Introduction

Hey! I'm a Anoushka a new Engineer working on Loaf! As both a frequent "accidently stepped out in the rain" victim and a former Civil/Environmental Engineer, the value of better local weather predictions feels very clear and I'm excited to be dusting off some ML skills for this project!

The Research

My initial thought on the research was how refreshingly accessible it was. The intent, reasoning, and procedure were clearly laid out and I was able to grasp the base logic within a read. I was suprised that they were able to get such an improvement in RMSE from just a transformer model, signaling the value in this work. I'm curious to how this research could apply to regions with more stable weather conditions than the Northeast (I'm from LA the land of constant perfect weather).

I also looked into what other literature was other on ML models localizing weather and found ClimaX by Microsoft and some more neural based models. I'd love to avoid a black box model and maintain understandibility as much as possible, but also am curious how other models perform.

Next Steps

After diving into both the research and the code, I want to: - Recreate modeling with a local dataset - I'm based in the DMV and am from LA and am curious how the model handels both area - Finish full training cycle - close some gaps in the code regarding training/model assesment - Try other model types to drive accuracy - based on other literature see how other models compare for same dataset - Make codebase more user-friendly - the goal is to eventually make the model self-service and I have a few thoughts on how to make the UX as friendly as possible