Enhancing hurricane forecasting and alerts through AI-driven analysis of General Aviation data
Creators: name: Pik, Eugene
name: Woolsey, Amy
name: Luciano, Jude
name: Solomonov, Natanel
name: Wright, Maria
name: Gerber-Chavez, Logan
Publisher: name: Zenodo
Year: 2024

description: Enhancing Hurricane Forecasting and Alerts through AI-Driven Analysis of General Aviation Data

(PDF document)

Advancements in weather prediction are being markedly driven by the integration of Artificial Intelligence (AI) and the latest aviation technology, especially the Automatic Dependent Surveillance-Broadcast (ADS-B) version 3, installed in General Aviation (GA) aircraft. Since the approval of ADS-B v3 in 2020, this technology has transformed GA aircraft into airborne weather stations that may collect and transmit real-time atmospheric data such as temperature, humidity, pressure, and wind metrics. Analyzed by AI algorithms, this data enhances the speed and precision of weather forecasts, overcoming the limitations of traditional data collection methods that rely on stationary geographic locations and face data processing delays. The application of ADS-B v3 technology enables the capture of atmospheric data across diverse regions and altitudes, effectively filling critical data coverage gaps. This is particularly beneficial for hurricane prediction, leading to more timely and effective community alerts and responses, thus marking a considerable advance in disaster preparedness and public safety.

The project encompasses comprehensive data collection through GA aircraft equipped with ADS-B version 3 transmitters, facilitating the collection of a wide range of atmospheric data over diverse geographical areas and altitudes. This broadens the scope of weather monitoring beyond fixed weather stations and periodic weather balloon releases, improving the accuracy and reliability of weather forecasts for managing weather-related disasters, including hurricanes. AI-driven data analysis processes this data in near-real-time, enhancing weather prediction models to be more responsive to changes in atmospheric conditions. The proposal also focuses on the integration of these enhanced models with local government alert systems and public notification apps, ensuring that improvements in prediction accuracy translate into timely and effective alerts. This integration faces challenges such as ensuring system compatibility and managing the vast data volumes, but it also offers opportunities to substantially enhance disaster response capabilities. The proposed system aims to streamline the flow of critical weather information, improving the preparedness and response capabilities of the public and emergency management agencies alike.

 

2-minute video - https://www.youtube.com/watch?v=p1GkIVu_zwM



Transcript

This proposal suggests a substantial advancement in hurricane prediction by utilizing inputs from an untapped resource of rich weather data.Our strategy for revolutionizing hurricane prediction focuses on leveraging General Aviation aircraft.

The primary objective is to gather comprehensive atmospheric data by installing weather sensors on GA aircraft.The plan involves equipping the aircraft with ADS-B v3 transmitters capable of transmitting real-time weather conditions to ground-based receivers linked to the internet.

Next, the subsequent critical phase involves processing in near real-time using AI and machine learning techniques.The aim is to swiftly process atmospheric information to identify early indicators of hurricane activity, which consists of developing or adapting AI models tailored for meteorological data analysis.

To enhance the accuracy of our hurricane forecasts, we are integrating additional weather data sources and refining our prediction algorithms.This integration enables us to use a dynamic grid size for our weather map, leading to forecasts with higher resolution.

Additionally, we can adjust the grid sizes based on the density of the weather data sources, which further improves our ability to predict hurricanes.The improved predictive capabilities enables us to provide more timely and accurate alerts to communities in the path of hurricanes.

Establishing efficient alert distribution channels ensures prompt notification of potentially affected areas.The final step involves ensuring broad alert distribution through seamless integration and user-friendly mobile applications.

It entails linking the prediction system with local government alerts and developing a user-friendly mobile application for public use.