What it does

Allows images to be run against pre-trained Azure ML model and weather prediction database to enhance tornado and lightning detection from still images.

Stormie is intended as a prototype for "shallow storm" detection - these are high velocity but underreported meteorological events that are too small to be seen by radar or satellite; or are too short to alert any numerical systems.

The intention of creating Stormie is to provide better models to detect shallow storms and create more microclimate research.

How I built it

Tools used: Vue.js Javascript Azure Firebase

Method: Stormie lets an anonymous user add image data to an interactive climate model. The registered image is added to pre-trained database and sent to the Azure's ML model to calculate the chances that the given image is indeed a shallow storm.

Challenges I ran into

As less-than-experienced web developers, understanding the various technologies and how to get started was difficult. Additionally, as amateurs in JavaScript, integration was slow as we battled syntax errors.

Accomplishments that I'm proud of

- Training an ML model using some data we collected from various sources
- Making the Azure API query 
- Creating a responsive climate model and map that updates
- Transitioning from a Flask backend to Firebase hosting

What I learned

- Flashy documentation != straightforward to use
- Azure is cool!

What's next for Stormie

- Bringing the interface into the 2000s :(
- New ML model trained with more images
- Ability to identify shallow storms from publicly available data automatically (street cameras, news footage)
- Heatmap of shallow storm activity - integrate with existing weather data sources
- Real time updates of storm activity

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