Furthermore, by combining this with existing weather and aviation data on “3DARVI,” the solution creates an environment where pilots can view all essential operational information in one view, supporting informed flight decisions with turbulence risk taken into account.<\/p>\n

Leveraging upon their respective expertise, the three companies will combine AI turbulence predictions with existing meteorological data to help flight operations better understand risks and refine flight planning. Going forward, they aim to further enhance flight safety, passenger comfort, and operational efficiency by applying insights gained from actual flight operations.<\/p>\n

 <\/p>\n

\u25c6<\/strong> Integrating AI Turbulence Prediction into “3DARVI” for timely risk assessment in the cockpit<\/strong><\/p>\n

Turbulence occurs when air moves unpredictably due to changes in wind speed and direction. Because it is influenced by complex factors like weather\"\"<\/a> patterns and terrain, predicting its exact location and intensity in advance is challenging. Early identification of turbulence risks is essential for airlines to optimize flight paths and altitudes, ensuring passenger and crew safety while delivering a smooth flight experience.<\/p>\n

BlueWX’s AI Turbulence Prediction uses deep learning techniques to analyze data from numerical weather forecasts, observation stations, and in-flight aircraft measurements to predict turbulence risks. Over four years, thousands of airline pilots and flight dispatchers tested the system in trials, providing feedback that helped BlueWX improve the system\u2019s accuracy and reliability.<\/p>\n

ZIPAIR leverages Starlink high-speed internet to enhance in-flight access to weather information and flight-support applications in the cockpit. As part of this effort, ZIPAIR is introducing BlueWX’s AI Turbulence Prediction through MTI’s “3DARVI” service in operation.<\/p>\n

With this update, ZIPAIR pilots can check AI turbulence predictions alongside existing weather and flight data from the cockpit, giving them timely insights to guide better flight decisions.<\/p>\n

 <\/p>\n

\u25c6<\/strong>Enhancing Flight Decision-Making and Safety through Collaboration<\/strong><\/p>\n

Through this initiative, ZIPAIR will apply these features to daily flight operations to make smarter, risk-aware decisions. BlueWX will incorporate operational feedback to further refine its turbulence predictions, while also developing new forecasting products for weather phenomena beyond turbulence. MTI will address user feedback from flight operations to enhance data and usability in “3DARVI,” while working to expand its customer base among airlines.
\nTogether, the three companies will continue to bridge AI weather prediction and live flight operations to advance operational decision-making and ensure safe flight operations.<\/p>\n

 <\/p>\n

\uff1c<\/strong>About BlueWX’s AI Turbulence Prediction<\/strong>\uff1e<\/strong><\/p>\n

It leverages deep learning to analyze complex weather data and accumulated turbulence records, enabling future turbulence risk prediction. Unlike conventional methods based on standard turbulence metrics, this system achieves high accuracy by analyzing vast meteorological datasets alongside several years of global turbulence occurrence data. Extensive testing by pilots and dispatchers has proven its reliability and practical value. For details, please visit: https:\/\/www.bluewx.co.jp\/en<\/a><\/p>\n

 <\/p>\n

\uff1c<\/strong>About MTI’s Aviation Weather System “3DARVI”<\/strong>\uff1e\"\"<\/a><\/strong><\/p>\n

3DARVI is an aviation weather system which allows users to check meteorological information that affects aircraft operations and flight plans on a single screen.
\nThe system allows users to check real-time weather phenomena and flight routes at a glance, helping flight dispatchers select routes with good weather conditions for safe flight plans. And, by maintaining smooth operation even when displaying multiple meteorological information sources that involve large amounts of data, ARVI improves the efficiency of operations and allows flight dispatchers and pilots to quickly check for dangerous weather phenomena along their route.
\n3DARVI also visualizes meteorological phenomena in 3D, enabling users to quickly and intuitively grasp weather conditions and supporting safe aircraft operations. For details, please visit: https:\/\/www.aviavi-arvi.com\/en<\/a><\/p>\n

 <\/p>\n

[Media Contacts]<\/p>\n

ZIPAIR Tokyo Inc.
\nBlueWX Co., Ltd. – Public Relations (Email: contact@bluewx.co.jp<\/a>)
\nMTI Ltd. – Public Relations Department (Email: mtipr@mti.co.jp<\/a>)<\/p>\n

 <\/p>\n

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ZIPAIR Tokyo Inc. (hereinafter “ZIPAIR”), BlueWX Co., Ltd. (hereinafter “BlueWX”), and […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[5,493],"tags":[500],"class_list":["post-4459","post","type-post","status-publish","format-standard","hentry","category-news","category-mti","tag-3darvi"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t